System

Abstract

An object of the system according to the embodiment is to fairly and efficiently share the burden of housework and childcare and to reduce the dissatisfaction between the husband and wife.SOLUTION: A system according to an embodiment includes an task management unit, a communication unit, and a childcare support unit. The task management component manages and optimizes housekeeping and child care tasks. The communication unit realizes smooth communication between husband and wife and between family members as a whole. The child care support unit supports growth and health 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 technology makes it difficult for dual-income couples to share the burden of housework and childcare fairly and efficiently, which can lead to dissatisfaction between the couple.

[0005] The system according to the embodiment aims to share the burden of housework and childcare fairly and efficiently, thereby reducing dissatisfaction between spouses. [Means for solving the problem]

[0006] The system according to the embodiment includes a task management unit, a communication unit, and a child-rearing support unit. The task management unit manages and optimizes housework and child-rearing tasks. The communication unit facilitates smooth communication between spouses and the entire family. The child-rearing support unit supports the growth and health of children. [Effects of the Invention]

[0007] The system according to the embodiment can share the burden of housework and childcare fairly and efficiently, thereby reducing dissatisfaction between spouses. [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) The housework and childcare sharing platform according to an embodiment of the present invention is a system that divides the burden of housework and childcare fairly and efficiently, reducing dissatisfaction between couples, thereby enabling dual-income couples to save time and energy and lead a less stressful family life.

[0029] A housework and childcare sharing platform according to an embodiment includes a task management unit, a communication unit, and a childcare support unit. The task management unit manages and optimizes housework and childcare tasks. For example, the task management unit uses a generation AI to automatically schedule tasks such as cleaning, laundry, cooking, and taking children to and from school, ensuring fair division between spouses. The task management unit also uses a generation AI to reevaluate task priorities in real time, enabling the system to respond to sudden schedule changes. For example, the generation AI monitors a user's calendar and schedule in real time and automatically reevaluates task priorities in the event of a sudden schedule change. The task management unit also uses a generation AI to monitor the progress of each task and automatically reschedule tasks if a delay occurs. For example, the generation AI monitors the progress of housework and childcare tasks in real time and automatically reschedules tasks if a delay occurs. The communication unit facilitates smooth communication between spouses and the entire family. For example, the communication unit facilitates information sharing and consultations between family members through chat and video call functions. In addition, the communication unit uses the generation AI to analyze communication history between family members, learn from past troubles and complaints, and propose ways to improve communication in the future. For example, the generation AI analyzes communication history between family members, learns from past troubles and complaints, and proposes ways to improve communication in the future. The child-rearing support unit supports children's growth and health. For example, the child-rearing support unit uses the generation AI to analyze children's health data and growth records and provide appropriate advice. For example, the generation AI monitors children's health data in real time and immediately issues an alert if an abnormality is detected. As a result, the housework and child-rearing sharing platform according to the embodiment can share the burden of housework and child-rearing fairly and efficiently, reducing dissatisfaction between couples.

[0030] The task management unit can use the generation AI to reevaluate task priorities in real time, allowing it to respond to sudden schedule changes. For example, the generation AI in the task management unit monitors the user's calendar and schedule in real time and automatically reevaluates task priorities when sudden schedule changes occur. For example, if a meeting time is changed, the generation AI will rearrange housework and childcare tasks taking into account the impact. The task management unit also learns the user's behavioral patterns and suggests optimal task rearrangements when sudden schedule changes occur. For example, if a child's school event suddenly comes up, the housework scheduled for that time can be moved to a different time. The task management unit also flexibly responds to sudden schedule changes by using the generation AI to reevaluate the priorities of multiple tasks in real time. For example, it takes into account the schedules of all family members and rearranges tasks in the most efficient way. This allows it to flexibly respond to sudden schedule changes.

[0031] The task management unit uses the generation AI to monitor the progress of tasks and automatically reschedule them if a delay occurs. For example, the generation AI monitors the progress of housework and childcare tasks in real time and automatically reschedules them if a delay occurs. For example, if laundry is behind schedule, the subsequent tasks will be adjusted. The task management unit also analyzes task progress data and suggests optimal rescheduling when a delay occurs. For example, if cooking preparation is behind schedule, the schedule will be changed so that other housework is done first. The task management unit also builds a system in which the generation AI continuously monitors the progress of tasks and automatically reschedules them if a delay occurs. For example, if a child's homework is taking longer than expected, the subsequent tasks will be adjusted. This allows the system to grasp the progress of tasks in real time and respond quickly if a delay occurs.

[0032] The task management unit can manage not only housework and childcare tasks, but also other household activities such as pet care and gardening. For example, the generation AI manages not only housework and childcare tasks, but also other household activities such as pet care and gardening. For example, it automatically incorporates schedules for feeding and walking pets. The task management unit also centrally manages all household activities and efficiently schedules tasks such as housework, childcare, pet care, and gardening. For example, it adjusts gardening time to fit the housework and childcare schedule. The task management unit also manages tasks comprehensively, including other household activities, and balances them with housework and childcare tasks. For example, it optimizes pet care and gardening time to fit the housework and childcare schedule. This centrally manages all household activities and achieves efficient schedule management.

[0033] The task management unit can work with local community services and utilize external resources as needed. For example, the generation AI can work with local babysitter services or cleaning services and utilize external resources as needed. For example, it can automatically arrange for a babysitter service if there is a sudden change in plans. The task management unit can also work with local community services and utilize external resources to reduce the burden of housework and childcare. For example, it can make suggestions to reduce the burden of housework by using a cleaning service. The task management unit can also build a system in which the generation AI can work with local service providers and utilize external resources as needed. For example, it can provide a function to automatically arrange for a babysitter service or cleaning service. This allows the user to utilize local community services and reduce the burden of housework and childcare.

[0034] The communication unit uses the generation AI to analyze the communication history between family members, learn about past problems and complaints, and propose improvements to future communication. For example, the communication unit uses the generation AI to analyze the communication history between family members, learn about past problems and complaints, and propose improvements to future communication. For example, it provides specific advice to avoid past problems. The communication unit also analyzes the communication history between family members, learns about past complaints, and builds a system that proposes improvements to future communication. For example, it makes specific suggestions to resolve past complaints. The communication unit also uses the generation AI to analyze the communication history between family members, learn about past problems and complaints, and automatically proposes improvements to future communication. For example, it provides specific advice to avoid past problems. In this way, it learns about past problems and complaints and aims to improve future communication.

[0035] The communication unit can use the generation AI to automatically adjust family schedules and send reminders to prevent important events and appointments from being forgotten. For example, the communication unit uses the generation AI to automatically adjust the schedules of all family members and send reminders to prevent them from forgetting important events and appointments. For example, it integrates the calendars of all family members and notifies them of important appointments. The communication unit also analyzes family schedules and builds a system that sends reminders to prevent them from forgetting important events and appointments. For example, it automatically notifies them of important events such as birthdays and anniversaries. The communication unit also provides a function that uses the generation AI to automatically adjust the schedules of all family members and send reminders to prevent them from forgetting important events and appointments. For example, it integrates the calendars of all family members and notifies them of important appointments. This allows for efficient management of family schedules and prevents them from forgetting important events and appointments.

[0036] The communication unit can use the generative AI to suggest activities based on common hobbies and interests to support family communication. For example, the generative AI analyzes the hobbies and interests of all family members and suggests activities based on those common hobbies and interests. For example, it suggests outdoor activities that the whole family can enjoy. The communication unit also builds a system that analyzes the hobbies and interests of all family members and suggests activities based on those common hobbies and interests. For example, it suggests movie nights and sporting events that the whole family can enjoy. The communication unit also builds a system that analyzes the hobbies and interests of all family members and automatically suggests activities based on those common hobbies and interests. For example, it suggests outdoor activities that the whole family can enjoy. This suggests activities that the whole family can enjoy and promotes communication.

[0037] The communication unit can use the generative AI to provide interactive content such as games and quizzes to promote family communication. For example, the generative AI provides interactive content such as games and quizzes that the whole family can enjoy, promoting communication. For example, the communication unit suggests an online quiz that the whole family can participate in. The communication unit also builds a system that provides interactive content that the whole family can enjoy and promotes communication. For example, the communication unit suggests online games that the whole family can participate in. The communication unit also builds a system that automatically provides interactive content such as games and quizzes that the whole family can enjoy, promoting communication. For example, the communication unit suggests an online quiz ... This provides interactive content that the whole family can enjoy and promotes communication.

[0038] The child-rearing support department can use the generation AI to analyze children's learning data and propose the optimal learning plan for each individual child. For example, the generation AI analyzes children's learning data and proposes the optimal learning plan for each individual child. For example, a customized learning plan is provided taking into account the child's strengths and weaknesses. The child-rearing support department also builds a system that analyzes children's learning data and proposes the optimal learning plan for each individual child. For example, the optimal learning plan is provided based on the child's learning progress and grades. The child-rearing support department also builds a system that analyzes children's learning data and proposes the optimal learning plan for each individual child. For example, a customized learning plan is provided taking into account the child's strengths and weaknesses. This provides the optimal learning plan for each individual child and improves learning effectiveness.

[0039] The child-rearing support department can use the generation AI to monitor children's health data in real time and immediately issue an alert if an abnormality is detected. For example, the generation AI in the child-rearing support department monitors children's health data in real time and immediately issue an alert if an abnormality is detected. For example, an alert is sent when an abnormality in body temperature or heart rate is detected. The child-rearing support department also builds a system that monitors children's health data in real time and immediately issue an alert if an abnormality is detected. For example, an alert is sent when an abnormality in body temperature or heart rate is detected. The child-rearing support department can also monitor children's health data in real time and automatically issue an alert if an abnormality is detected. For example, an alert is sent when an abnormality in body temperature or heart rate is detected. This allows children's health to be monitored in real time and allows for quick response when an abnormality occurs.

[0040] The child-rearing support unit can use the generation AI to automatically organize a child's growth record and create a digital album that can be shared by the entire family. For example, the generation AI automatically organizes a child's growth record and creates a digital album that can be shared by the entire family. For example, it organizes photos and videos in chronological order and compiles them into a digital album. The child-rearing support unit also builds a system that ... This organizes a child's growth record and creates a digital album that can be shared by the entire family.

[0041] The child-rearing support department can use the generation AI to collaborate with local child-rearing support services and provide information as needed. For example, the generation AI collaborates with local child-rearing support services and provides information as needed. For example, it automatically provides information on child-rearing consultations and parent-child events. The child-rearing support department also builds a system that ... generation AI collaborates with local child-rearing support services and provides information as needed. For example, it automatically provides information on child-rearing consultations and parent-child events. This collaborates with local child-rearing support services and provides necessary information.

[0042] The task management unit not only uses generative AI to efficiently manage housework and childcare tasks, but also optimizes the schedules of all family members, reducing wasted time. For example, the task management unit uses generative AI to efficiently manage housework and childcare tasks and optimize the schedules of all family members, reducing wasted time. For example, it integrates the schedules of all family members and suggests the optimal division of tasks. The task management unit also builds a system that efficiently manages housework and childcare tasks and optimizes the schedules of all family members. For example, it integrates the schedules of all family members and suggests the optimal division of tasks. The task management unit also uses generative AI to efficiently manage housework and childcare tasks and optimize the schedules of all family members, reducing wasted time. For example, it integrates the schedules of all family members and suggests the optimal division of tasks. In this way, the task management unit efficiently manages housework and childcare tasks and optimizes the schedules of all family members, reducing wasted time.

[0043] The task management unit uses the generation AI to monitor the energy levels of all family members and adjust the task burden so that fatigue does not accumulate. For example, the generation AI monitors the energy levels of all family members in real time and adjusts the task burden so that fatigue does not accumulate. For example, when fatigue increases, it suggests tasks that will help you relax. The task management unit also builds a system that monitors the energy levels of all family members and adjusts the task burden so that fatigue does not accumulate. For example, when fatigue increases, it suggests tasks that will help you relax. The task management unit also builds a system that monitors the energy levels of all family members using the generation AI to monitor the energy levels of all family members in real time and automatically adjusts the task burden so that fatigue does not accumulate. For example, when fatigue increases, it suggests tasks that will help you relax. In this way, the energy levels of all family members are monitored and the task burden is adjusted so that fatigue does not accumulate.

[0044] The task management unit can use the generation AI to optimize energy consumption within the home and make suggestions to reduce electricity and water usage. For example, the generation AI in the task management unit monitors energy consumption within the home in real time and makes suggestions to reduce electricity and water usage. For example, it may suggest the use of energy-efficient home appliances. The task management unit also builds a system that optimizes energy consumption within the home ... generation AI in the task management unit monitors energy consumption within the home in real time and automatically makes suggestions to reduce electricity and water usage. For example, it may suggest the use of energy-efficient home appliances. This optimizes energy consumption within the home and reduces electricity and water usage.

[0045] The task management unit can use a generation AI to manage household items and automatically generate a shopping list of necessary items. For example, the generation AI in the task management unit manages household items and automatically generates a shopping list of necessary items. For example, it monitors the contents of the refrigerator and lists the necessary ingredients. The task management unit also builds a system that manages household items and automatically generates a shopping list of necessary items. For example, it monitors the contents of the refrigerator and lists the necessary ingredients. The task management unit also builds a system that manages household items and automatically generates a shopping list of necessary items. For example, it monitors the contents of the refrigerator and lists the necessary ingredients. The task management unit also builds a system that manages household items and automatically generates a shopping list of necessary items. For example, it monitors the contents of the refrigerator and lists the necessary ingredients. This makes household item management more efficient and automatically generates a shopping list of necessary items.

[0046] The child-rearing support department can use the generating AI to periodically evaluate the happiness levels of all family members and propose specific action plans to improve happiness levels. For example, the generating AI in the child-rearing support department periodically evaluates the happiness levels of all family members and proposes specific action plans to improve happiness levels. For example, it proposes activities that the whole family can enjoy. The child-rearing support department also builds a system that ... In this way, the child-rearing support department periodically evaluates the happiness levels of all family members and proposes specific action plans to improve happiness levels.

[0047] The child-rearing support unit can use the generative AI to automatically manage family life events and plan special events. For example, the generative AI in the child-rearing support unit automatically manages family life events and plans special events. For example, birthdays and anniversaries are automatically incorporated into the schedule and preparations for the events are suggested. The child-rearing support unit also builds a system that automatically manages family life events and plans special events. For example, birthdays and anniversaries are automatically incorporated into the schedule and preparations for the events are suggested. The child-rearing support unit also builds a system that automatically manages family life events and plans special events. For example, birthdays and anniversaries are automatically incorporated into the schedule and preparations for the events are suggested. In this way, family life events are automatically managed and special events are planned.

[0048] The child-rearing support department can use the generating AI to monitor the health status of all family members and provide specific advice to maintain their health. For example, the generating AI monitors the health status of all family members in real time and provides specific advice to maintain their health. For example, it provides advice on diet and exercise. The child-rearing support department also builds a system that monitors the health status of all family members and provides specific advice to maintain their health. For example, it provides advice on diet and exercise. The child-rearing support department also builds a system that monitors the health status of all family members in real time and automatically provides specific advice to maintain their health. For example, it provides advice on diet and exercise. This monitors the health status of all family members and provides specific advice to maintain their health.

[0049] The child-rearing support department can use the generation AI to understand the hobbies and interests of all family members and suggest common activities. For example, the generation AI in the child-rearing support department can understand the hobbies and interests of all family members and suggest common activities. For example, it can suggest outdoor activities that the whole family can enjoy. The child-rearing support department can also build a system that understands ... use the generation AI to understand the hobbies and interests of all family members and automatically suggest common activities. For example, it can suggest outdoor activities that the whole family can enjoy. This allows the child-rearing support department to understand the hobbies and interests of all family members and suggest common activities.

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

[0051] The task management unit can manage not only housework and childcare tasks, but also other household activities such as pet care and gardening. For example, the generation AI automatically incorporates schedules for feeding and walking pets in addition to housework and childcare tasks. The generation AI also centrally manages all household activities and efficiently schedules tasks such as housework, childcare, pet care, and gardening. Furthermore, the generation AI performs comprehensive task management, including other household activities, and balances them with housework and childcare tasks. This allows for centralized management of all household activities and efficient schedule management.

[0052] The task management unit can link with local community services and utilize external resources as needed. For example, the generation AI can link with local babysitter services and cleaning services to automatically arrange babysitter services if there is a sudden change in plans. The generation AI can also link with local community services to utilize external resources to reduce the burden of housework and childcare. Furthermore, the generation AI can link with local service providers to provide a function to automatically arrange babysitter services and cleaning services. This allows users to utilize local community services to reduce the burden of housework and childcare.

[0053] The task management unit can use the generative AI to optimize energy consumption within the home and make suggestions for reducing electricity and water usage. For example, the generative AI can monitor energy consumption within the home in real time and suggest the use of energy-efficient home appliances. It can also build a system that optimizes energy consumption within the home and reduces electricity and water usage. Furthermore, the generative AI can monitor energy consumption within the home in real time and automatically suggest the use of energy-efficient home appliances. This makes it possible to optimize energy consumption within the home and reduce electricity and water usage.

[0054] The communication department can use generative AI to automatically adjust family schedules and send reminders to prevent important events and appointments from being forgotten. For example, generative AI can automatically adjust the schedules of all family members, integrate everyone's calendars, and notify them of important appointments. It can also build a system that analyzes family schedules and automatically notifies them of important events such as birthdays and anniversaries. Furthermore, it provides a function where generative AI can automatically adjust the schedules of all family members, integrate everyone's calendars, and notify them of important appointments. This allows for efficient management of family schedules and prevents important events and appointments from being forgotten.

[0055] The communication department can use generative AI to suggest activities based on shared hobbies and interests to support family communication. For example, generative AI can analyze the hobbies and interests of each family member and suggest outdoor activities that the whole family can enjoy. We can also build a system that analyzes the hobbies and interests of each family member and suggests movie nights and sporting events that the whole family can enjoy. Furthermore, generative AI can analyze the hobbies and interests of each family member and automatically suggest outdoor activities that the whole family can enjoy. This can suggest activities that the whole family can enjoy and promote communication.

[0056] The child-rearing support department can use generative AI to analyze children's learning data and propose the optimal learning plan for each individual child. For example, generative AI can provide a customized learning plan taking into account a child's strengths and weaknesses. In addition, a system can be built that analyzes children's learning data and provides the optimal learning plan based on the child's learning progress and grades. Furthermore, generative AI can automatically provide a customized learning plan taking into account a child's strengths and weaknesses. This makes it possible to provide the optimal learning plan for each individual child and improve learning effectiveness.

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

[0058] Step 1: The task management unit manages and optimizes household and childcare tasks. For example, it uses generation AI to automatically schedule tasks such as cleaning, laundry, cooking, and taking children to and from school, ensuring a fair division of the work between spouses. It also uses generation AI to reevaluate task priorities in real time, allowing it to respond to sudden schedule changes. It also uses generation AI to monitor the progress of each task and automatically reschedules it if a delay occurs. Step 2: The communication section enables smooth communication between spouses and the entire family. For example, chat and video call functions make it easy for family members to share information and discuss matters. Generative AI is also used to analyze communication history between family members, learning from past problems and complaints and suggesting ways to improve communication in the future. Step 3: The childcare support department supports children's growth and health. For example, it uses AI generation to analyze children's health data and growth records and provide appropriate advice. AI generation monitors children's health data in real time and immediately issues an alert if an abnormality is detected.

[0059] (Example 2) The housework and childcare sharing platform according to an embodiment of the present invention is a system that divides the burden of housework and childcare fairly and efficiently, reducing dissatisfaction between couples, thereby enabling dual-income couples to save time and energy and lead a less stressful family life.

[0060] A housework and childcare sharing platform according to an embodiment includes a task management unit, a communication unit, and a childcare support unit. The task management unit manages and optimizes housework and childcare tasks. For example, the task management unit uses a generation AI to automatically schedule tasks such as cleaning, laundry, cooking, and taking children to and from school, ensuring fair division between spouses. The task management unit also uses a generation AI to reevaluate task priorities in real time, enabling the system to respond to sudden schedule changes. For example, the generation AI monitors a user's calendar and schedule in real time and automatically reevaluates task priorities in the event of a sudden schedule change. The task management unit also uses a generation AI to monitor the progress of each task and automatically reschedule tasks if a delay occurs. For example, the generation AI monitors the progress of housework and childcare tasks in real time and automatically reschedules tasks if a delay occurs. The communication unit facilitates smooth communication between spouses and the entire family. For example, the communication unit facilitates information sharing and consultations between family members through chat and video call functions. In addition, the communication unit uses the generation AI to analyze communication history between family members, learn from past troubles and complaints, and propose ways to improve communication in the future. For example, the generation AI analyzes communication history between family members, learns from past troubles and complaints, and proposes ways to improve communication in the future. The child-rearing support unit supports children's growth and health. For example, the child-rearing support unit uses the generation AI to analyze children's health data and growth records and provide appropriate advice. For example, the generation AI monitors children's health data in real time and immediately issues an alert if an abnormality is detected. As a result, the housework and child-rearing sharing platform according to the embodiment can share the burden of housework and child-rearing fairly and efficiently, reducing dissatisfaction between couples.

[0061] The task management unit can use the generation AI to reevaluate task priorities in real time, allowing it to respond to sudden schedule changes. For example, the generation AI in the task management unit monitors the user's calendar and schedule in real time and automatically reevaluates task priorities when sudden schedule changes occur. For example, if a meeting time is changed, the generation AI will rearrange housework and childcare tasks taking into account the impact. The task management unit also learns the user's behavioral patterns and suggests optimal task rearrangements when sudden schedule changes occur. For example, if a child's school event suddenly comes up, the housework scheduled for that time can be moved to a different time. The task management unit also flexibly responds to sudden schedule changes by using the generation AI to reevaluate the priorities of multiple tasks in real time. For example, it takes into account the schedules of all family members and rearranges tasks in the most efficient way. This allows it to flexibly respond to sudden schedule changes.

[0062] The task management unit uses the generation AI to monitor the progress of tasks and automatically reschedule them if a delay occurs. For example, the generation AI monitors the progress of housework and childcare tasks in real time and automatically reschedules them if a delay occurs. For example, if laundry is behind schedule, the subsequent tasks will be adjusted. The task management unit also analyzes task progress data and suggests optimal rescheduling when a delay occurs. For example, if cooking preparation is behind schedule, the schedule will be changed so that other housework is done first. The task management unit also builds a system in which the generation AI continuously monitors the progress of tasks and automatically reschedules them if a delay occurs. For example, if a child's homework is taking longer than expected, the subsequent tasks will be adjusted. This allows the system to grasp the progress of tasks in real time and respond quickly if a delay occurs.

[0063] The task management unit can detect the user's stress level using the emotion estimation function and make suggestions to reduce the task burden when stress increases. The task management unit, for example, uses the emotion estimation function to analyze the user's stress level in real time and make suggestions to reduce the task burden when stress increases. For example, if high stress is detected, the task management unit prioritizes suggestions for relaxing tasks. The task management unit also monitors the user's stress level and makes specific suggestions to reduce the task burden when stress increases. For example, it suggests outsourcing some housework to an external service. The task management unit also uses the emotion estimation function to analyze the user's stress level and automatically reschedule tasks to reduce the task burden when stress increases. For example, if high stress is detected, tasks are rearranged to ensure time for relaxation. This reduces the user's stress and achieves efficient task management.

[0064] The task management unit can manage not only housework and childcare tasks, but also other household activities such as pet care and gardening. For example, the generation AI manages not only housework and childcare tasks, but also other household activities such as pet care and gardening. For example, it automatically incorporates schedules for feeding and walking pets. The task management unit also centrally manages all household activities and efficiently schedules tasks such as housework, childcare, pet care, and gardening. For example, it adjusts gardening time to fit the housework and childcare schedule. The task management unit also manages tasks comprehensively, including other household activities, and balances them with housework and childcare tasks. For example, it optimizes pet care and gardening time to fit the housework and childcare schedule. This centrally manages all household activities and achieves efficient schedule management.

[0065] The task management unit can work with local community services and utilize external resources as needed. For example, the generation AI can work with local babysitter services or cleaning services and utilize external resources as needed. For example, it can automatically arrange for a babysitter service if there is a sudden change in plans. The task management unit can also work with local community services and utilize external resources to reduce the burden of housework and childcare. For example, it can make suggestions to reduce the burden of housework by using a cleaning service. The task management unit can also build a system in which the generation AI can work with local service providers and utilize external resources as needed. For example, it can provide a function to automatically arrange for a babysitter service or cleaning service. This allows the user to utilize local community services and reduce the burden of housework and childcare.

[0066] The task management unit can use the emotion estimation function to monitor the emotional states of all family members and suggest task allocations that will elicit positive emotions. For example, the task management unit uses the emotion estimation function to monitor the emotional states of all family members in real time and suggest task allocations that will elicit positive emotions. For example, it prioritizes suggesting tasks that all family members can enjoy. The task management unit also analyzes the emotional states of all family members and builds a system for task allocation that will elicit positive emotions. For example, it suggests tasks that all family members can complete cooperatively. The task management unit also uses the emotion estimation function to monitor the emotional states of all family members and automatically assign tasks that will elicit positive emotions. For example, it rearranges tasks to ensure that all family members have time to relax. This allows task allocation that takes into account the emotional states of all family members.

[0067] The communication unit uses the generation AI to analyze the communication history between family members, learn about past problems and complaints, and propose improvements to future communication. For example, the communication unit uses the generation AI to analyze the communication history between family members, learn about past problems and complaints, and propose improvements to future communication. For example, it provides specific advice to avoid past problems. The communication unit also analyzes the communication history between family members, learns about past complaints, and builds a system that proposes improvements to future communication. For example, it makes specific suggestions to resolve past complaints. The communication unit also uses the generation AI to analyze the communication history between family members, learn about past problems and complaints, and automatically proposes improvements to future communication. For example, it provides specific advice to avoid past problems. In this way, it learns about past problems and complaints and aims to improve future communication.

[0068] The communication unit can use the generation AI to automatically adjust family schedules and send reminders to prevent important events and appointments from being forgotten. For example, the communication unit uses the generation AI to automatically adjust the schedules of all family members and send reminders to prevent them from forgetting important events and appointments. For example, it integrates the calendars of all family members and notifies them of important appointments. The communication unit also analyzes family schedules and builds a system that sends reminders to prevent them from forgetting important events and appointments. For example, it automatically notifies them of important events such as birthdays and anniversaries. The communication unit also provides a function that uses the generation AI to automatically adjust the schedules of all family members and send reminders to prevent them from forgetting important events and appointments. For example, it integrates the calendars of all family members and notifies them of important appointments. This allows for efficient management of family schedules and prevents them from forgetting important events and appointments.

[0069] The communication unit can use the emotion estimation function to analyze the tone and emotions of communication between family members, and provide appropriate advice when negative emotions are heightened. For example, the communication unit can use the emotion estimation function to analyze the tone and emotions of communication between family members in real time, and provide appropriate advice when negative emotions are heightened. For example, specific suggestions can be made to stay calm. Furthermore, the communication unit can analyze the tone and emotions of communication between family members, and build a system that provides appropriate advice when negative emotions are heightened. For example, specific suggestions can be made to stay calm when emotions are heightened. Furthermore, the communication unit can use the emotion estimation function to analyze the tone and emotions of communication between family members, and automatically provide appropriate advice when negative emotions are heightened. For example, specific suggestions can be made to stay calm. This improves communication between family members and reduces negative emotions.

[0070] The communication unit can use the generative AI to suggest activities based on common hobbies and interests to support family communication. For example, the generative AI analyzes the hobbies and interests of all family members and suggests activities based on those common hobbies and interests. For example, it suggests outdoor activities that the whole family can enjoy. The communication unit also builds a system that analyzes the hobbies and interests of all family members and suggests activities based on those common hobbies and interests. For example, it suggests movie nights and sporting events that the whole family can enjoy. The communication unit also builds a system that analyzes the hobbies and interests of all family members and automatically suggests activities based on those common hobbies and interests. For example, it suggests outdoor activities that the whole family can enjoy. This suggests activities that the whole family can enjoy and promotes communication.

[0071] The communication unit can use the generative AI to provide interactive content such as games and quizzes to promote family communication. For example, the generative AI provides interactive content such as games and quizzes that the whole family can enjoy, promoting communication. For example, the communication unit suggests an online quiz that the whole family can participate in. The communication unit also builds a system that provides interactive content that the whole family can enjoy and promotes communication. For example, the communication unit suggests online games that the whole family can participate in. The communication unit also builds a system that automatically provides interactive content such as games and quizzes that the whole family can enjoy, promoting communication. For example, the communication unit suggests an online quiz ... This provides interactive content that the whole family can enjoy and promotes communication.

[0072] The communication unit can use the emotion estimation function to monitor the emotional states of all family members in real time and suggest communication methods to elicit positive emotions. For example, the communication unit uses the emotion estimation function to monitor the emotional states of all family members in real time and suggest communication methods to elicit positive emotions. For example, it suggests topics that will help all family members relax. The communication unit also builds a system that analyzes the emotional states of all family members and suggests communication methods to elicit positive emotions. For example, it suggests topics and activities that the whole family can enjoy. The communication unit also uses the emotion estimation function to monitor the emotional states of all family members in real time and automatically suggest communication methods to elicit positive emotions. For example, it suggests topics that will help all family members relax. In this way, it suggests communication methods that take into account the emotional states of all family members and elicit positive emotions.

[0073] The child-rearing support department can use the generation AI to analyze children's learning data and propose the optimal learning plan for each individual child. For example, the generation AI analyzes children's learning data and proposes the optimal learning plan for each individual child. For example, a customized learning plan is provided taking into account the child's strengths and weaknesses. The child-rearing support department also builds a system that analyzes children's learning data and proposes the optimal learning plan for each individual child. For example, the optimal learning plan is provided based on the child's learning progress and grades. The child-rearing support department also builds a system that analyzes children's learning data and proposes the optimal learning plan for each individual child. For example, a customized learning plan is provided taking into account the child's strengths and weaknesses. This provides the optimal learning plan for each individual child and improves learning effectiveness.

[0074] The child-rearing support department can use the generation AI to monitor children's health data in real time and immediately issue an alert if an abnormality is detected. For example, the generation AI in the child-rearing support department monitors children's health data in real time and immediately issue an alert if an abnormality is detected. For example, an alert is sent when an abnormality in body temperature or heart rate is detected. The child-rearing support department also builds a system that monitors children's health data in real time and immediately issue an alert if an abnormality is detected. For example, an alert is sent when an abnormality in body temperature or heart rate is detected. The child-rearing support department can also monitor children's health data in real time and automatically issue an alert if an abnormality is detected. For example, an alert is sent when an abnormality in body temperature or heart rate is detected. This allows children's health to be monitored in real time and allows for quick response when an abnormality occurs.

[0075] The child-rearing support unit can use the emotion estimation function to analyze a child's emotional state and suggest appropriate measures when stress or anxiety increases. The child-rearing support unit, for example, uses the emotion estimation function to analyze a child's emotional state in real time and suggest appropriate measures when stress or anxiety increases. For example, it suggests a relaxing activity. The child-rearing support unit also builds a system that analyzes a child's emotional state and suggests appropriate measures when stress or anxiety increases. For example, it suggests a relaxing activity. The child-rearing support unit also uses the emotion estimation function to analyze a child's emotional state in real time and automatically suggests appropriate measures when stress or anxiety increases. For example, it suggests a relaxing activity. In this way, the child's emotional state can be grasped in real time and appropriate measures can be suggested when stress or anxiety increases.

[0076] The child-rearing support unit can use the generation AI to automatically organize a child's growth record and create a digital album that can be shared by the entire family. For example, the generation AI automatically organizes a child's growth record and creates a digital album that can be shared by the entire family. For example, it organizes photos and videos in chronological order and compiles them into a digital album. The child-rearing support unit also builds a system that ... This organizes a child's growth record and creates a digital album that can be shared by the entire family.

[0077] The child-rearing support department can use the generation AI to collaborate with local child-rearing support services and provide information as needed. For example, the generation AI collaborates with local child-rearing support services and provides information as needed. For example, it automatically provides information on child-rearing consultations and parent-child events. The child-rearing support department also builds a system that ... generation AI collaborates with local child-rearing support services and provides information as needed. For example, it automatically provides information on child-rearing consultations and parent-child events. This collaborates with local child-rearing support services and provides necessary information.

[0078] The child-rearing support unit can use the emotion estimation function to monitor a child's emotional state and suggest activities to bring out positive emotions. For example, the child-rearing support unit uses the emotion estimation function to monitor a child's emotional state in real time and suggest activities to bring out positive emotions. For example, it suggests games and learning activities that the child can enjoy. The child-rearing support unit also builds a system that analyzes a child's emotional state and suggests activities to bring out positive emotions. For example, it suggests games and learning activities that the child can enjoy. The child-rearing support unit also uses the emotion estimation function to monitor a child's emotional state in real time and automatically suggest activities to bring out positive emotions. For example, it suggests games and learning activities that the child can enjoy. In this way, the child's emotional state is grasped in real time and activities to bring out positive emotions are suggested.

[0079] The task management unit not only uses generative AI to efficiently manage housework and childcare tasks, but also optimizes the schedules of all family members, reducing wasted time. For example, the task management unit uses generative AI to efficiently manage housework and childcare tasks and optimize the schedules of all family members, reducing wasted time. For example, it integrates the schedules of all family members and suggests the optimal division of tasks. The task management unit also builds a system that efficiently manages housework and childcare tasks and optimizes the schedules of all family members. For example, it integrates the schedules of all family members and suggests the optimal division of tasks. The task management unit also uses generative AI to efficiently manage housework and childcare tasks and optimize the schedules of all family members, reducing wasted time. For example, it integrates the schedules of all family members and suggests the optimal division of tasks. In this way, the task management unit efficiently manages housework and childcare tasks and optimizes the schedules of all family members, reducing wasted time.

[0080] The task management unit uses the generation AI to monitor the energy levels of all family members and adjust the task burden so that fatigue does not accumulate. For example, the generation AI monitors the energy levels of all family members in real time and adjusts the task burden so that fatigue does not accumulate. For example, when fatigue increases, it suggests tasks that will help you relax. The task management unit also builds a system that monitors the energy levels of all family members and adjusts the task burden so that fatigue does not accumulate. For example, when fatigue increases, it suggests tasks that will help you relax. The task management unit also builds a system that monitors the energy levels of all family members using the generation AI to monitor the energy levels of all family members in real time and automatically adjusts the task burden so that fatigue does not accumulate. For example, when fatigue increases, it suggests tasks that will help you relax. In this way, the energy levels of all family members are monitored and the task burden is adjusted so that fatigue does not accumulate.

[0081] The task management unit can use the emotion estimation function to analyze the stress levels of all family members and suggest relaxation methods when stress increases. The task management unit, for example, uses the emotion estimation function to analyze the stress levels of all family members in real time and suggest relaxation methods when stress increases. For example, it can suggest relaxing activities. The task management unit also builds a system that analyzes the stress levels of all family members and suggests relaxation methods when stress increases. For example, it can suggest relaxing activities. The task management unit also uses the emotion estimation function to analyze the stress levels of all family members in real time and automatically suggest relaxation methods when stress increases. For example, it can suggest relaxing activities. In this way, the stress levels of all family members are analyzed and relaxation methods are suggested when stress increases.

[0082] The task management unit can use the generation AI to optimize energy consumption within the home and make suggestions to reduce electricity and water usage. For example, the generation AI in the task management unit monitors energy consumption within the home in real time and makes suggestions to reduce electricity and water usage. For example, it may suggest the use of energy-efficient home appliances. The task management unit also builds a system that optimizes energy consumption within the home ... generation AI in the task management unit monitors energy consumption within the home in real time and automatically makes suggestions to reduce electricity and water usage. For example, it may suggest the use of energy-efficient home appliances. This optimizes energy consumption within the home and reduces electricity and water usage.

[0083] The task management unit can use a generation AI to manage household items and automatically generate a shopping list of necessary items. For example, the generation AI in the task management unit manages household items and automatically generates a shopping list of necessary items. For example, it monitors the contents of the refrigerator and lists the necessary ingredients. The task management unit also builds a system that manages household items and automatically generates a shopping list of necessary items. For example, it monitors the contents of the refrigerator and lists the necessary ingredients. The task management unit also builds a system that manages household items and automatically generates a shopping list of necessary items. For example, it monitors the contents of the refrigerator and lists the necessary ingredients. The task management unit also builds a system that manages household items and automatically generates a shopping list of necessary items. For example, it monitors the contents of the refrigerator and lists the necessary ingredients. This makes household item management more efficient and automatically generates a shopping list of necessary items.

[0084] The task management unit can use the emotion estimation function to monitor the emotional states of all family members and suggest relaxation methods to bring out positive emotions. For example, the task management unit uses the emotion estimation function to monitor the emotional states of all family members in real time and suggest relaxation methods to bring out positive emotions. For example, it suggests relaxing activities. The task management unit also builds a system that analyzes the emotional states of all family members and suggests relaxation methods to bring out positive emotions. For example, it suggests relaxing activities. The task management unit also uses the emotion estimation function to monitor the emotional states of all family members in real time and automatically suggests relaxation methods to bring out positive emotions. For example, it suggests relaxing activities. In this way, the emotional states of all family members are monitored and relaxation methods to bring out positive emotions are suggested.

[0085] The child-rearing support department can use the generating AI to periodically evaluate the happiness levels of all family members and propose specific action plans to improve happiness levels. For example, the generating AI in the child-rearing support department periodically evaluates the happiness levels of all family members and proposes specific action plans to improve happiness levels. For example, it proposes activities that the whole family can enjoy. The child-rearing support department also builds a system that ... In this way, the child-rearing support department periodically evaluates the happiness levels of all family members and proposes specific action plans to improve happiness levels.

[0086] The child-rearing support unit can use the generative AI to automatically manage family life events and plan special events. For example, the generative AI in the child-rearing support unit automatically manages family life events and plans special events. For example, birthdays and anniversaries are automatically incorporated into the schedule and preparations for the events are suggested. The child-rearing support unit also builds a system that automatically manages family life events and plans special events. For example, birthdays and anniversaries are automatically incorporated into the schedule and preparations for the events are suggested. The child-rearing support unit also builds a system that automatically manages family life events and plans special events. For example, birthdays and anniversaries are automatically incorporated into the schedule and preparations for the events are suggested. In this way, family life events are automatically managed and special events are planned.

[0087] The child-rearing support unit can use the emotion estimation function to analyze the emotional states of all family members and suggest activities that will bring out positive emotions. For example, the child-rearing support unit uses the emotion estimation function to analyze the emotional states of all family members in real time and suggest activities that will bring out positive emotions. For example, it suggests activities that the whole family can enjoy. Furthermore, the child-rearing support unit builds a system that analyzes the emotional states of all family members and suggests activities that will bring out positive emotions. For example, it suggests activities that the whole family can enjoy. Furthermore, the child-rearing support unit uses the emotion estimation function to analyze the emotional states of all family members in real time and automatically suggests activities that will bring out positive emotions. For example, it suggests activities that the whole family can enjoy. In this way, the emotional states of all family members are analyzed and activities that will bring out positive emotions are suggested.

[0088] The child-rearing support department can use the generating AI to monitor the health status of all family members and provide specific advice to maintain their health. For example, the generating AI monitors the health status of all family members in real time and provides specific advice to maintain their health. For example, it provides advice on diet and exercise. The child-rearing support department also builds a system that monitors the health status of all family members and provides specific advice to maintain their health. For example, it provides advice on diet and exercise. The child-rearing support department also builds a system that monitors the health status of all family members in real time and automatically provides specific advice to maintain their health. For example, it provides advice on diet and exercise. This monitors the health status of all family members and provides specific advice to maintain their health.

[0089] The child-rearing support department can use the generation AI to understand the hobbies and interests of all family members and suggest common activities. For example, the generation AI in the child-rearing support department can understand the hobbies and interests of all family members and suggest common activities. For example, it can suggest outdoor activities that the whole family can enjoy. The child-rearing support department can also build a system that understands ... use the generation AI to understand the hobbies and interests of all family members and automatically suggest common activities. For example, it can suggest outdoor activities that the whole family can enjoy. This allows the child-rearing support department to understand the hobbies and interests of all family members and suggest common activities.

[0090] The child-rearing support unit can use the emotion estimation function to monitor the emotional states of all family members and suggest activities that will bring out positive emotions. For example, the child-rearing support unit uses the emotion estimation function to monitor the emotional states of all family members in real time and suggest activities that will bring out positive emotions. For example, it suggests activities that the whole family can enjoy. The child-rearing support unit also builds a system that analyzes the emotional states of all family members and suggests activities that will bring out positive emotions. For example, it suggests activities that the whole family can enjoy. The child-rearing support unit also uses the emotion estimation function to monitor the emotional states of all family members in real time and automatically suggests activities that will bring out positive emotions. For example, it suggests activities that the whole family can enjoy. In this way, the emotional states of all family members are monitored and activities that will bring out positive emotions are suggested.

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

[0092] The task management unit can manage not only housework and childcare tasks, but also other household activities such as pet care and gardening. For example, the generation AI automatically incorporates schedules for feeding and walking pets in addition to housework and childcare tasks. The generation AI also centrally manages all household activities and efficiently schedules tasks such as housework, childcare, pet care, and gardening. Furthermore, the generation AI performs comprehensive task management, including other household activities, and balances them with housework and childcare tasks. This allows for centralized management of all household activities and efficient schedule management.

[0093] The task management unit can link with local community services and utilize external resources as needed. For example, the generation AI can link with local babysitter services and cleaning services to automatically arrange babysitter services if there is a sudden change in plans. The generation AI can also link with local community services to utilize external resources to reduce the burden of housework and childcare. Furthermore, the generation AI can link with local service providers to provide a function to automatically arrange babysitter services and cleaning services. This allows users to utilize local community services to reduce the burden of housework and childcare.

[0094] The task management unit can use the emotion estimation function to detect the user's stress level and make suggestions to reduce the task burden when stress increases. For example, the emotion estimation function can be used to analyze the user's stress level in real time, and if high stress is detected, it can prioritize and suggest relaxing tasks. It can also monitor the user's stress level and suggest outsourcing some of the housework to an external service. Furthermore, the emotion estimation function can be used to analyze the user's stress level and reallocate tasks to ensure time for relaxation. This reduces the user's stress and enables efficient task management.

[0095] The task management unit can use the generative AI to optimize energy consumption within the home and make suggestions for reducing electricity and water usage. For example, the generative AI can monitor energy consumption within the home in real time and suggest the use of energy-efficient home appliances. It can also build a system that optimizes energy consumption within the home and reduces electricity and water usage. Furthermore, the generative AI can monitor energy consumption within the home in real time and automatically suggest the use of energy-efficient home appliances. This makes it possible to optimize energy consumption within the home and reduce electricity and water usage.

[0096] The task management unit can use the emotion estimation function to monitor the emotional state of each family member and suggest task allocation that will elicit positive emotions. For example, the emotion estimation function can be used to monitor the emotional state of each family member in real time and prioritize tasks that the whole family can enjoy. In addition, a system can be constructed that analyzes the emotional state of each family member and suggests tasks that the whole family can do together. Furthermore, the emotion estimation function can be used to monitor the emotional state of each family member and rearrange tasks to ensure that everyone has time to relax. This makes it possible to allocate tasks that take into account the emotional state of each family member.

[0097] The communication department can use generative AI to automatically adjust family schedules and send reminders to prevent important events and appointments from being forgotten. For example, generative AI can automatically adjust the schedules of all family members, integrate everyone's calendars, and notify them of important appointments. It can also build a system that analyzes family schedules and automatically notifies them of important events such as birthdays and anniversaries. Furthermore, it provides a function where generative AI can automatically adjust the schedules of all family members, integrate everyone's calendars, and notify them of important appointments. This allows for efficient management of family schedules and prevents important events and appointments from being forgotten.

[0098] The communication unit uses the emotion estimation function to analyze the tone and emotions of communication between family members, and can provide appropriate advice when negative emotions rise. For example, the emotion estimation function can be used to analyze the tone and emotions of communication between family members in real time, and provide specific suggestions for staying calm. We also build a system that analyzes the tone and emotions of communication between family members and provides specific suggestions for staying calm when emotions rise. Furthermore, the emotion estimation function can be used to analyze the tone and emotions of communication between family members, and automatically provide specific suggestions for staying calm. This can improve communication between family members and reduce negative emotions.

[0099] The communication department can use generative AI to suggest activities based on shared hobbies and interests to support family communication. For example, generative AI can analyze the hobbies and interests of each family member and suggest outdoor activities that the whole family can enjoy. We can also build a system that analyzes the hobbies and interests of each family member and suggests movie nights and sporting events that the whole family can enjoy. Furthermore, generative AI can analyze the hobbies and interests of each family member and automatically suggest outdoor activities that the whole family can enjoy. This can suggest activities that the whole family can enjoy and promote communication.

[0100] The child-rearing support department can use generative AI to analyze children's learning data and propose the optimal learning plan for each individual child. For example, generative AI can provide a customized learning plan taking into account a child's strengths and weaknesses. In addition, a system can be built that analyzes children's learning data and provides the optimal learning plan based on the child's learning progress and grades. Furthermore, generative AI can automatically provide a customized learning plan taking into account a child's strengths and weaknesses. This makes it possible to provide the optimal learning plan for each individual child and improve learning effectiveness.

[0101] The child-rearing support unit can use the emotion estimation function to analyze a child's emotional state and suggest appropriate measures when stress or anxiety increases. For example, the emotion estimation function can be used to analyze a child's emotional state in real time and suggest activities that will help them relax. A system can also be built that analyzes a child's emotional state and suggests activities that will help them relax. Furthermore, the emotion estimation function can be used to analyze a child's emotional state in real time and automatically suggest activities that will help them relax. This makes it possible to grasp a child's emotional state in real time and suggest appropriate measures when stress or anxiety increases.

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

[0103] Step 1: The task management unit manages and optimizes household and childcare tasks. For example, it uses generation AI to automatically schedule tasks such as cleaning, laundry, cooking, and taking children to and from school, ensuring a fair division of the work between spouses. It also uses generation AI to reevaluate task priorities in real time, allowing it to respond to sudden schedule changes. It also uses generation AI to monitor the progress of each task and automatically reschedules it if a delay occurs. Step 2: The communication section enables smooth communication between spouses and the entire family. For example, chat and video call functions make it easy for family members to share information and discuss matters. Generative AI is also used to analyze communication history between family members, learning from past problems and complaints and suggesting ways to improve communication in the future. Step 3: The childcare support department supports children's growth and health. For example, it uses AI generation to analyze children's health data and growth records and provide appropriate advice. AI generation monitors children's health data in real time and immediately issues an alert if an abnormality is detected.

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

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (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.

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

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

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

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

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

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

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

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

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

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

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

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

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

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

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

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

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

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

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

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0138] 7, 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

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

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

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

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

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

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

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

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

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

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

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

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

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

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

[0162] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

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

Claims

1. A task management section that manages and optimizes housework and childcare tasks, The communication department ensures smooth communication between couples and the whole family, We have a childcare support department that supports children's growth and health. A system characterized by:

2. The task management unit Using generative AI, the priorities of these tasks are reevaluated in real time, allowing for sudden schedule changes. The system of claim 1 .

3. The task management unit Generative AI is used to monitor the progress of the task and automatically reschedule it if a delay occurs. The system of claim 1 .

4. The task management unit Detecting the user's stress level, and when the stress level increases, making suggestions to reduce the burden of the task The system of claim 1 .

5. The task management unit Manage not only the housework and childcare tasks, but also other household activities such as pet care and yard work. The system of claim 1 .

6. The task management unit Collaborate with local community services and utilize external resources as needed The system of claim 1 .

7. The task management unit Monitor the emotional state of each family member and suggest tasks to elicit positive emotions The system of claim 1 .

8. The communication unit Generative AI is used to analyze communication history between family members, learn about problems and complaints, and propose ways to improve said communication in the future. The system of claim 1 .

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