system

Abstract

The system according to this embodiment aims to facilitate schedule sharing and task management among family members and to promote communication. [Solution] The system according to the embodiment comprises a schedule sharing unit, a task management unit, a reminder unit, an emotion sharing unit, a health management unit, and a predictive support unit. The schedule sharing unit shares and coordinates the schedules of each family member. The task management unit manages household tasks and tracks their progress based on the schedule shared by the schedule sharing unit. The reminder unit provides reminder functions based on the tasks managed by the task management unit. The emotion sharing unit provides suggestion tools for emotion sharing based on the reminders provided by the reminder unit. The health management unit provides health management functions based on the suggestions provided by the emotion sharing unit. The predictive support unit provides predictive support functions based on health management information provided by the health management unit.

Description

Technical Field

[0006] , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there are problems that schedule sharing and task management among family members are not sufficiently carried out, and there are deficiencies in communication and difficulties in household coordination.

[0005] The system according to the embodiment aims to facilitate schedule sharing and task management among family members and promote communication.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a schedule sharing unit, a task management unit, a reminder unit, an emotion sharing unit, a health management unit, and a predictive support unit. The schedule sharing unit shares and coordinates the schedules of each family member. The task management unit manages household tasks and tracks their progress based on the schedule shared by the schedule sharing unit. The reminder unit provides reminder functions based on the tasks managed by the task management unit. The emotion sharing unit provides suggestion tools for emotion sharing based on the reminders provided by the reminder unit. The health management unit provides health management functions based on the suggestions provided by the emotion sharing unit. The predictive support unit provides predictive support functions based on health management information provided by the health management unit. [Effects of the Invention]

[0007] The system according to this embodiment can facilitate schedule sharing and task management among family members, and promote communication. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. 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, a specific processing unit 290 (see FIG. 2) acquires data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The next-generation AI agent system according to an embodiment of the present invention is a system for facilitating the daily life of a family. This system promotes communication among family members and supports the management of household tasks and schedule adjustments. For example, the next-generation AI agent system includes a schedule sharing unit that shares and adjusts the schedules of each family member. The schedule sharing unit shares and adjusts the schedules of each family member. Next, the next-generation AI agent system includes a task management unit that manages household tasks and tracks their progress. The task management unit manages household tasks and tracks their progress. Furthermore, the next-generation AI agent system includes a reminder unit that provides a reminder function. The reminder unit provides a reminder function. Next, the next-generation AI agent system includes an emotion sharing unit that provides suggestion tools for emotion sharing. The emotion sharing unit provides suggestion tools for emotion sharing. Furthermore, the next-generation AI agent system includes a health management unit that provides health management functions. The health management unit provides health management functions. Finally, the next-generation AI agent system includes a prediction support unit that provides prediction support functions. The prediction support unit provides prediction support functions. This will enable the next-generation AI agent system to increase the amount of time that all family members can spend together without stress. It will facilitate the smooth running of family daily life, promote communication among family members, and support the management of household tasks and scheduling.

[0029] The next-generation AI agent system according to this embodiment comprises a schedule sharing unit, a task management unit, a reminder unit, an emotion sharing unit, a health management unit, and a predictive support unit. The schedule sharing unit shares and adjusts the schedules of each family member. For example, the schedule sharing unit displays and shares the schedules of each family member in a calendar format. The schedule sharing unit can also adjust schedules to avoid overlaps in the schedules of each family member. For example, the schedule sharing unit updates and shares the schedules of each family member in real time. The schedule sharing unit can also notify each family member of changes in their schedule. The task management unit manages household tasks and tracks their progress. For example, the task management unit displays and manages household tasks in a list format. The task management unit can also update and track the progress of household tasks in real time. For example, the task management unit displays and manages the progress of household tasks in a graph format. The task management unit can also notify when household tasks are completed. The Reminder Unit provides reminder functionality. For example, the Reminder Unit notifies users of deadlines for household tasks. The Reminder Unit can also notify users of the progress of household tasks. For example, the Reminder Unit notifies users when the deadline for a household task is approaching. The Reminder Unit can also periodically notify users of the progress of household tasks. The Emotion Sharing Unit provides suggestion tools for sharing emotions. For example, the Emotion Sharing Unit makes suggestions for sharing the emotions of each family member. The Emotion Sharing Unit can also provide tools for sharing the emotions of each family member. For example, the Emotion Sharing Unit provides an application for sharing the emotions of each family member. The Emotion Sharing Unit can also provide a chat function for sharing the emotions of each family member. The Health Management Unit provides health management functionality. For example, the Health Management Unit manages the health status of each family member. The Health Management Unit can also monitor the health status of each family member. For example, the Health Management Unit displays and manages the health status of each family member in graph format.Furthermore, the health management unit can notify the family of changes in the health status of each family member. The predictive support unit provides predictive support functions. For example, the predictive support unit predicts and supports the schedules of each family member. The predictive support unit can also predict and support the progress of each family member's tasks. For example, the predictive support unit predicts and notifies the family of changes in their schedules. The predictive support unit can also predict and notify the family of changes in the progress of each family member's tasks. As a result, the next-generation AI agent system according to this embodiment can streamline the family's daily life, promote communication among family members, and support the management of household tasks and schedule adjustments.

[0030] The Schedule Sharing section allows family members to share and coordinate their schedules. For example, it displays and shares each family member's schedule in a calendar format. Specifically, the Schedule Sharing section provides a cloud-based calendar system for centralized management of each member's schedule. This calendar system allows each member to input their own schedule and share it with others. For example, if a family member makes a hospital appointment, that information is reflected in the calendars of other members in real time. This makes it easier for all family members to keep track of each other's schedules, reducing the hassle of scheduling conflicts and coordination. The Schedule Sharing section also has a function to send immediate notifications when there are changes to the schedule. For example, if there is a sudden change or cancellation of a schedule, that information is conveyed to all family members via push notification or email. Furthermore, the Schedule Sharing section can optimize schedules using AI. For example, it analyzes the schedules of all family members and suggests setting up family meetings and events at the optimal time. In this way, the Schedule Sharing section plays a role in streamlining family schedule management and facilitating smooth communication.

[0031] The Task Management Department manages household tasks and tracks their progress. For example, it displays and manages household tasks in a list format. Specifically, the Task Management Department provides a task management application accessible to all family members. This application allows each member to input their tasks and update their progress. For instance, it lists household tasks such as cleaning, shopping, and cooking, clearly indicating who is responsible for which task. The Task Management Department also updates and tracks task progress in real time. For example, when a task is completed, this information is immediately notified to other members. Furthermore, the Task Management Department includes a function to display task progress in a graph format, allowing all family members to grasp the progress at a glance. The Task Management Department also provides a function to set task priorities. For example, tasks can be categorized according to importance and deadline, allowing for work on high-priority tasks first. In this way, the Task Management Department streamlines household task management and supports all family members in collaborating to complete tasks.

[0032] The Reminders app provides reminder functionality. For example, it notifies users of deadlines for household tasks. Specifically, it manages the deadlines of tasks set by each member and sends notifications when the deadline approaches. For example, when the deadline for an important task, such as garbage collection day or payment deadline, is approaching, this information is conveyed to all family members via push notifications or email. The Reminders app can also notify users of the progress of household tasks. For example, if a task is behind schedule or has passed its deadline, it notifies users of this information to encourage completion. In this way, the Reminders app supports household task management and helps prevent tasks from being missed or delayed. Furthermore, the Reminders app also provides a function to set recurring reminders. For example, by sending regular reminders for tasks that need to be done regularly, such as weekly cleaning or monthly payments, all family members can complete the tasks without forgetting. In this way, the Reminders app streamlines household task management and supports all family members in cooperating to complete tasks.

[0033] The Emotion Sharing Department provides suggestion tools for sharing emotions. For example, it makes suggestions for sharing the emotions of each family member. Specifically, the Emotion Sharing Department provides an emotion sharing application that all family members can access. In this application, each member can input their emotions and share them with other members. For example, by recording daily events and feelings and sharing them with the whole family, it becomes easier to understand each other's emotions. The Emotion Sharing Department also has a function to suggest sharing emotions. For example, it sends notifications that encourage sharing emotions in response to specific events or occurrences. In this way, the Emotion Sharing Department promotes communication among family members and supports mutual understanding of each other's emotions. Furthermore, the Emotion Sharing Department also provides a chat function for sharing emotions. For example, emotions and events can be shared in real time through a group chat that all family members can participate in. In this way, the Emotion Sharing Department facilitates communication among family members and supports mutual understanding of each other's emotions.

[0034] The Health Management Department provides health management functions. For example, it manages the health status of each family member. Specifically, the Health Management Department provides an application that allows each member to input their own health data and share it with other members. This application can record health data such as weight, blood pressure, heart rate, and sleep duration, and display it in a graph format. The Health Management Department also has a function to notify users of changes in health status. For example, if an abnormality is detected in the health data, this information will be sent to all family members via push notification or email. In this way, the Health Management Department plays a role in enabling all family members to understand each other's health status and support health management. Furthermore, the Health Management Department also provides functions to give health advice and suggestions. For example, it analyzes health data and provides advice on exercise and diet. In this way, the Health Management Department supports all family members in leading healthy lives.

[0035] The Predictive Support Department provides predictive support functions. For example, it predicts and supports the schedules of each family member. Specifically, the Predictive Support Department uses AI to analyze each member's schedule and task progress, and predicts future plans and task progress. This AI can suggest optimal schedules and task progress based on past data and current circumstances. For example, it analyzes the schedules of all family members and suggests setting family meetings and events at the optimal time. It also analyzes the progress of tasks and notifies users to take action early if delays are expected. In this way, the Predictive Support Department supports all family members in efficiently managing their schedules and completing tasks. Furthermore, the Predictive Support Department also has the function of predicting future risks and problems and suggesting countermeasures to take in advance. For example, it analyzes health data, predicts future health risks, and suggests taking action early. In this way, the Predictive Support Department supports all family members in efficiently managing their schedules and leading healthy lives.

[0036] The schedule sharing unit can analyze the family's past schedule history and select the optimal schedule sharing method. For example, the schedule sharing unit can analyze the family's past schedule history and automatically add frequently occurring events to the schedule. It can also analyze the family's past schedule history and suggest the optimal schedule sharing method for specific days of the week or time slots. Furthermore, the schedule sharing unit can analyze the family's past schedule history and select the optimal sharing method to avoid schedule overlaps. In this way, the optimal schedule sharing method can be selected by analyzing past schedule history. Some or all of the above processing in the schedule sharing unit may be performed using AI, for example, or without AI. For example, the schedule sharing unit can input the family's past schedule data into a generating AI and have the generating AI select the optimal schedule sharing method.

[0037] The schedule sharing unit can automatically adjust the schedule based on each family member's priorities when the schedule is shared. For example, the schedule sharing unit considers each family member's priorities and prioritizes scheduling important tasks. It can also consider each family member's priorities and postpone lower-priority tasks. Furthermore, the schedule sharing unit can consider each family member's priorities and make adjustments to avoid schedule conflicts. This allows important tasks to be processed preferentially by adjusting the schedule based on family priorities. Some or all of the above processing in the schedule sharing unit may be performed using AI, for example, or not. For example, the schedule sharing unit can input family priority data into a generating AI and have the generating AI perform the automatic schedule adjustment.

[0038] The schedule sharing unit can prioritize sharing highly relevant schedules by considering the geographical location information of family members when sharing schedules. For example, the schedule sharing unit can consider the geographical location information of family members and prioritize adding nearby events to the schedule. It can also consider the geographical location information of family members and postpone events that take place far away. Furthermore, the schedule sharing unit can consider the geographical location information of family members and suggest a schedule that minimizes travel time. In this way, highly relevant schedules can be shared preferentially by considering geographical location information. Some or all of the above processing in the schedule sharing unit may be performed using AI, for example, or not using AI. For example, the schedule sharing unit can input the geographical location information of family members into a generating AI and have the generating AI select highly relevant schedules.

[0039] The schedule sharing unit can analyze the family's social media activity and share relevant schedules when sharing schedules. For example, the schedule sharing unit can analyze the family's social media activity and add planned event attendance to the schedule. It can also analyze the family's social media activity and reflect plans with friends in the schedule. Furthermore, the schedule sharing unit can analyze the family's social media activity and suggest events of interest to the schedule. In this way, relevant schedules can be shared by analyzing social media activity. Some or all of the above processing in the schedule sharing unit may be performed using AI, for example, or not using AI. For example, the schedule sharing unit can input family social media data into a generating AI and have the generating AI select relevant schedules.

[0040] The task management unit can analyze the family's past task history to select the optimal task management method during task management. For example, the task management unit can analyze the family's past task history and automatically add frequently performed tasks. It can also analyze the family's past task history and suggest the optimal task management method for specific days of the week or time slots. Furthermore, the task management unit can analyze the family's past task history and select the optimal management method to avoid task duplication. In this way, the optimal task management method can be selected by analyzing past task history. Some or all of the above processes in the task management unit may be performed using AI, for example, or without AI. For example, the task management unit can input the family's past task data into a generating AI and have the generating AI select the optimal task management method.

[0041] The task management unit can automatically assign tasks based on the skill sets of each family member during task management. For example, the task management unit considers the skill sets of each family member and assigns appropriate tasks. The task management unit can also consider the skill sets of each family member and adjust the assignment according to the difficulty of the task. Furthermore, the task management unit can consider the skill sets of each family member and make assignments that maximize task efficiency. This enables efficient task management by assigning tasks based on each member's skill set. Some or all of the above processes in the task management unit may be performed using AI, for example, or not using AI. For example, the task management unit can input family skill set data into a generating AI and have the generating AI perform automatic task assignment.

[0042] The task management unit can prioritize tasks based on their relevance, taking into account the geographical location information of family members. For example, the task management unit can prioritize tasks performed nearby, taking into account the geographical location information of family members. It can also postpone tasks performed far away, taking into account the geographical location information of family members. Furthermore, the task management unit can propose task management strategies that minimize travel time, taking into account the geographical location information of family members. In this way, by considering geographical location information, tasks based on relevance can be prioritized. Some or all of the above processes in the task management unit may be performed using AI, for example, or not. For example, the task management unit can input the geographical location information of family members into a generating AI and have the generating AI select tasks based on relevance.

[0043] The task management unit can analyze family members' social media activity and manage related tasks during task management. For example, the task management unit can analyze family members' social media activity and add planned event attendance as tasks. It can also analyze family members' social media activity and reflect plans with friends as tasks. Furthermore, the task management unit can analyze family members' social media activity and suggest events of interest as tasks. In this way, related tasks can be managed by analyzing social media activity. Some or all of the above processing in the task management unit may be performed using AI, for example, or not using AI. For example, the task management unit can input family social media data into a generating AI and have the generating AI select related tasks.

[0044] The reminder function can adjust the level of detail of a reminder based on the importance of the task when setting a reminder. For example, if the task is highly important, the reminder function will set a detailed reminder. Conversely, if the task is less important, the reminder function can set a concise reminder. Furthermore, the reminder function can also adjust the frequency of reminder notifications according to the importance of the task. This allows important tasks to be notified preferentially by adjusting the level of detail of the reminder based on the importance of the task. Some or all of the above processing in the reminder function may be performed using AI, for example, or without AI. For example, the reminder function can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the reminder.

[0045] The reminder function can apply different reminder algorithms depending on the task category when setting a reminder. For example, if the task category is work, the reminder function can apply a business-oriented reminder algorithm. It can also apply a home-oriented reminder algorithm if the task category is home-related, and a hobby-related reminder algorithm if the task category is hobbies. This allows for appropriate notifications by applying the correct reminder algorithm according to the task category. Some or all of the above processing in the reminder function may be performed using AI, or without AI. For example, the reminder function can input task category data into a generating AI and have the generating AI apply the reminder algorithm.

[0046] The reminder unit can determine the priority of reminders based on the task submission deadline when setting reminders. For example, the reminder unit will prioritize reminders when the task submission deadline is approaching. Conversely, the reminder unit can also lower the priority of reminders when the task submission deadline is far away. Furthermore, the reminder unit can adjust the frequency of reminder notifications according to the task submission deadline. This allows important tasks to be notified preferentially by determining the priority of reminders based on the task submission deadline. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input task submission deadline data into a generating AI and have the generating AI determine the priority of reminders.

[0047] The reminder unit can adjust the order of reminders based on the relevance of tasks when setting reminders. For example, the reminder unit can prioritize setting reminders for highly relevant tasks. It can also postpone less relevant tasks. Furthermore, the reminder unit can adjust the notification order of reminders according to the relevance of tasks. This allows for priority notification of highly relevant tasks by adjusting the order of reminders based on the relevance of tasks. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input task relevance data into a generating AI and have the generating AI perform the adjustment of the reminder order.

[0048] The emotion sharing unit can analyze the family's past emotional history to select the optimal method of emotion sharing. For example, the emotion sharing unit can analyze the family's past emotional history and suggest regular emotion sharing to members whose emotions fluctuate little. It can also analyze the family's past emotional history and adjust the frequency of emotion sharing for members whose emotions fluctuate greatly. Furthermore, if the family's past emotional history indicates that emotional changes are related to a specific event, the emotion sharing unit can suggest emotion sharing tailored to that event. In this way, the optimal method of emotion sharing can be selected by analyzing past emotional history. Some or all of the above processing in the emotion sharing unit may be performed using AI, for example, or without AI. For example, the emotion sharing unit can input the family's past emotional data into a generating AI and have the generating AI select the optimal method of emotion sharing.

[0049] The emotion sharing unit can adjust the timing of emotion sharing based on the emotional changes of each family member. For example, the emotion sharing unit can monitor the emotional changes of family members in real time and suggest emotion sharing when emotions are stable. It can also monitor the emotional changes of family members in real time and refrain from emotion sharing when emotions are unstable. Furthermore, it can monitor the emotional changes of family members in real time and suggest emotion sharing when emotions are positive. By adjusting the timing of emotion sharing based on emotional changes, emotion sharing can be performed at the appropriate time. Some or all of the above processing in the emotion sharing unit may be performed using AI, for example, or without AI. For example, the emotion sharing unit can input family emotional data into a generating AI and have the generating AI perform an evaluation of emotional changes.

[0050] The emotion sharing unit can prioritize highly relevant emotion sharing by considering the geographical location information of family members during emotion sharing. For example, the emotion sharing unit can prioritize emotion sharing with nearby members by considering the geographical location information of family members. It can also postpone emotion sharing with members who are far away by considering the geographical location information of family members. Furthermore, the emotion sharing unit can also propose emotion sharing that minimizes travel time by considering the geographical location information of family members. In this way, highly relevant emotion sharing can be prioritized by considering geographical location information. Some or all of the above processing in the emotion sharing unit may be performed using AI, for example, or without the use of AI. For example, the emotion sharing unit can input the geographical location information of family members into a generating AI and have the generating AI select highly relevant emotion sharing.

[0051] The emotion sharing unit can analyze the family's social media activity and share relevant emotions when sharing emotions. For example, the emotion sharing unit can analyze the family's social media activity and share emotions in line with planned event participation. It can also analyze the family's social media activity and share emotions based on interactions with friends. Furthermore, the emotion sharing unit can analyze the family's social media activity and share emotions related to events of interest. In this way, relevant emotions can be shared by analyzing social media activity. Some or all of the above processing in the emotion sharing unit may be performed using AI, for example, or without AI. For example, the emotion sharing unit can input family social media data into a generating AI and have the generating AI select relevant emotions.

[0052] The Health Management Department can analyze a family's past health history to select the optimal health management method during health management. For example, the Health Management Department can analyze a family's past health history and propose frequently performed health management methods. Furthermore, the Health Management Department can analyze a family's past health history and select the optimal management method for specific health problems. In addition, the Health Management Department can analyze a family's past health history and propose management methods that adapt to changes in health status. This allows for the selection of the optimal health management method by analyzing past health history. Some or all of the above processes in the Health Management Department may be performed using AI, for example, or without AI. For example, the Health Management Department can input the family's past health data into a generating AI and have the generating AI select the optimal health management method.

[0053] The health management department can customize health management methods based on the health status of each family member during health management. For example, the health management department can consider the health status of each family member and propose appropriate health management methods. The health management department can also adjust the frequency of health management considering the health status of each family member. Furthermore, the health management department can customize the content of health management considering the health status of each family member. This allows for appropriate health management by customizing health management methods based on the health status of each member. Some or all of the above processes in the health management department may be performed using AI, for example, or without AI. For example, the health management department can input family health status data into a generating AI and have the generating AI perform the customization of health management methods.

[0054] The Health Management Department can select the optimal health management method when providing health management services, taking into account the geographical location information of the family. For example, the Health Management Department can prioritize suggesting nearby medical institutions, taking into account the family's geographical location information. It can also postpone suggesting distant medical institutions, taking into account the family's geographical location information. Furthermore, the Health Management Department can suggest health management methods that minimize travel time, taking into account the family's geographical location information. In this way, the optimal health management method can be selected by considering geographical location information. Some or all of the above processing in the Health Management Department may be performed using AI, for example, or without AI. For example, the Health Management Department can input the family's geographical location information into a generating AI and have the generating AI select the optimal health management method.

[0055] The Health Management Department can analyze a family's social media activity and propose health management strategies during health management. For example, the Health Management Department can analyze a family's social media activity and propose health management strategies based on health-related posts. It can also analyze a family's social media activity and propose strategies based on the health management methods of their friends. Furthermore, the Health Management Department can analyze a family's social media activity and propose health management methods of interest. This allows for the proposal of appropriate health management strategies through the analysis of social media activity. Some or all of the above processes in the Health Management Department may be performed using AI, for example, or without AI. For example, the Health Management Department can input family social media data into a generating AI and have the generating AI generate suggestions for health management strategies.

[0056] The prediction support unit can analyze the family's past data to select the optimal prediction support method during prediction support. For example, the prediction support unit can analyze the family's past data and provide prediction support based on frequently occurring activities. The prediction support unit can also analyze the family's past data and select the optimal prediction support method for a specific event. Furthermore, the prediction support unit can analyze the family's past data and provide prediction support in response to changes in activities. In this way, the optimal prediction support method can be selected by analyzing past data. Some or all of the above processing in the prediction support unit may be performed using AI, for example, or without AI. For example, the prediction support unit can input the family's past data into a generating AI and have the generating AI select the optimal prediction support method.

[0057] The predictive support unit can improve the accuracy of predictive support based on the behavioral patterns of each family member during the prediction support process. For example, the predictive support unit can analyze the behavioral patterns of each family member and provide appropriate predictive support. The predictive support unit can also analyze the behavioral patterns of each family member and adjust the frequency of predictive support. Furthermore, the predictive support unit can analyze the behavioral patterns of each family member and customize the content of the predictive support. This allows for appropriate support by improving the accuracy of predictive support based on the behavioral patterns of each member. Some or all of the above-described processes in the predictive support unit may be performed using AI, for example, or without AI. For example, the predictive support unit can input family behavioral pattern data into a generating AI and have the generating AI perform the task of improving the accuracy of predictive support.

[0058] The prediction support unit can select the optimal prediction support method by considering the geographical location information of the family during prediction support. For example, the prediction support unit can consider the geographical location information of the family and provide prediction support based on activities taking place nearby. The prediction support unit can also consider the geographical location information of the family and postpone activities taking place far away. Furthermore, the prediction support unit can consider the geographical location information of the family and propose prediction support to minimize travel time. In this way, the optimal prediction support method can be selected by considering geographical location information. Some or all of the above processing in the prediction support unit may be performed using AI, for example, or without using AI. For example, the prediction support unit can input the geographical location information of the family into a generating AI and have the generating AI perform the selection of the optimal prediction support method.

[0059] The prediction support unit can analyze the family's social media activity and propose means of prediction support during prediction support. For example, the prediction support unit can analyze the family's social media activity and provide prediction support in line with planned event participation. It can also analyze the family's social media activity and provide prediction support based on interactions with friends. Furthermore, it can analyze the family's social media activity and provide prediction support related to events of interest. In this way, by analyzing social media activity, it can propose appropriate means of prediction support. Some or all of the above processing in the prediction support unit may be performed using AI, for example, or without AI. For example, the prediction support unit can input the family's social media data into a generating AI and have the generating AI execute the proposal of means of prediction support.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The next-generation AI agent system can suggest events and activities based on each family member's hobbies and interests. For example, the schedule sharing function can automatically add events related to each family member's hobbies to the calendar. It can also suggest weekend activities based on each family member's interests. Furthermore, it can suggest events that the whole family can enjoy, based on each family member's hobbies and interests. This allows for more enjoyable time for the whole family by adjusting schedules based on each family member's hobbies and interests.

[0062] The task management system can assign tasks based on each family member's skill set. For example, it can assign cooking-related tasks to members who are good at cooking, cleaning-related tasks to members who are good at cleaning, and repair-related tasks to members who are good at DIY. By assigning tasks based on each family member's skill set, efficient task management becomes possible.

[0063] The reminder function can adjust the timing of reminder notifications based on each family member's daily routine. For example, it can send early morning reminders to members who are early risers, and late-night reminders to members who are night owls. Furthermore, it can send daytime reminders to members who are active during the day. By adjusting the timing of reminder notifications based on the family's daily routine, more effective reminder notifications become possible.

[0064] The Health Management Department can provide health management advice based on the health status of each family member. For example, it can advise members who are not getting enough exercise to start exercising. It can also suggest balanced meals to members with poor eating habits. Furthermore, it can provide relaxation advice to members who are experiencing stress. In this way, by providing health management advice based on the health status of each family member, the department can support the health of the entire family.

[0065] The predictive support unit can improve the accuracy of its predictive support based on the behavioral patterns of each family member. For example, the predictive support unit analyzes the behavioral patterns of each family member and provides appropriate predictive support. It can also analyze the behavioral patterns of each family member and adjust the frequency of predictive support. Furthermore, the predictive support unit can analyze the behavioral patterns of each family member and customize the content of the predictive support. This allows for improved accuracy of predictive support based on each member's behavioral patterns, enabling more appropriate support.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The schedule sharing section shares and coordinates the schedules of each family member. For example, the schedule sharing section displays and shares each family member's schedule in a calendar format. The schedule sharing section can also adjust schedules to avoid overlaps among family members. The schedule sharing section updates and shares each family member's schedule in real time and notifies them of schedule changes. Step 2: The task management unit manages household tasks and tracks their progress. For example, the task management unit displays and manages household tasks in a list format. It can also update and track the progress of household tasks in real time. The task management unit displays and manages the progress of household tasks in a graph format and notifies users when tasks are completed. Step 3: The reminder section provides reminder functionality. For example, the reminder section notifies users of deadlines for household tasks and provides updates on task progress. The reminder section notifies users when deadlines for household tasks are approaching and provides regular updates on task progress. Step 4: The Emotion Sharing Department provides suggestion tools for sharing emotions. For example, the Emotion Sharing Department makes suggestions for sharing the emotions of each family member and provides tools for sharing emotions. The Emotion Sharing Department provides applications and chat functions for sharing the emotions of each family member. Step 5: The Health Management Department provides health management functions. For example, the Health Management Department manages and monitors the health status of each family member. The Health Management Department displays and manages the health status of each family member in graph format and notifies of changes in health status. Step 6: The predictive support unit provides predictive support functions. For example, the predictive support unit predicts and supports the schedules of each family member. The predictive support unit can predict and support the progress of tasks for each family member. The predictive support unit predicts and notifies changes in each family member's schedule and predicts and notifies changes in task progress.

[0068] (Example of form 2) The next-generation AI agent system according to an embodiment of the present invention is a system for facilitating the daily life of a family. This system promotes communication among family members and supports the management of household tasks and schedule adjustments. For example, the next-generation AI agent system includes a schedule sharing unit that shares and adjusts the schedules of each family member. The schedule sharing unit shares and adjusts the schedules of each family member. Next, the next-generation AI agent system includes a task management unit that manages household tasks and tracks their progress. The task management unit manages household tasks and tracks their progress. Furthermore, the next-generation AI agent system includes a reminder unit that provides a reminder function. The reminder unit provides a reminder function. Next, the next-generation AI agent system includes an emotion sharing unit that provides suggestion tools for emotion sharing. The emotion sharing unit provides suggestion tools for emotion sharing. Furthermore, the next-generation AI agent system includes a health management unit that provides health management functions. The health management unit provides health management functions. Finally, the next-generation AI agent system includes a prediction support unit that provides prediction support functions. The prediction support unit provides prediction support functions. This will enable the next-generation AI agent system to increase the amount of time that all family members can spend together without stress. It will facilitate the smooth running of family daily life, promote communication among family members, and support the management of household tasks and scheduling.

[0069] The next-generation AI agent system according to this embodiment comprises a schedule sharing unit, a task management unit, a reminder unit, an emotion sharing unit, a health management unit, and a predictive support unit. The schedule sharing unit shares and adjusts the schedules of each family member. For example, the schedule sharing unit displays and shares the schedules of each family member in a calendar format. The schedule sharing unit can also adjust schedules to avoid overlaps in the schedules of each family member. For example, the schedule sharing unit updates and shares the schedules of each family member in real time. The schedule sharing unit can also notify each family member of changes in their schedule. The task management unit manages household tasks and tracks their progress. For example, the task management unit displays and manages household tasks in a list format. The task management unit can also update and track the progress of household tasks in real time. For example, the task management unit displays and manages the progress of household tasks in a graph format. The task management unit can also notify when household tasks are completed. The Reminder Unit provides reminder functionality. For example, the Reminder Unit notifies users of deadlines for household tasks. The Reminder Unit can also notify users of the progress of household tasks. For example, the Reminder Unit notifies users when the deadline for a household task is approaching. The Reminder Unit can also periodically notify users of the progress of household tasks. The Emotion Sharing Unit provides suggestion tools for sharing emotions. For example, the Emotion Sharing Unit makes suggestions for sharing the emotions of each family member. The Emotion Sharing Unit can also provide tools for sharing the emotions of each family member. For example, the Emotion Sharing Unit provides an application for sharing the emotions of each family member. The Emotion Sharing Unit can also provide a chat function for sharing the emotions of each family member. The Health Management Unit provides health management functionality. For example, the Health Management Unit manages the health status of each family member. The Health Management Unit can also monitor the health status of each family member. For example, the Health Management Unit displays and manages the health status of each family member in graph format.Furthermore, the health management unit can notify the family of changes in the health status of each family member. The predictive support unit provides predictive support functions. For example, the predictive support unit predicts and supports the schedules of each family member. The predictive support unit can also predict and support the progress of each family member's tasks. For example, the predictive support unit predicts and notifies the family of changes in their schedules. The predictive support unit can also predict and notify the family of changes in the progress of each family member's tasks. As a result, the next-generation AI agent system according to this embodiment can streamline the family's daily life, promote communication among family members, and support the management of household tasks and schedule adjustments.

[0070] The Schedule Sharing section allows family members to share and coordinate their schedules. For example, it displays and shares each family member's schedule in a calendar format. Specifically, the Schedule Sharing section provides a cloud-based calendar system for centralized management of each member's schedule. This calendar system allows each member to input their own schedule and share it with others. For example, if a family member makes a hospital appointment, that information is reflected in the calendars of other members in real time. This makes it easier for all family members to keep track of each other's schedules, reducing the hassle of scheduling conflicts and coordination. The Schedule Sharing section also has a function to send immediate notifications when there are changes to the schedule. For example, if there is a sudden change or cancellation of a schedule, that information is conveyed to all family members via push notification or email. Furthermore, the Schedule Sharing section can optimize schedules using AI. For example, it analyzes the schedules of all family members and suggests setting up family meetings and events at the optimal time. In this way, the Schedule Sharing section plays a role in streamlining family schedule management and facilitating smooth communication.

[0071] The Task Management Department manages household tasks and tracks their progress. For example, it displays and manages household tasks in a list format. Specifically, the Task Management Department provides a task management application accessible to all family members. This application allows each member to input their tasks and update their progress. For instance, it lists household tasks such as cleaning, shopping, and cooking, clearly indicating who is responsible for which task. The Task Management Department also updates and tracks task progress in real time. For example, when a task is completed, this information is immediately notified to other members. Furthermore, the Task Management Department includes a function to display task progress in a graph format, allowing all family members to grasp the progress at a glance. The Task Management Department also provides a function to set task priorities. For example, tasks can be categorized according to importance and deadline, allowing for work on high-priority tasks first. In this way, the Task Management Department streamlines household task management and supports all family members in collaborating to complete tasks.

[0072] The Reminders app provides reminder functionality. For example, it notifies users of deadlines for household tasks. Specifically, it manages the deadlines of tasks set by each member and sends notifications when the deadline approaches. For example, when the deadline for an important task, such as garbage collection day or payment deadline, is approaching, this information is conveyed to all family members via push notifications or email. The Reminders app can also notify users of the progress of household tasks. For example, if a task is behind schedule or has passed its deadline, it notifies users of this information to encourage completion. In this way, the Reminders app supports household task management and helps prevent tasks from being missed or delayed. Furthermore, the Reminders app also provides a function to set recurring reminders. For example, by sending regular reminders for tasks that need to be done regularly, such as weekly cleaning or monthly payments, all family members can complete the tasks without forgetting. In this way, the Reminders app streamlines household task management and supports all family members in cooperating to complete tasks.

[0073] The Emotion Sharing Department provides suggestion tools for sharing emotions. For example, it makes suggestions for sharing the emotions of each family member. Specifically, the Emotion Sharing Department provides an emotion sharing application that all family members can access. In this application, each member can input their emotions and share them with other members. For example, by recording daily events and feelings and sharing them with the whole family, it becomes easier to understand each other's emotions. The Emotion Sharing Department also has a function to suggest sharing emotions. For example, it sends notifications that encourage sharing emotions in response to specific events or occurrences. In this way, the Emotion Sharing Department promotes communication among family members and supports mutual understanding of each other's emotions. Furthermore, the Emotion Sharing Department also provides a chat function for sharing emotions. For example, emotions and events can be shared in real time through a group chat that all family members can participate in. In this way, the Emotion Sharing Department facilitates communication among family members and supports mutual understanding of each other's emotions.

[0074] The Health Management Department provides health management functions. For example, it manages the health status of each family member. Specifically, the Health Management Department provides an application that allows each member to input their own health data and share it with other members. This application can record health data such as weight, blood pressure, heart rate, and sleep duration, and display it in a graph format. The Health Management Department also has a function to notify users of changes in health status. For example, if an abnormality is detected in the health data, this information will be sent to all family members via push notification or email. In this way, the Health Management Department plays a role in enabling all family members to understand each other's health status and support health management. Furthermore, the Health Management Department also provides functions to give health advice and suggestions. For example, it analyzes health data and provides advice on exercise and diet. In this way, the Health Management Department supports all family members in leading healthy lives.

[0075] The Predictive Support Department provides predictive support functions. For example, it predicts and supports the schedules of each family member. Specifically, the Predictive Support Department uses AI to analyze each member's schedule and task progress, and predicts future plans and task progress. This AI can suggest optimal schedules and task progress based on past data and current circumstances. For example, it analyzes the schedules of all family members and suggests setting family meetings and events at the optimal time. It also analyzes the progress of tasks and notifies users to take action early if delays are expected. In this way, the Predictive Support Department supports all family members in efficiently managing their schedules and completing tasks. Furthermore, the Predictive Support Department also has the function of predicting future risks and problems and suggesting countermeasures to take in advance. For example, it analyzes health data, predicts future health risks, and suggests taking action early. In this way, the Predictive Support Department supports all family members in efficiently managing their schedules and leading healthy lives.

[0076] The schedule sharing unit can estimate the emotions of family members and adjust the schedule based on those emotions. For example, the schedule sharing unit can estimate the emotions of family members and reassign tasks to other members to alleviate the stress on members who are feeling stressed. It can also estimate the emotions of family members and prioritize assigning important tasks to members who are relaxed. Furthermore, the schedule sharing unit can estimate the emotions of family members and adjust the schedule of emotionally exhausted members to increase their rest time. This allows all family members to live stress-free by adjusting the schedule according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the schedule sharing unit may be performed using AI or not. For example, the schedule sharing unit can input family emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The schedule sharing unit can analyze the family's past schedule history and select the optimal schedule sharing method. For example, the schedule sharing unit can analyze the family's past schedule history and automatically add frequently occurring events to the schedule. It can also analyze the family's past schedule history and suggest the optimal schedule sharing method for specific days of the week or time slots. Furthermore, the schedule sharing unit can analyze the family's past schedule history and select the optimal sharing method to avoid schedule overlaps. In this way, the optimal schedule sharing method can be selected by analyzing past schedule history. Some or all of the above processing in the schedule sharing unit may be performed using AI, for example, or without AI. For example, the schedule sharing unit can input the family's past schedule data into a generating AI and have the generating AI select the optimal schedule sharing method.

[0078] The schedule sharing unit can automatically adjust the schedule based on each family member's priorities when the schedule is shared. For example, the schedule sharing unit considers each family member's priorities and prioritizes scheduling important tasks. It can also consider each family member's priorities and postpone lower-priority tasks. Furthermore, the schedule sharing unit can consider each family member's priorities and make adjustments to avoid schedule conflicts. This allows important tasks to be processed preferentially by adjusting the schedule based on family priorities. Some or all of the above processing in the schedule sharing unit may be performed using AI, for example, or not. For example, the schedule sharing unit can input family priority data into a generating AI and have the generating AI perform the automatic schedule adjustment.

[0079] The schedule sharing unit can estimate the emotions of family members and adjust the timing of schedule notifications based on the estimated emotions. For example, the schedule sharing unit can estimate the emotions of family members and delay notifications for members who are feeling stressed. It can also estimate the emotions of family members and send notifications earlier to members who are relaxed. Furthermore, it can estimate the emotions of family members and refrain from sending notifications to members who are emotionally exhausted. In this way, stress can be reduced by adjusting the notification timing according to the emotions of family members. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the schedule sharing unit may be performed using AI, or not using AI. For example, the schedule sharing unit can input family emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The schedule sharing unit can prioritize sharing highly relevant schedules by considering the geographical location information of family members when sharing schedules. For example, the schedule sharing unit can consider the geographical location information of family members and prioritize adding nearby events to the schedule. It can also consider the geographical location information of family members and postpone events that take place far away. Furthermore, the schedule sharing unit can consider the geographical location information of family members and suggest a schedule that minimizes travel time. In this way, highly relevant schedules can be shared preferentially by considering geographical location information. Some or all of the above processing in the schedule sharing unit may be performed using AI, for example, or not using AI. For example, the schedule sharing unit can input the geographical location information of family members into a generating AI and have the generating AI select highly relevant schedules.

[0081] The schedule sharing unit can analyze the family's social media activity and share relevant schedules when sharing schedules. For example, the schedule sharing unit can analyze the family's social media activity and add planned event attendance to the schedule. It can also analyze the family's social media activity and reflect plans with friends in the schedule. Furthermore, the schedule sharing unit can analyze the family's social media activity and suggest events of interest to the schedule. In this way, relevant schedules can be shared by analyzing social media activity. Some or all of the above processing in the schedule sharing unit may be performed using AI, for example, or not using AI. For example, the schedule sharing unit can input family social media data into a generating AI and have the generating AI select relevant schedules.

[0082] The task management unit can estimate the emotions of family members and adjust task priorities based on those estimates. For example, the task management unit can estimate the emotions of family members and reallocate tasks to other members to reduce the workload of members who are feeling stressed. It can also estimate the emotions of family members and prioritize assigning important tasks to members who are relaxed. Furthermore, the task management unit can estimate the emotions of family members and adjust tasks for emotionally exhausted members to increase their rest time. This reduces stress by adjusting task priorities according to the emotions of family members. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the task management unit may be performed using AI, for example, or not using AI. For example, the task management unit can input family emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The task management unit can analyze the family's past task history to select the optimal task management method during task management. For example, the task management unit can analyze the family's past task history and automatically add frequently performed tasks. It can also analyze the family's past task history and suggest the optimal task management method for specific days of the week or time slots. Furthermore, the task management unit can analyze the family's past task history and select the optimal management method to avoid task duplication. In this way, the optimal task management method can be selected by analyzing past task history. Some or all of the above processes in the task management unit may be performed using AI, for example, or without AI. For example, the task management unit can input the family's past task data into a generating AI and have the generating AI select the optimal task management method.

[0084] The task management unit can automatically assign tasks based on the skill sets of each family member during task management. For example, the task management unit considers the skill sets of each family member and assigns appropriate tasks. The task management unit can also consider the skill sets of each family member and adjust the assignment according to the difficulty of the task. Furthermore, the task management unit can consider the skill sets of each family member and make assignments that maximize task efficiency. This enables efficient task management by assigning tasks based on each member's skill set. Some or all of the above processes in the task management unit may be performed using AI, for example, or not using AI. For example, the task management unit can input family skill set data into a generating AI and have the generating AI perform automatic task assignment.

[0085] The task management unit can estimate the emotions of family members and adjust the timing of task progress notifications based on the estimated emotions. For example, the task management unit can estimate the emotions of family members and delay notifications for members who are feeling stressed. It can also estimate the emotions of family members and send notifications earlier to members who are relaxed. Furthermore, it can estimate the emotions of family members and refrain from sending notifications to members who are emotionally exhausted. This reduces stress by adjusting notification timing according to the emotions of family members. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the task management unit may be performed using AI, for example, or not using AI. For example, the task management unit can input family emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The task management unit can prioritize tasks based on their relevance, taking into account the geographical location information of family members. For example, the task management unit can prioritize tasks performed nearby, taking into account the geographical location information of family members. It can also postpone tasks performed far away, taking into account the geographical location information of family members. Furthermore, the task management unit can propose task management strategies that minimize travel time, taking into account the geographical location information of family members. In this way, by considering geographical location information, tasks based on relevance can be prioritized. Some or all of the above processes in the task management unit may be performed using AI, for example, or not. For example, the task management unit can input the geographical location information of family members into a generating AI and have the generating AI select tasks based on relevance.

[0087] The task management unit can analyze family members' social media activity and manage related tasks during task management. For example, the task management unit can analyze family members' social media activity and add planned event attendance as tasks. It can also analyze family members' social media activity and reflect plans with friends as tasks. Furthermore, the task management unit can analyze family members' social media activity and suggest events of interest as tasks. In this way, related tasks can be managed by analyzing social media activity. Some or all of the above processing in the task management unit may be performed using AI, for example, or not using AI. For example, the task management unit can input family social media data into a generating AI and have the generating AI select related tasks.

[0088] The reminder unit can estimate the emotions of family members and adjust the notification method of reminders based on the estimated emotions. For example, the reminder unit can estimate the emotions of family members and use a calm notification sound for members who are feeling stressed. It can also estimate the emotions of family members and use a cheerful notification sound for members who are relaxed. Furthermore, the reminder unit can estimate the emotions of family members and refrain from sending notifications to members who are emotionally exhausted. This reduces stress by adjusting the notification method according to the emotions of family members. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using AI or not using AI. For example, the reminder unit can input family emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The reminder function can adjust the level of detail of a reminder based on the importance of the task when setting a reminder. For example, if the task is highly important, the reminder function will set a detailed reminder. Conversely, if the task is less important, the reminder function can set a concise reminder. Furthermore, the reminder function can also adjust the frequency of reminder notifications according to the importance of the task. This allows important tasks to be notified preferentially by adjusting the level of detail of the reminder based on the importance of the task. Some or all of the above processing in the reminder function may be performed using AI, for example, or without AI. For example, the reminder function can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the reminder.

[0090] The reminder function can apply different reminder algorithms depending on the task category when setting a reminder. For example, if the task category is work, the reminder function can apply a business-oriented reminder algorithm. It can also apply a home-oriented reminder algorithm if the task category is home-related, and a hobby-related reminder algorithm if the task category is hobbies. This allows for appropriate notifications by applying the correct reminder algorithm according to the task category. Some or all of the above processing in the reminder function may be performed using AI, or without AI. For example, the reminder function can input task category data into a generating AI and have the generating AI apply the reminder algorithm.

[0091] The reminder unit can estimate the emotions of family members and adjust the timing of reminder notifications based on the estimated emotions. For example, the reminder unit can estimate the emotions of family members and delay notifications for members who are feeling stressed. It can also estimate the emotions of family members and send notifications earlier to members who are relaxed. Furthermore, it can estimate the emotions of family members and refrain from sending notifications to members who are emotionally exhausted. This reduces stress by adjusting notification timing according to the emotions of family members. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using AI or not using AI. For example, the reminder unit can input family emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] The reminder unit can determine the priority of reminders based on the task submission deadline when setting reminders. For example, the reminder unit will prioritize reminders when the task submission deadline is approaching. Conversely, the reminder unit can also lower the priority of reminders when the task submission deadline is far away. Furthermore, the reminder unit can adjust the frequency of reminder notifications according to the task submission deadline. This allows important tasks to be notified preferentially by determining the priority of reminders based on the task submission deadline. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input task submission deadline data into a generating AI and have the generating AI determine the priority of reminders.

[0093] The reminder unit can adjust the order of reminders based on the relevance of tasks when setting reminders. For example, the reminder unit can prioritize setting reminders for highly relevant tasks. It can also postpone less relevant tasks. Furthermore, the reminder unit can adjust the notification order of reminders according to the relevance of tasks. This allows for priority notification of highly relevant tasks by adjusting the order of reminders based on the relevance of tasks. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input task relevance data into a generating AI and have the generating AI perform the adjustment of the reminder order.

[0094] The emotion sharing unit can estimate the emotions of family members and adjust the method of suggesting emotion sharing based on the estimated emotions. For example, the emotion sharing unit can estimate the emotions of family members and refrain from suggesting emotion sharing to members who are feeling stressed. It can also estimate the emotions of family members and actively suggest emotion sharing to members who are relaxed. Furthermore, the emotion sharing unit can estimate the emotions of family members and delay suggesting emotion sharing to members who are emotionally exhausted. In this way, stress can be reduced by adjusting the method of suggesting emotion sharing according to the emotions of family members. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the emotion sharing unit may be performed using AI, for example, or without AI. For example, the emotion sharing unit can input family emotion data into a generative AI and have the generative AI perform emotion estimation.

[0095] The emotion sharing unit can analyze the family's past emotional history to select the optimal method of emotion sharing. For example, the emotion sharing unit can analyze the family's past emotional history and suggest regular emotion sharing to members whose emotions fluctuate little. It can also analyze the family's past emotional history and adjust the frequency of emotion sharing for members whose emotions fluctuate greatly. Furthermore, if the family's past emotional history indicates that emotional changes are related to a specific event, the emotion sharing unit can suggest emotion sharing tailored to that event. In this way, the optimal method of emotion sharing can be selected by analyzing past emotional history. Some or all of the above processing in the emotion sharing unit may be performed using AI, for example, or without AI. For example, the emotion sharing unit can input the family's past emotional data into a generating AI and have the generating AI select the optimal method of emotion sharing.

[0096] The emotion sharing unit can adjust the timing of emotion sharing based on the emotional changes of each family member. For example, the emotion sharing unit can monitor the emotional changes of family members in real time and suggest emotion sharing when emotions are stable. It can also monitor the emotional changes of family members in real time and refrain from emotion sharing when emotions are unstable. Furthermore, it can monitor the emotional changes of family members in real time and suggest emotion sharing when emotions are positive. By adjusting the timing of emotion sharing based on emotional changes, emotion sharing can be performed at the appropriate time. Some or all of the above processing in the emotion sharing unit may be performed using AI, for example, or without AI. For example, the emotion sharing unit can input family emotional data into a generating AI and have the generating AI perform an evaluation of emotional changes.

[0097] The emotion sharing unit can estimate the emotions of family members and adjust the timing of emotion sharing notifications based on the estimated emotions. For example, the emotion sharing unit can estimate the emotions of family members and delay notifications for members who are feeling stressed. It can also estimate the emotions of family members and send notifications earlier to members who are relaxed. Furthermore, it can estimate the emotions of family members and refrain from sending notifications to members who are emotionally exhausted. In this way, stress can be reduced by adjusting the notification timing according to the emotions of family members. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emotion sharing unit may be performed using AI, for example, or not using AI. For example, the emotion sharing unit can input family emotion data into a generative AI and have the generative AI perform emotion estimation.

[0098] The emotion sharing unit can prioritize highly relevant emotion sharing by considering the geographical location information of family members during emotion sharing. For example, the emotion sharing unit can prioritize emotion sharing with nearby members by considering the geographical location information of family members. It can also postpone emotion sharing with members who are far away by considering the geographical location information of family members. Furthermore, the emotion sharing unit can also propose emotion sharing that minimizes travel time by considering the geographical location information of family members. In this way, highly relevant emotion sharing can be prioritized by considering geographical location information. Some or all of the above processing in the emotion sharing unit may be performed using AI, for example, or without the use of AI. For example, the emotion sharing unit can input the geographical location information of family members into a generating AI and have the generating AI select highly relevant emotion sharing.

[0099] The emotion sharing unit can analyze the family's social media activity and share relevant emotions when sharing emotions. For example, the emotion sharing unit can analyze the family's social media activity and share emotions in line with planned event participation. It can also analyze the family's social media activity and share emotions based on interactions with friends. Furthermore, the emotion sharing unit can analyze the family's social media activity and share emotions related to events of interest. In this way, relevant emotions can be shared by analyzing social media activity. Some or all of the above processing in the emotion sharing unit may be performed using AI, for example, or without AI. For example, the emotion sharing unit can input family social media data into a generating AI and have the generating AI select relevant emotions.

[0100] The health management department can estimate the emotions of family members and adjust health management advice based on those estimated emotions. For example, the health management department can estimate the emotions of family members and provide advice on relaxation to members who are feeling stressed. It can also estimate the emotions of family members and suggest healthy activities to members who are relaxed. Furthermore, the health management department can estimate the emotions of family members and provide advice prioritizing rest to members who are emotionally exhausted. This reduces stress by adjusting health management advice according to the emotions of family members. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the health management department may be performed using AI or not using AI. For example, the health management department can input family emotion data into a generative AI and have the generative AI perform emotion estimation.

[0101] The Health Management Department can analyze a family's past health history to select the optimal health management method during health management. For example, the Health Management Department can analyze a family's past health history and propose frequently performed health management methods. Furthermore, the Health Management Department can analyze a family's past health history and select the optimal management method for specific health problems. In addition, the Health Management Department can analyze a family's past health history and propose management methods that adapt to changes in health status. This allows for the selection of the optimal health management method by analyzing past health history. Some or all of the above processes in the Health Management Department may be performed using AI, for example, or without AI. For example, the Health Management Department can input the family's past health data into a generating AI and have the generating AI select the optimal health management method.

[0102] The health management department can customize health management methods based on the health status of each family member during health management. For example, the health management department can consider the health status of each family member and propose appropriate health management methods. The health management department can also adjust the frequency of health management considering the health status of each family member. Furthermore, the health management department can customize the content of health management considering the health status of each family member. This allows for appropriate health management by customizing health management methods based on the health status of each member. Some or all of the above processes in the health management department may be performed using AI, for example, or without AI. For example, the health management department can input family health status data into a generating AI and have the generating AI perform the customization of health management methods.

[0103] The health management department can estimate the emotions of family members and determine the priority of health management based on those estimated emotions. For example, the health management department can estimate the emotions of family members and prioritize the health management of members who are feeling stressed. It can also estimate the emotions of family members and postpone the health management of members who are relaxed. Furthermore, it can estimate the emotions of family members and prioritize the health management of members who are emotionally exhausted. This reduces stress by prioritizing health management according to the emotions of family members. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the health management department may be performed using AI, for example, or not using AI. For example, the health management department can input family emotion data into a generative AI and have the generative AI perform emotion estimation.

[0104] The Health Management Department can select the optimal health management method when providing health management services, taking into account the geographical location information of the family. For example, the Health Management Department can prioritize suggesting nearby medical institutions, taking into account the family's geographical location information. It can also postpone suggesting distant medical institutions, taking into account the family's geographical location information. Furthermore, the Health Management Department can suggest health management methods that minimize travel time, taking into account the family's geographical location information. In this way, the optimal health management method can be selected by considering geographical location information. Some or all of the above processing in the Health Management Department may be performed using AI, for example, or without AI. For example, the Health Management Department can input the family's geographical location information into a generating AI and have the generating AI select the optimal health management method.

[0105] The Health Management Department can analyze a family's social media activity and propose health management strategies during health management. For example, the Health Management Department can analyze a family's social media activity and propose health management strategies based on health-related posts. It can also analyze a family's social media activity and propose strategies based on the health management methods of their friends. Furthermore, the Health Management Department can analyze a family's social media activity and propose health management methods of interest. This allows for the proposal of appropriate health management strategies through the analysis of social media activity. Some or all of the above processes in the Health Management Department may be performed using AI, for example, or without AI. For example, the Health Management Department can input family social media data into a generating AI and have the generating AI generate suggestions for health management strategies.

[0106] The predictive support unit can estimate the emotions of family members and adjust its predictive support methods based on the estimated emotions. For example, the predictive support unit can estimate the emotions of family members and provide predictive support to members who are feeling stressed, encouraging them to relax. It can also estimate the emotions of family members and provide predictive support suggesting positive activities to members who are relaxed. Furthermore, it can estimate the emotions of family members and provide predictive support prioritizing rest to members who are emotionally exhausted. In this way, stress can be reduced by adjusting the predictive support methods according to the emotions of family members. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the predictive support unit may be performed using AI, for example, or not using AI. For example, the predictive support unit can input family emotion data into a generative AI and have the generative AI perform emotion estimation.

[0107] The prediction support unit can analyze the family's past data to select the optimal prediction support method during prediction support. For example, the prediction support unit can analyze the family's past data and provide prediction support based on frequently occurring activities. The prediction support unit can also analyze the family's past data and select the optimal prediction support method for a specific event. Furthermore, the prediction support unit can analyze the family's past data and provide prediction support in response to changes in activities. In this way, the optimal prediction support method can be selected by analyzing past data. Some or all of the above processing in the prediction support unit may be performed using AI, for example, or without AI. For example, the prediction support unit can input the family's past data into a generating AI and have the generating AI select the optimal prediction support method.

[0108] The predictive support unit can improve the accuracy of predictive support based on the behavioral patterns of each family member during the prediction support process. For example, the predictive support unit can analyze the behavioral patterns of each family member and provide appropriate predictive support. The predictive support unit can also analyze the behavioral patterns of each family member and adjust the frequency of predictive support. Furthermore, the predictive support unit can analyze the behavioral patterns of each family member and customize the content of the predictive support. This allows for appropriate support by improving the accuracy of predictive support based on the behavioral patterns of each member. Some or all of the above-described processes in the predictive support unit may be performed using AI, for example, or without AI. For example, the predictive support unit can input family behavioral pattern data into a generating AI and have the generating AI perform the task of improving the accuracy of predictive support.

[0109] The predictive support unit can estimate the emotions of family members and adjust the timing of predictive support notifications based on the estimated emotions. For example, the predictive support unit can estimate the emotions of family members and delay notifications for members who are feeling stressed. It can also estimate the emotions of family members and send notifications earlier for members who are relaxed. Furthermore, it can estimate the emotions of family members and refrain from sending notifications for members who are emotionally exhausted. In this way, stress can be reduced by adjusting the notification timing according to the emotions of family members. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the predictive support unit may be performed using AI, for example, or not using AI. For example, the predictive support unit can input family emotion data into a generative AI and have the generative AI perform emotion estimation.

[0110] The prediction support unit can select the optimal prediction support method by considering the geographical location information of the family during prediction support. For example, the prediction support unit can consider the geographical location information of the family and provide prediction support based on activities taking place nearby. The prediction support unit can also consider the geographical location information of the family and postpone activities taking place far away. Furthermore, the prediction support unit can consider the geographical location information of the family and propose prediction support to minimize travel time. In this way, the optimal prediction support method can be selected by considering geographical location information. Some or all of the above processing in the prediction support unit may be performed using AI, for example, or without using AI. For example, the prediction support unit can input the geographical location information of the family into a generating AI and have the generating AI perform the selection of the optimal prediction support method.

[0111] The prediction support unit can analyze the family's social media activity and propose means of prediction support during prediction support. For example, the prediction support unit can analyze the family's social media activity and provide prediction support in line with planned event participation. It can also analyze the family's social media activity and provide prediction support based on interactions with friends. Furthermore, it can analyze the family's social media activity and provide prediction support related to events of interest. In this way, by analyzing social media activity, it can propose appropriate means of prediction support. Some or all of the above processing in the prediction support unit may be performed using AI, for example, or without AI. For example, the prediction support unit can input the family's social media data into a generating AI and have the generating AI execute the proposal of means of prediction support.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The next-generation AI agent system can suggest events and activities based on each family member's hobbies and interests. For example, the schedule sharing function can automatically add events related to each family member's hobbies to the calendar. It can also suggest weekend activities based on each family member's interests. Furthermore, it can suggest events that the whole family can enjoy, based on each family member's hobbies and interests. This allows for more enjoyable time for the whole family by adjusting schedules based on each family member's hobbies and interests.

[0114] The task management system can assign tasks based on each family member's skill set. For example, it can assign cooking-related tasks to members who are good at cooking, cleaning-related tasks to members who are good at cleaning, and repair-related tasks to members who are good at DIY. By assigning tasks based on each family member's skill set, efficient task management becomes possible.

[0115] The reminder function can adjust the timing of reminder notifications based on each family member's daily routine. For example, it can send early morning reminders to members who are early risers, and late-night reminders to members who are night owls. Furthermore, it can send daytime reminders to members who are active during the day. By adjusting the timing of reminder notifications based on the family's daily routine, more effective reminder notifications become possible.

[0116] The Health Management Department can provide health management advice based on the health status of each family member. For example, it can advise members who are not getting enough exercise to start exercising. It can also suggest balanced meals to members with poor eating habits. Furthermore, it can provide relaxation advice to members who are experiencing stress. In this way, by providing health management advice based on the health status of each family member, the department can support the health of the entire family.

[0117] The predictive support unit can improve the accuracy of its predictive support based on the behavioral patterns of each family member. For example, the predictive support unit analyzes the behavioral patterns of each family member and provides appropriate predictive support. It can also analyze the behavioral patterns of each family member and adjust the frequency of predictive support. Furthermore, the predictive support unit can analyze the behavioral patterns of each family member and customize the content of the predictive support. This allows for improved accuracy of predictive support based on each member's behavioral patterns, enabling more appropriate support.

[0118] The schedule sharing function can estimate family members' emotions and adjust schedules based on those estimates. For example, it can estimate family members' emotions and reassign tasks to other members to alleviate the stress of members who are feeling stressed. It can also estimate family members' emotions and prioritize assigning important tasks to members who are relaxed. Furthermore, it can estimate family members' emotions and adjust the schedules of emotionally exhausted members to increase their rest time. By adjusting schedules according to family members' emotions, everyone in the family can live without stress.

[0119] The task management system can estimate family members' emotions and adjust task priorities based on those estimates. For example, it can estimate family members' emotions and reallocate tasks to other members to reduce the workload of members who are feeling stressed. It can also estimate family members' emotions and prioritize assigning important tasks to members who are relaxed. Furthermore, it can estimate family members' emotions and adjust tasks for emotionally exhausted members to increase their rest time. This reduces stress by adjusting task priorities according to family members' emotions.

[0120] The reminder function can estimate the emotions of family members and adjust the notification method based on those estimates. For example, the reminder function can estimate a family member's emotions and use a calm notification sound for members who are feeling stressed. It can also estimate a family member's emotions and use a cheerful notification sound for members who are relaxed. Furthermore, the reminder function can estimate a family member's emotions and refrain from sending notifications to members who are emotionally exhausted. By adjusting the notification method according to the family member's emotions, stress can be reduced.

[0121] The emotion sharing unit can estimate the emotions of family members and adjust its approach to suggesting emotion sharing based on those estimates. For example, it can estimate the emotions of family members and refrain from suggesting emotion sharing to members who are feeling stressed. It can also estimate the emotions of family members and proactively suggest emotion sharing to members who are relaxed. Furthermore, it can estimate the emotions of family members and delay suggesting emotion sharing to members who are emotionally exhausted. By adjusting the approach to suggesting emotion sharing according to the emotions of family members, stress can be reduced.

[0122] The health management department can estimate the emotions of family members and adjust health management advice based on those estimates. For example, the health management department can estimate the emotions of family members and provide relaxation advice to members who are feeling stressed. It can also estimate the emotions of family members and suggest healthy activities to members who are relaxed. Furthermore, the health management department can estimate the emotions of family members and advise members who are emotionally exhausted to prioritize rest. This allows for stress reduction by adjusting health management advice according to the emotions of family members.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The schedule sharing section shares and coordinates the schedules of each family member. For example, the schedule sharing section displays and shares each family member's schedule in a calendar format. The schedule sharing section can also adjust schedules to avoid overlaps among family members. The schedule sharing section updates and shares each family member's schedule in real time and notifies them of schedule changes. Step 2: The task management unit manages household tasks and tracks their progress. For example, the task management unit displays and manages household tasks in a list format. It can also update and track the progress of household tasks in real time. The task management unit displays and manages the progress of household tasks in a graph format and notifies users when tasks are completed. Step 3: The reminder section provides reminder functionality. For example, the reminder section notifies users of deadlines for household tasks and provides updates on task progress. The reminder section notifies users when deadlines for household tasks are approaching and provides regular updates on task progress. Step 4: The Emotion Sharing Department provides suggestion tools for sharing emotions. For example, the Emotion Sharing Department makes suggestions for sharing the emotions of each family member and provides tools for sharing emotions. The Emotion Sharing Department provides applications and chat functions for sharing the emotions of each family member. Step 5: The Health Management Department provides health management functions. For example, the Health Management Department manages and monitors the health status of each family member. The Health Management Department displays and manages the health status of each family member in graph format and notifies of changes in health status. Step 6: The predictive support unit provides predictive support functions. For example, the predictive support unit predicts and supports the schedules of each family member. The predictive support unit can predict and support the progress of tasks for each family member. The predictive support unit predicts and notifies changes in each family member's schedule and predicts and notifies changes in task progress.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the schedule sharing unit, task management unit, reminder unit, emotion sharing unit, health management unit, and predictive support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the schedule sharing unit is implemented by the control unit 46A of the smart device 14, which displays and shares the schedules of each family member in calendar format. The task management unit is implemented by the specific processing unit 290 of the data processing unit 12, which manages household tasks and tracks their progress. The reminder unit is implemented by the control unit 46A of the smart device 14, which notifies the deadlines for household tasks. The emotion sharing unit is implemented by the specific processing unit 290 of the data processing unit 12, which makes suggestions for sharing the emotions of each family member. The health management unit is implemented by the control unit 46A of the smart device 14, which manages the health status of each family member. The predictive support unit is implemented by the specific processing unit 290 of the data processing unit 12, which predicts and supports the schedules of each family member. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the schedule sharing unit, task management unit, reminder unit, emotion sharing unit, health management unit, and predictive support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the schedule sharing unit is implemented by the control unit 46A of the smart glasses 214, which displays and shares the schedules of each family member in calendar format. The task management unit is implemented by the specific processing unit 290 of the data processing unit 12, which manages household tasks and tracks their progress. The reminder unit is implemented by the control unit 46A of the smart glasses 214, which notifies the deadlines for household tasks. The emotion sharing unit is implemented by the specific processing unit 290 of the data processing unit 12, which makes suggestions for sharing the emotions of each family member. The health management unit is implemented by the control unit 46A of the smart glasses 214, which manages the health status of each family member. The prediction support unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and predicts and supports the schedules of each family member. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the schedule sharing unit, task management unit, reminder unit, emotion sharing unit, health management unit, and predictive support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the schedule sharing unit is implemented by the control unit 46A of the headset terminal 314, which displays and shares the schedules of each family member in calendar format. The task management unit is implemented by the specific processing unit 290 of the data processing unit 12, which manages household tasks and tracks their progress. The reminder unit is implemented by the control unit 46A of the headset terminal 314, which notifies the deadlines for household tasks. The emotion sharing unit is implemented by the specific processing unit 290 of the data processing unit 12, which makes suggestions for sharing the emotions of each family member. The health management unit is implemented by the control unit 46A of the headset terminal 314, which manages the health status of each family member. The prediction support unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and predicts and supports the schedules of each family member. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the schedule sharing unit, task management unit, reminder unit, emotion sharing unit, health management unit, and predictive support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the schedule sharing unit is implemented by the control unit 46A of the robot 414, which displays and shares the schedules of each family member in calendar format. The task management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which manages household tasks and tracks their progress. The reminder unit is implemented by, for example, the control unit 46A of the robot 414, which notifies the deadlines for household tasks. The emotion sharing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which makes suggestions for sharing the emotions of each family member. The health management unit is implemented by, for example, the control unit 46A of the robot 414, which manages the health status of each family member. The predictive support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which predicts and supports the schedules of each family member. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0178] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] 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.

[0188] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] (Note 1) A schedule sharing department that shares and coordinates the schedules of each family member, Based on the schedule shared by the aforementioned schedule sharing unit, a task management unit manages household tasks and tracks their progress. A reminder unit provides a reminder function based on tasks managed by the aforementioned task management unit, Based on the reminders provided by the reminder unit, the emotion sharing unit provides a suggestion tool for sharing emotions, Based on the proposals provided by the aforementioned emotion sharing unit, the health management unit provides health management functions, The system includes a predictive support unit that provides a predictive support function based on health management information provided by the aforementioned health management unit. A system characterized by the following features. (Note 2) The aforementioned schedule sharing unit is It estimates the emotions of family members and adjusts the schedule based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned schedule sharing unit is Analyze the family's past schedule history to select the optimal schedule sharing method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned schedule sharing unit is When sharing schedules, the system automatically adjusts the schedule based on each family member's priorities. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned schedule sharing unit is It estimates the emotions of family members and adjusts the timing of scheduled notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned schedule sharing unit is When sharing schedules, prioritize sharing highly relevant schedules by considering the geographical location of family members. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned schedule sharing unit is When sharing schedules, analyze family members' social media activity and share relevant schedules. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned task management unit, The system estimates family members' emotions and adjusts task priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned task management unit, When managing tasks, analyze the family's past task history to select the optimal task management method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned task management unit, When managing tasks, automatically assign tasks based on the skill set of each family member. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned task management unit, It estimates the emotions of family members and adjusts the timing of task progress notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned task management unit, When managing tasks, prioritize tasks that are highly relevant by considering the geographical location of family members. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned task management unit, When managing tasks, analyze family members' social media activity and manage related tasks. The system described in Appendix 1, characterized by the features described herein. (Note 14) The reminder unit is, It estimates family members' emotions and adjusts how reminders are sent based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The reminder unit is, When setting a reminder, adjust the level of detail of the reminder based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 16) The reminder unit is, When setting reminders, different reminder algorithms are applied depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The reminder unit is, It estimates the emotions of family members and adjusts the timing of reminder notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The reminder unit is, When setting reminders, the priority of reminders is determined based on the task submission date. The system described in Appendix 1, characterized by the features described herein. (Note 19) The reminder unit is, When setting reminders, the order of reminders will be adjusted based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned emotion sharing unit is Estimate family members' emotions and adjust the suggested methods for sharing those emotions based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned emotion sharing unit is When sharing emotions, analyze the family's past emotional history to select the most suitable method of emotional sharing. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned emotion sharing unit is When sharing emotions, adjust the timing of sharing based on the emotional changes of each family member. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned emotion sharing unit is It estimates family members' emotions and adjusts the timing of emotion sharing notifications based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned emotion sharing unit is When sharing emotions, prioritize sharing emotions that are highly relevant, taking into account the geographical location of family members. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned emotion sharing unit is When sharing emotions, analyze family members' social media activity and share relevant emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned health management department, It estimates family members' emotions and adjusts health management advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned health management department, When managing health, analyze the family's past health history to select the most suitable health management method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned health management department, When managing health, customize the health management methods based on the health status of each family member. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned health management department, It estimates the emotions of family members and determines health management priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned health management department, When managing health, the optimal health management method is selected by considering the geographical location information of family members. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned health management department, When managing health, we analyze family members' social media activity to suggest health management strategies. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned prediction support unit, It estimates family members' emotions and adjusts predictive support methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned prediction support unit, When providing predictive support, we analyze the family's past data to select the most suitable predictive support method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned prediction support unit, When providing predictive support, we improve the accuracy of the support based on the behavioral patterns of each family member. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned prediction support unit, It estimates family members' emotions and adjusts the timing of predictive support notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned prediction support unit, When providing prediction support, the optimal prediction support method is selected by considering the geographical location information of the family. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned prediction support unit, During predictive support, we analyze the family's social media activity and propose methods for predictive support. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A schedule sharing department that shares and coordinates the schedules of each family member, Based on the schedule shared by the aforementioned schedule sharing unit, a task management unit manages household tasks and tracks their progress. A reminder unit provides a reminder function based on tasks managed by the aforementioned task management unit, Based on the reminders provided by the reminder unit, the emotion sharing unit provides a suggestion tool for sharing emotions, Based on the proposals provided by the aforementioned emotion sharing unit, the health management unit provides health management functions, The system includes a predictive support unit that provides a predictive support function based on health management information provided by the aforementioned health management unit. A system characterized by the following features.

2. The aforementioned schedule sharing unit is It estimates the emotions of family members and adjusts the schedule based on those estimated emotions. The system according to feature 1.

3. The aforementioned schedule sharing unit is Analyze the family's past schedule history to select the optimal schedule sharing method. The system according to feature 1.

4. The aforementioned schedule sharing unit is When sharing schedules, the system automatically adjusts the schedule based on each family member's priorities. The system according to feature 1.

5. The aforementioned schedule sharing unit is It estimates the emotions of family members and adjusts the timing of scheduled notifications based on those estimated emotions. The system according to feature 1.

6. The aforementioned schedule sharing unit is When sharing schedules, prioritize sharing highly relevant schedules by considering the geographical location of family members. The system according to feature 1.

7. The aforementioned schedule sharing unit is When sharing schedules, analyze family members' social media activity and share relevant schedules. The system according to feature 1.

8. The aforementioned task management unit, Estimate family members' emotions and adjust task priorities based on those estimated emotions. The system according to feature 1.

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