system

Abstract

The system according to this embodiment aims to optimize the distribution of household tasks, taking into account the family's schedule and physical condition, and to achieve efficient household management. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a task allocation unit, and an adjustment unit. The collection unit automatically imports each person's schedule in conjunction with the family's calendar. The analysis unit analyzes the importance of the schedules collected by the collection unit and reflects this in the prioritization of household chores. The generation unit automatically generates household chore tasks based on the results analyzed by the analysis unit. The task allocation unit optimizes the distribution of household chore tasks generated by the generation unit, taking into account the strengths and weaknesses of each family member, their schedules, and their physical condition. The adjustment unit readjusts household chore tasks in response to sudden schedule changes or illness.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to optimally distribute housework tasks considering the schedules and physical conditions of family members, and it is difficult to efficiently manage housework.

[0005] The system according to the embodiment aims to optimally distribute housework tasks considering the schedules and physical conditions of family members and realize efficient housework management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a task allocation unit, and an adjustment unit. The collection unit automatically imports each family member's schedule in conjunction with the family calendar. The analysis unit analyzes the importance of the schedules collected by the collection unit and reflects this in the prioritization of household chores. The generation unit automatically generates household chore tasks based on the results analyzed by the analysis unit. The task allocation unit optimizes the distribution of household chore tasks generated by the generation unit, taking into account the strengths and weaknesses of each family member, their schedules, and their physical condition. The adjustment unit readjusts household chore tasks in response to sudden schedule changes or illness. [Effects of the Invention]

[0007] The system according to this embodiment can optimally distribute household tasks while taking into account the family's schedule and physical condition, thereby achieving efficient household management. [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 signed storage is one or more non-volatile storage devices that store various programs and various parameters. 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 labeled 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 applicable to the communication I / F include wireless communication standards such as 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the 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 household chore optimization system according to an embodiment of the present invention is a tool that utilizes AI to optimize household chores. This household chore optimization system works in conjunction with the family calendar and automatically imports each person's schedule. The AI ​​analyzes the importance of the schedule and reflects this in the prioritization of household chores. For example, it adjusts the household chore schedule considering important meetings or children's school events. Next, it has an automatic household chore task generation function. It reflects not only daily household chore tasks but also additional tasks for special events (birthdays, trips, parties, etc.). For example, it automatically generates tasks necessary for special events such as preparing for a birthday party or packing for a trip. Furthermore, it has a personalized household chore division function. It optimizes the division of chores considering the strengths and weaknesses of family members, their schedules, and their physical condition. It also incorporates participation in chores according to the age of the children. For example, it assigns cooking to members who are good at cooking and cleaning to members who are good at cleaning. It also has a real-time adjustment function. The AI ​​readjusts in response to sudden schedule changes or illness. It monitors the progress of household chores and assigns help if there are delays. For example, if a sudden meeting comes up or someone becomes ill, it assigns tasks to other members. Furthermore, it includes an advice function for efficiency. It suggests how to efficiently combine multiple household chores. It also considers the optimization of energy use when scheduling. For example, doing laundry and cleaning at the same time saves time and energy. Finally, it has a notification and reminder function. It sends timely reminders via smartphone. It creates an environment for sharing task completion status and mutual support among family members. For example, when a task is completed, a notification is sent so that other members can check the status. In this way, the AI-powered household chore optimization system is a useful tool for working couples and families with children to reduce the burden of household chores and perform them efficiently. As a result, the household chore optimization system automatically incorporates family schedules, optimizes the prioritization of chores, and can respond to sudden changes.

[0029] The household chore optimization system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a task allocation unit, and an adjustment unit. The collection unit automatically imports each family member's schedule by linking with the family's calendar. The collection unit obtains calendar information using, for example, API integration. The collection unit can also periodically update calendar information using a scheduling function. The analysis unit analyzes the importance of the schedules collected by the collection unit and reflects this in the prioritization of household chores. The analysis unit evaluates importance based on, for example, urgency and impact. The analysis unit can also determine priorities by considering the family's needs. The generation unit automatically generates household chore tasks based on the results analyzed by the analysis unit. The generation unit generates, for example, daily household chore tasks. The generation unit can also generate additional tasks for special events (birthdays, trips, parties, etc.). The task allocation unit optimizes the allocation of household chore tasks generated by the generation unit, taking into account the strengths and weaknesses of family members, their schedules, physical condition, etc. The task allocation unit assigns cooking to members who are good at cooking, and cleaning to members who are good at cleaning. Furthermore, the task-sharing unit can incorporate participation in household chores according to the children's ages. The coordination unit readjusts household tasks in response to sudden schedule changes or illness. For example, if a sudden meeting comes up or someone becomes ill, the coordination unit assigns tasks to other members. The coordination unit can also monitor the progress of household chores and assign help if there are delays. As a result, the household chore optimization system according to this embodiment can automatically incorporate family schedules, optimize the prioritization of household chores, and respond to sudden changes.

[0030] The data collection unit integrates with family calendars and automatically imports each person's schedule. For example, the unit obtains calendar information using API integration. Specifically, it integrates with common calendar services such as Google Calendar and iCloud Calendar via APIs to obtain each member's schedule in real time. The data collection unit can also periodically update calendar information using a scheduling function. For example, it can retrieve all members' calendar information at once at 6 AM every morning to keep the day's schedule up-to-date. Furthermore, the data collection unit can collect not only calendar information but also notifications from smartphones and tablets used by family members. This ensures that any changes or additions to schedules are immediately reflected in the system. The data collection unit centrally manages this information and makes it accessible to other departments. For example, collected data is stored on a cloud server, allowing the analysis and generation departments to access it in real time. This enables the data collection unit to efficiently manage family schedules and improve the overall system performance.

[0031] The analytics department analyzes the importance of the schedules collected by the data collection department and reflects this in the prioritization of household chores. The analytics department evaluates importance based on factors such as urgency and impact. Specifically, it scores the importance of each schedule by considering factors such as the content, time of day, and number of people involved. For example, children's school events and important parent meetings are rated as highly important. The analytics department can also determine priorities by considering the needs of the family. For example, it may prioritize events that the whole family participates in or schedules that are particularly important to certain members. Furthermore, the analytics department can use historical data and statistical information to analyze long-term trends and patterns. For example, based on the history of past household tasks, it can predict the workload of household chores on specific days and times and propose an optimal schedule. The analytics department can also use AI to analyze data and perform anomaly detection and predictive analysis. This allows the analytics department to quickly and accurately analyze the collected data and optimize the prioritization of household chores.

[0032] The generation unit automatically generates household tasks based on the results analyzed by the analysis unit. For example, the generation unit generates daily household tasks. Specifically, it automatically generates basic household tasks such as cleaning, laundry, cooking, and taking out the trash, and assigns them to each member. The generation unit can also generate additional tasks for special events (birthdays, trips, parties, etc.). For example, it can automatically generate tasks related to specific events, such as preparing for a birthday party or creating a packing list for a trip, and incorporate them into the schedule. Furthermore, the generation unit can also generate tasks according to the season and weather. For example, it considers seasonal tasks such as snow removal and heating appliance maintenance in winter, and garden maintenance and air conditioner cleaning in summer. The generation unit efficiently generates these tasks so that all family members can complete household chores without difficulty. In this way, the generation unit can reduce the burden on families and achieve efficient household management through the automatic generation of household tasks.

[0033] The task distribution unit optimizes the distribution of household tasks generated by the generation unit, taking into account each family member's strengths and weaknesses, schedule, and physical condition. For example, the task distribution unit assigns cooking to members who are good at cooking, and cleaning to members who are good at cleaning. Specifically, it uses an algorithm that assigns the most suitable task based on each member's skills and past performance. The task distribution unit can also incorporate age-appropriate participation in household chores. For example, it assigns simple cleaning and tidying to elementary school children, and cooking and laundry to middle school and older children, depending on their age. Furthermore, the task distribution unit can adjust the workload by considering each member's physical condition and fatigue level. For example, it assigns lighter tasks to members who are feeling unwell, distributing the burden among other members. The task distribution unit updates this information in real time to maintain optimal task distribution. In this way, the task distribution unit enables all family members to complete household chores without undue burden, achieving efficient household management.

[0034] The coordination unit readjusts household tasks in response to sudden schedule changes or illness. For example, if a member has a sudden meeting or becomes ill, the coordination unit will assign tasks to other members. Specifically, it uses an algorithm to reassign tasks based on each member's latest schedule and health information. The coordination unit can also monitor the progress of household chores and assign help if there are delays. For example, if cleaning is not progressing as planned, it will request help from other members to help complete the task. Furthermore, the coordination unit can facilitate communication among all family members and share task progress and changes. For example, it can use a dedicated app or chat tool to share task progress and changes in real time, ensuring everyone is up-to-date. This allows the coordination unit to respond flexibly to sudden changes and achieve efficient management of household tasks.

[0035] The data collection unit can automatically import individual appointments by linking with family calendars. The data collection unit can obtain calendar information using, for example, API integration. For example, the data collection unit can link with Google Calendar or Outlook® Calendar to automatically import individual appointments. The data collection unit can also periodically update calendar information using a scheduling function. For example, the data collection unit can be set to update calendar information at midnight every day. Furthermore, the data collection unit allows family members to manually enter appointments. For example, the data collection unit provides an interface for entering appointments via a smartphone app. This enables the automatic import of appointments by linking with family calendars. Some or all of the above processes in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input calendar information into a generation AI and have the generation AI perform appointment acquisition.

[0036] The analysis unit can analyze the importance of the schedules collected by the collection unit and reflect this in the prioritization of household chores. The analysis unit can evaluate importance based on factors such as urgency and impact. For example, the analysis unit can assess the urgency of the schedule and prioritize those with high urgency. It can also assess the impact of the schedule and prioritize those with high impact. Furthermore, the analysis unit can determine priorities by considering the needs of the family. For example, the analysis unit can adjust priorities by considering the wishes and requests of family members. This allows the analysis of the importance of the collected schedules to be reflected in the prioritization of household chores. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the collected schedule data into a generating AI and have the generating AI perform the importance analysis.

[0037] The generation unit can reflect not only daily household tasks but also additional tasks for special events. For example, the generation unit generates daily household tasks. For example, the generation unit automatically generates tasks such as daily cleaning, laundry, and cooking. The generation unit can also generate additional tasks for special events (birthdays, trips, parties, etc.). For example, the generation unit automatically generates tasks necessary for special events such as preparing for a birthday party or packing for a trip. Furthermore, the generation unit can adjust tasks based on the schedules of family members. For example, the generation unit adjusts the task schedule to match the schedules of family members. This allows it to reflect additional tasks for special events. Some or all of the above processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can have a generation AI perform the generation of household tasks.

[0038] The task-sharing system can optimize the division of household chores by considering each family member's strengths and weaknesses, schedules, and physical condition. For example, it can assign cooking to a member who is good at cooking, and cleaning to a member who is good at cleaning. For example, it can prioritize assigning household chores that each family member is good at. The task-sharing system can also adjust the division of chores by considering each family member's schedule and physical condition. For example, it can adjust household tasks to match each family member's schedule. Furthermore, the task-sharing system can incorporate children's participation in household chores according to their age. For example, it can assign simple household tasks to children. This allows for the optimization of the division of chores by considering each family member's strengths and weaknesses, schedules, and physical condition. Some or all of the above processes in the task-sharing system may be performed using AI, for example, or not. For example, the task-sharing system can input data such as each family member's strengths and weaknesses, schedule, and physical condition into a generating AI, and have the generating AI perform the optimization of the division of chores.

[0039] The coordination unit can readjust household tasks in response to sudden schedule changes or illness. For example, if a sudden meeting comes up or someone becomes ill, the coordination unit will assign tasks to other members. For example, the coordination unit will readjust household tasks in response to sudden schedule changes. The coordination unit can also monitor the progress of household tasks and assign help if there are delays. For example, the coordination unit will monitor the progress of household tasks in real time and request help from other members if delays occur. This allows for the readjustment of household tasks in response to sudden schedule changes or illness. Some or all of the above processes in the coordination unit may be performed using AI, for example, or not using AI. For example, the coordination unit can have a generation AI perform the readjustment of household tasks.

[0040] The coordination unit can monitor the progress of household chores and assign help if there are delays. For example, the coordination unit can monitor the progress of household chores in real time and request help from other members if delays occur. For example, the coordination unit can monitor the progress of household chores and assign tasks to other members if delays occur. The coordination unit can also periodically check the progress of household chores and take preventative measures before delays occur. For example, the coordination unit can periodically check the progress of household chores and request help from other members before delays occur. This allows the coordination unit to monitor the progress of household chores and assign help if there are delays. Some or all of the above processes in the coordination unit may be performed using AI, for example, or not using AI. For example, the coordination unit can input household chore progress data into a generating AI and have the generating AI perform delay detection and help assignment.

[0041] The generation unit can suggest efficient combinations of multiple household chores. For example, it can suggest saving time and energy by doing laundry and cleaning simultaneously. For example, it can suggest cleaning while the washing machine is running to efficiently complete household chores. The generation unit can also suggest cooking and cleaning simultaneously. For example, it can suggest putting away cooking utensils while cooking to reduce cleanup time. Furthermore, the generation unit can also suggest optimizing the order of household chores. For example, it can suggest cleaning first and then doing laundry to efficiently complete household chores. In this way, it can suggest efficient combinations of multiple household chores. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on household chore tasks into a generation AI and have the generation AI execute suggestions for efficient combinations.

[0042] The generation unit can also perform scheduling while considering the optimization of energy use. For example, the generation unit can schedule household chores to be performed during times of low electricity consumption. For example, it can schedule laundry to be done during nighttime hours when electricity consumption is low. The generation unit can also schedule household chores while considering eco-friendly choices. For example, it can prioritize scheduling times when energy-efficient appliances are used. Furthermore, the generation unit can suggest optimizing energy use by performing multiple household chores simultaneously. For example, it can optimize energy use by performing laundry and cleaning at the same time. This allows for scheduling while considering the optimization of energy use. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input energy usage data into a generation AI and have the generation AI execute the optimal scheduling.

[0043] The coordination unit can send timely reminders via smartphone. For example, the coordination unit can send reminders for household tasks to a smartphone. For example, the coordination unit can send a reminder 10 minutes before the start time of a household task. The coordination unit can also send notifications on the smartphone regarding the progress of household tasks. For example, the coordination unit can send a notification when a household task is completed. Furthermore, the coordination unit can also provide a function to share task completion status among family members. For example, the coordination unit can send a notification to other family members when a family member completes a task. This allows for timely reminder notifications via smartphone. Some or all of the above processes in the coordination unit may be performed using AI, for example, or not using AI. For example, the coordination unit can have a generation AI perform the scheduling of reminder notifications.

[0044] The coordination unit can create an environment for sharing task completion status and mutual support among family members. For example, the coordination unit can send notifications to other members when a family member completes a task. For example, the coordination unit can send notifications in real time when a family member completes a task. The coordination unit can also provide a function for sharing task progress among family members. For example, the coordination unit can provide a platform where family members can check the progress of tasks. Furthermore, the coordination unit can provide a function for creating an environment for mutual support among family members. For example, the coordination unit can provide a function for family members to request help from other members. This creates an environment for sharing task completion status and mutual support among family members. Some or all of the above processes in the coordination unit may be performed using AI, for example, or not using AI. For example, the coordination unit can have a generating AI perform the task completion sharing and mutual support environment.

[0045] The data collection unit can analyze the past schedule history of family members and select the optimal data acquisition method. For example, the data collection unit can prioritize data acquisition methods that family members have frequently used in the past. For example, the data collection unit can analyze the past schedule history of family members and prioritize frequently used data acquisition methods. The data collection unit can also suggest the optimal data acquisition method for a specific time period based on the past schedule history of family members. For example, the data collection unit can suggest the optimal data acquisition method for a specific time period based on the past schedule history. Furthermore, the data collection unit can analyze the past schedule history of family members and select the most efficient data acquisition method. For example, the data collection unit can select the most efficient data acquisition method based on the past schedule history. This allows the data collection unit to analyze the past schedule history of family members and select the optimal data acquisition method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past schedule history data into a generating AI and have the generating AI select the optimal data acquisition method.

[0046] The data collection unit can filter schedules based on the current living situation and areas of interest of family members when acquiring them. For example, the data collection unit can acquire only relevant schedules based on the current living situation of family members (work, school, etc.). For example, the data collection unit considers the living situation of family members and filters for relevant schedules. The data collection unit can also acquire only relevant schedules based on the areas of interest of family members (hobbies, sports, etc.). For example, the data collection unit considers the areas of interest of family members and filters for relevant schedules. Furthermore, the data collection unit can combine the current living situation and areas of interest of family members to filter for the most suitable schedules. For example, the data collection unit filters for the most suitable schedules based on living situation and areas of interest. This allows filtering based on the current living situation and areas of interest of family members. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0047] The data collection unit can prioritize the acquisition of highly relevant schedules by considering the geographical location information of family members when acquiring schedules. For example, the data collection unit can prioritize the acquisition of schedules that are close to where the family member is currently located. For example, the data collection unit can prioritize the acquisition of schedules that are close to where the family member is currently located, considering the geographical location information of family members. Furthermore, the data collection unit can prioritize the acquisition of schedules related to places that family members frequently visit, considering the geographical location information of family members. For example, the data collection unit can prioritize the acquisition of schedules related to places that family members frequently visit, considering the geographical location information of family members. In addition, the data collection unit can prioritize the acquisition of the most relevant schedules based on the geographical location information of family members. For example, the data collection unit can prioritize the acquisition of schedules that are highly relevant based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI acquire highly relevant schedules.

[0048] The data collection unit can analyze the social media activity of family members when acquiring schedules and retrieve relevant schedules. For example, the data collection unit can prioritize retrieving events mentioned by family members on social media. For example, the data collection unit can analyze the social media activity of family members and prioritize the mentioned events. The data collection unit can also retrieve events of interest from the social media activity of family members. For example, the data collection unit can retrieve events of interest based on social media activity. Furthermore, the data collection unit can analyze the social media activity of family members and retrieve the most relevant schedules. For example, the data collection unit can retrieve highly relevant schedules based on social media activity. This allows for the analysis of family members' social media activity and the retrieval of relevant schedules. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI retrieve relevant schedules.

[0049] The analysis unit can optimize its analysis algorithm by referring to past importance data when analyzing the importance of a schedule. For example, the analysis unit can analyze the importance of a current schedule based on past importance data. For example, the analysis unit can analyze the importance of a current schedule by referring to past importance data. The analysis unit can also optimize its analysis algorithm by referring to past importance data. For example, the analysis unit optimizes its analysis algorithm based on past importance data. Furthermore, the analysis unit can select the most appropriate analysis algorithm based on past importance data. For example, the analysis unit selects an appropriate analysis algorithm based on past importance data. This allows the analysis algorithm to be optimized by referring to past importance data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past importance data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0050] The analysis unit can analyze the importance of appointments while considering the attribute information of family members. For example, the analysis unit can analyze the importance of appointments while considering the age and gender of family members. For example, the analysis unit can analyze the importance of appointments based on the age and gender of family members. The analysis unit can also analyze the importance of appointments while considering the occupation and hobbies of family members. For example, the analysis unit can analyze the importance of appointments based on the occupation and hobbies of family members. Furthermore, the analysis unit can also analyze the importance of appointments while considering the health status of family members. For example, the analysis unit can analyze the importance of appointments based on the health status of family members. This allows the analysis of appointment importance to take into account the attribute information of family members. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the attribute information of family members into a generating AI and have the generating AI perform the importance analysis.

[0051] The analysis unit can analyze the importance of appointments while considering the geographical distribution of family members. For example, the analysis unit can analyze the importance of appointments based on the current location of family members. For example, the analysis unit can analyze the importance of appointments based on the current location, taking into account the geographical distribution of family members. The analysis unit can also analyze the importance of appointments based on places that family members frequently visit. For example, the analysis unit can analyze the importance of appointments based on places that family members frequently visit, taking into account the geographical distribution of family members. Furthermore, the analysis unit can select the most appropriate analysis method based on the geographical distribution of family members. For example, the analysis unit can select an appropriate analysis method based on the geographical distribution. This allows the analysis of appointment importance to take into account the geographical distribution of family members. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input geographical distribution data into a generating AI and have the generating AI perform the importance analysis.

[0052] The analysis unit can improve the accuracy of its analysis by referring to relevant literature when analyzing the importance of a schedule. For example, the analysis unit can analyze the importance of a schedule by referring to relevant literature. For example, the analysis unit can analyze the importance of a schedule based on relevant literature. The analysis unit can also optimize its analysis algorithm based on relevant literature. For example, the analysis unit can optimize its analysis algorithm by referring to relevant literature. Furthermore, the analysis unit can select the most appropriate analysis method by referring to relevant literature. For example, the analysis unit selects an appropriate analysis method based on relevant literature. This allows the accuracy of the analysis to be improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0053] The generation unit can optimize its generation algorithm by referring to past task data when generating household tasks. For example, the generation unit generates current household tasks based on past task data. For example, the generation unit generates current household tasks by referring to past task data. The generation unit can also optimize its generation algorithm by referring to past task data. For example, the generation unit optimizes its generation algorithm based on past task data. Furthermore, the generation unit can select the most appropriate generation algorithm based on past task data. For example, the generation unit selects an appropriate generation algorithm based on past task data. This allows the generation algorithm to be optimized by referring to past task data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past task data into a generation AI and have the generation AI perform the optimization of the generation algorithm.

[0054] The generation unit can generate household tasks while considering the attribute information of family members. For example, the generation unit can generate household tasks considering the age and gender of family members. For example, the generation unit can generate household tasks based on the age and gender of family members. The generation unit can also generate household tasks considering the occupation and hobbies of family members. For example, the generation unit can generate household tasks based on the occupation and hobbies of family members. Furthermore, the generation unit can also generate household tasks considering the health status of family members. For example, the generation unit can generate household tasks based on the health status of family members. This allows for the generation of household tasks while considering the attribute information of family members. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the attribute information of family members into a generation AI and have the generation AI execute the generation of household tasks.

[0055] The generation unit can generate household tasks while considering the geographical distribution of family members. For example, the generation unit can generate household tasks based on the current location of family members. For example, the generation unit can consider the geographical distribution of family members and generate household tasks based on their current location. The generation unit can also generate household tasks based on places that family members frequently visit. For example, the generation unit can consider the geographical distribution of family members and generate household tasks based on places that family members frequently visit. Furthermore, the generation unit can select the most appropriate generation method based on the geographical distribution of family members. For example, the generation unit can select an appropriate generation method based on the geographical distribution. This allows the generation of household tasks to be generated while considering the geographical distribution of family members. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input geographical distribution data into a generation AI and have the generation AI perform the generation of household tasks.

[0056] The generation unit can improve the accuracy of its generation of household tasks by referring to relevant literature. For example, the generation unit generates household tasks by referring to relevant literature. For example, the generation unit generates household tasks based on relevant literature. The generation unit can also optimize its generation algorithm based on relevant literature. For example, the generation unit optimizes its generation algorithm by referring to relevant literature. Furthermore, the generation unit can select the most appropriate generation method by referring to relevant literature. For example, the generation unit selects an appropriate generation method based on relevant literature. This allows the generation unit to improve accuracy by referring to relevant literature. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input relevant literature data into a generation AI and have the generation AI perform the task of improving generation accuracy.

[0057] The task-sharing unit can optimize the task-sharing algorithm by referring to past task-sharing data when optimizing the division of household chores. For example, the task-sharing unit can optimize the current division of household chores based on past task-sharing data. For example, the task-sharing unit can optimize the current division of household chores by referring to past task-sharing data. The task-sharing unit can also optimize the task-sharing algorithm by referring to past task-sharing data. For example, the task-sharing unit can optimize the task-sharing algorithm based on past task-sharing data. Furthermore, the task-sharing unit can select the most appropriate task-sharing algorithm based on past task-sharing data. For example, the task-sharing unit can select an appropriate task-sharing algorithm based on past task-sharing data. This allows the task-sharing algorithm to be optimized by referring to past task-sharing data. Some or all of the above processes in the task-sharing unit may be performed using AI, for example, or without AI. For example, the task-sharing unit can input past task-sharing data into a generating AI and have the generating AI perform the optimization of the task-sharing algorithm.

[0058] The task-sharing unit can optimize household chore distribution by considering the attribute information of family members. For example, the task-sharing unit can optimize household chore distribution by considering the age and gender of family members. For example, the task-sharing unit can optimize household chore distribution based on the age and gender of family members. The task-sharing unit can also optimize household chore distribution by considering the occupation and hobbies of family members. For example, the task-sharing unit can optimize household chore distribution based on the occupation and hobbies of family members. Furthermore, the task-sharing unit can also optimize household chore distribution by considering the health status of family members. For example, the task-sharing unit can optimize household chore distribution based on the health status of family members. This allows for the optimization of household chore distribution by considering the attribute information of family members. Some or all of the above processing in the task-sharing unit may be performed using AI, for example, or without AI. For example, the task-sharing unit can input the attribute information of family members into a generating AI and have the generating AI perform the optimization of household chore distribution.

[0059] The task-sharing unit can optimize household chore distribution by considering the geographical distribution of family members. For example, the task-sharing unit can optimize chore distribution based on the current location of each family member. For example, the task-sharing unit can optimize chore distribution based on the geographical distribution of family members and their current location. The task-sharing unit can also optimize chore distribution based on places that family members frequently visit. For example, the task-sharing unit can optimize chore distribution based on the geographical distribution of family members and their frequently visited places. Furthermore, the task-sharing unit can select the most appropriate method of division of labor based on the geographical distribution of family members. For example, the task-sharing unit can select an appropriate method of division of labor based on geographical distribution. This allows for the optimization of chore distribution by considering the geographical distribution of family members. Some or all of the above processing in the task-sharing unit may be performed using AI, for example, or without AI. For example, the task-sharing unit can input geographical distribution data into a generating AI and have the generating AI perform the optimization of chore distribution.

[0060] The task-sharing unit can improve the accuracy of task sharing by referring to relevant literature when optimizing the distribution of household chores. For example, the task-sharing unit can optimize the distribution of household chores by referring to relevant literature. For example, the task-sharing unit optimizes the distribution of household chores based on relevant literature. The task-sharing unit can also optimize the task-sharing algorithm based on relevant literature. For example, the task-sharing unit optimizes the task-sharing algorithm by referring to relevant literature. Furthermore, the task-sharing unit can select the most appropriate method of task sharing by referring to relevant literature. For example, the task-sharing unit selects an appropriate method of task sharing based on relevant literature. This allows the accuracy of task sharing to be improved by referring to relevant literature. Some or all of the above processing in the task-sharing unit may be performed using AI, for example, or without using AI. For example, the task-sharing unit can input relevant literature data into a generating AI and have the generating AI perform the task-sharing accuracy improvement.

[0061] The adjustment unit can optimize the adjustment algorithm by referring to past adjustment data when readjusting household tasks. For example, the adjustment unit readjusts the current household tasks based on past adjustment data. For example, the adjustment unit readjusts the current household tasks by referring to past adjustment data. The adjustment unit can also optimize the adjustment algorithm by referring to past adjustment data. For example, the adjustment unit optimizes the adjustment algorithm based on past adjustment data. Furthermore, the adjustment unit can select the most appropriate adjustment algorithm based on past adjustment data. For example, the adjustment unit selects an appropriate adjustment algorithm based on past adjustment data. This allows the adjustment algorithm to be optimized by referring to past adjustment data. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input past adjustment data into a generating AI and have the generating AI perform the optimization of the adjustment algorithm.

[0062] The adjustment unit can readjust household tasks while considering the attribute information of family members. For example, the adjustment unit can readjust household tasks while considering the age and gender of family members. For example, the adjustment unit can readjust household tasks based on the age and gender of family members. The adjustment unit can also readjust household tasks while considering the occupation and hobbies of family members. For example, the adjustment unit can readjust household tasks based on the occupation and hobbies of family members. Furthermore, the adjustment unit can also readjust household tasks while considering the health status of family members. For example, the adjustment unit can readjust household tasks based on the health status of family members. This allows for the readjustment of household tasks while considering the attribute information of family members. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the attribute information of family members into a generating AI and have the generating AI perform the readjustment of household tasks.

[0063] The adjustment unit can readjust household tasks while considering the geographical distribution of family members. For example, the adjustment unit can readjust household tasks based on the current location of family members. For example, the adjustment unit can consider the geographical distribution of family members and readjust household tasks based on their current location. The adjustment unit can also readjust household tasks based on places that family members frequently visit. For example, the adjustment unit can consider the geographical distribution of family members and readjust household tasks based on places that family members frequently visit. Furthermore, the adjustment unit can select the most appropriate adjustment method based on the geographical distribution of family members. For example, the adjustment unit can select an appropriate adjustment method based on geographical distribution. This allows for the readjustment of household tasks while considering the geographical distribution of family members. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input geographical distribution data into a generating AI and have the generating AI perform the readjustment of household tasks.

[0064] The adjustment unit can improve the accuracy of adjustments by referring to relevant literature when readjusting household tasks. For example, the adjustment unit readjusts household tasks by referring to relevant literature. For example, the adjustment unit readjusts household tasks based on relevant literature. The adjustment unit can also optimize the adjustment algorithm based on relevant literature. For example, the adjustment unit optimizes the adjustment algorithm by referring to relevant literature. Furthermore, the adjustment unit can select the most appropriate adjustment method by referring to relevant literature. For example, the adjustment unit selects an appropriate adjustment method based on relevant literature. This allows the adjustment unit to improve the accuracy of adjustments by referring to relevant literature. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input relevant literature data into a generating AI and have the generating AI perform the adjustment accuracy improvement.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The household chore optimization system can also include a health management unit. This unit monitors the health status of family members and reflects this in the assignment of household chores. For example, if a family member is unwell, the health management unit assigns them a less burdensome task. It can also consider the activity levels of family members and assign exercise tasks to those who are not getting enough exercise. Furthermore, the health management unit can monitor the dietary habits of family members and generate meal preparation tasks that consider nutritional balance. This makes it possible to assign household chores while taking the health status of the family into account.

[0067] The household optimization system can also include an educational support unit. This unit monitors children's learning progress and generates learning tasks. For example, it manages children's school homework and projects and sends reminders at appropriate times. It can also analyze children's learning progress and generate additional learning tasks as needed. Furthermore, it can suggest learning resources and activities based on children's interests and strengths. This supports children's learning and optimizes the educational environment within the home.

[0068] The household optimization system can also be equipped with an energy management unit. This unit monitors household energy usage and suggests efficient energy use. For example, it can schedule household tasks during off-peak hours when electricity consumption is low. It can also recommend the use of energy-efficient appliances. Furthermore, it can analyze household energy usage data and provide advice for energy conservation. This optimizes household energy use and enables an environmentally friendly lifestyle.

[0069] The household optimization system can also be equipped with a safety management unit. The safety management unit monitors the safety situation within the home and ensures the safety of household tasks. For example, the safety management unit monitors the usage of appliances and sends alerts if abnormalities occur. The safety management unit can also assign appropriate tasks to ensure that children can perform household chores safely. Furthermore, the safety management unit can identify hazardous areas within the home and suggest solutions. This ensures safety within the home and allows household chores to be performed with peace of mind.

[0070] The household optimization system can also be equipped with an environmental monitoring unit. This unit monitors the environmental conditions within the home and reflects this in the household task schedule. For example, it can monitor room temperature and humidity and suggest cleaning or ventilation at appropriate times. It can also monitor air quality and recommend the use of an air purifier as needed. Furthermore, it can analyze the environmental data within the home and provide advice for maintaining a comfortable living environment. This enables the scheduling of household tasks to take into account the environmental conditions within the home.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The data collection unit integrates with the family's calendar and automatically imports each person's schedule. The data collection unit can obtain calendar information using, for example, API integration. The data collection unit can also periodically update calendar information using its scheduling function. Step 2: The analysis unit analyzes the importance of the schedules collected by the collection unit and reflects this in the prioritization of household chores. The analysis unit evaluates importance based on factors such as urgency and impact. The analysis unit can also determine priorities by considering the needs of the family. Step 3: The generation unit automatically generates household tasks based on the results analyzed by the analysis unit. For example, the generation unit generates daily household tasks. The generation unit can also generate additional tasks for special events (birthdays, trips, parties, etc.). Step 4: The task distribution unit optimizes the distribution of household tasks generated by the generation unit, taking into account each family member's strengths and weaknesses, schedule, health, etc. For example, the task distribution unit assigns cooking to members who are good at cooking, and cleaning to members who are good at cleaning. The task distribution unit can also incorporate participation in household chores according to the children's ages. Step 5: The coordination team readjusts household tasks in response to sudden schedule changes or illness. For example, if a sudden meeting comes up or someone becomes ill, the coordination team will assign tasks to other members. The coordination team can also monitor the progress of household tasks and assign help if there are delays.

[0073] (Example of form 2) The household chore optimization system according to an embodiment of the present invention is a tool that utilizes AI to optimize household chores. This household chore optimization system works in conjunction with the family calendar and automatically imports each person's schedule. The AI ​​analyzes the importance of the schedule and reflects this in the prioritization of household chores. For example, it adjusts the household chore schedule considering important meetings or children's school events. Next, it has an automatic household chore task generation function. It reflects not only daily household chore tasks but also additional tasks for special events (birthdays, trips, parties, etc.). For example, it automatically generates tasks necessary for special events such as preparing for a birthday party or packing for a trip. Furthermore, it has a personalized household chore division function. It optimizes the division of chores considering the strengths and weaknesses of family members, their schedules, and their physical condition. It also incorporates participation in chores according to the age of the children. For example, it assigns cooking to members who are good at cooking and cleaning to members who are good at cleaning. It also has a real-time adjustment function. The AI ​​readjusts in response to sudden schedule changes or illness. It monitors the progress of household chores and assigns help if there are delays. For example, if a sudden meeting comes up or someone becomes ill, it assigns tasks to other members. Furthermore, it includes an advice function for efficiency. It suggests how to efficiently combine multiple household chores. It also considers the optimization of energy use when scheduling. For example, doing laundry and cleaning at the same time saves time and energy. Finally, it has a notification and reminder function. It sends timely reminders via smartphone. It creates an environment for sharing task completion status and mutual support among family members. For example, when a task is completed, a notification is sent so that other members can check the status. In this way, the AI-powered household chore optimization system is a useful tool for working couples and families with children to reduce the burden of household chores and perform them efficiently. As a result, the household chore optimization system automatically incorporates family schedules, optimizes the prioritization of chores, and can respond to sudden changes.

[0074] The household chore optimization system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a task allocation unit, and an adjustment unit. The collection unit automatically imports each family member's schedule by linking with the family's calendar. The collection unit obtains calendar information using, for example, API integration. The collection unit can also periodically update calendar information using a scheduling function. The analysis unit analyzes the importance of the schedules collected by the collection unit and reflects this in the prioritization of household chores. The analysis unit evaluates importance based on, for example, urgency and impact. The analysis unit can also determine priorities by considering the family's needs. The generation unit automatically generates household chore tasks based on the results analyzed by the analysis unit. The generation unit generates, for example, daily household chore tasks. The generation unit can also generate additional tasks for special events (birthdays, trips, parties, etc.). The task allocation unit optimizes the allocation of household chore tasks generated by the generation unit, taking into account the strengths and weaknesses of family members, their schedules, physical condition, etc. The task allocation unit assigns cooking to members who are good at cooking, and cleaning to members who are good at cleaning. Furthermore, the task-sharing unit can incorporate participation in household chores according to the children's ages. The coordination unit readjusts household tasks in response to sudden schedule changes or illness. For example, if a sudden meeting comes up or someone becomes ill, the coordination unit assigns tasks to other members. The coordination unit can also monitor the progress of household chores and assign help if there are delays. As a result, the household chore optimization system according to this embodiment can automatically incorporate family schedules, optimize the prioritization of household chores, and respond to sudden changes.

[0075] The data collection unit integrates with family calendars and automatically imports each person's schedule. For example, the unit obtains calendar information using API integration. Specifically, it integrates with common calendar services such as Google Calendar and iCloud Calendar via APIs to obtain each member's schedule in real time. The data collection unit can also periodically update calendar information using scheduling functions. For example, it can retrieve all members' calendar information at once at 6 AM every morning to keep the day's schedule up-to-date. Furthermore, the data collection unit can collect not only calendar information but also notifications from smartphones and tablets used by family members. This ensures that any changes or additions to schedules are immediately reflected in the system. The data collection unit centrally manages this information and makes it accessible to other departments. For example, collected data is stored on a cloud server, allowing the analysis and generation departments to access it in real time. This enables the data collection unit to efficiently manage family schedules and improve the overall system performance.

[0076] The analytics department analyzes the importance of the schedules collected by the data collection department and reflects this in the prioritization of household chores. The analytics department evaluates importance based on factors such as urgency and impact. Specifically, it scores the importance of each schedule by considering factors such as the content, time of day, and number of people involved. For example, children's school events and important parent meetings are rated as highly important. The analytics department can also determine priorities by considering the needs of the family. For example, it may prioritize events that the whole family participates in or schedules that are particularly important to certain members. Furthermore, the analytics department can use historical data and statistical information to analyze long-term trends and patterns. For example, based on the history of past household tasks, it can predict the workload of household chores on specific days and times and propose an optimal schedule. The analytics department can also use AI to analyze data and perform anomaly detection and predictive analysis. This allows the analytics department to quickly and accurately analyze the collected data and optimize the prioritization of household chores.

[0077] The generation unit automatically generates household tasks based on the results analyzed by the analysis unit. For example, the generation unit generates daily household tasks. Specifically, it automatically generates basic household tasks such as cleaning, laundry, cooking, and taking out the trash, and assigns them to each member. The generation unit can also generate additional tasks for special events (birthdays, trips, parties, etc.). For example, it can automatically generate tasks related to specific events, such as preparing for a birthday party or creating a packing list for a trip, and incorporate them into the schedule. Furthermore, the generation unit can also generate tasks according to the season and weather. For example, it considers seasonal tasks such as snow removal and heating appliance maintenance in winter, and garden maintenance and air conditioner cleaning in summer. The generation unit efficiently generates these tasks so that all family members can complete household chores without difficulty. In this way, the generation unit can reduce the burden on families and achieve efficient household management through the automatic generation of household tasks.

[0078] The task distribution unit optimizes the distribution of household tasks generated by the generation unit, taking into account each family member's strengths and weaknesses, schedule, and physical condition. For example, the task distribution unit assigns cooking to members who are good at cooking, and cleaning to members who are good at cleaning. Specifically, it uses an algorithm that assigns the most suitable task based on each member's skills and past performance. The task distribution unit can also incorporate age-appropriate participation in household chores. For example, it assigns simple cleaning and tidying to elementary school children, and cooking and laundry to middle school and older children, depending on their age. Furthermore, the task distribution unit can adjust the workload by considering each member's physical condition and fatigue level. For example, it assigns lighter tasks to members who are feeling unwell, distributing the burden among other members. The task distribution unit updates this information in real time to maintain optimal task distribution. In this way, the task distribution unit enables all family members to complete household chores without undue burden, achieving efficient household management.

[0079] The coordination unit readjusts household tasks in response to sudden schedule changes or illness. For example, if a member has a sudden meeting or becomes ill, the coordination unit will assign tasks to other members. Specifically, it uses an algorithm to reassign tasks based on each member's latest schedule and health information. The coordination unit can also monitor the progress of household chores and assign help if there are delays. For example, if cleaning is not progressing as planned, it will request help from other members to help complete the task. Furthermore, the coordination unit can facilitate communication among all family members and share task progress and changes. For example, it can use a dedicated app or chat tool to share task progress and changes in real time, ensuring everyone is up-to-date. This allows the coordination unit to respond flexibly to sudden changes and achieve efficient management of household tasks.

[0080] The data collection unit can automatically import individual appointments by linking with family calendars. The data collection unit can obtain calendar information using, for example, API integration. For example, the data collection unit can link with Google Calendar or Outlook Calendar to automatically import individual appointments. The data collection unit can also periodically update calendar information using a scheduling function. For example, the data collection unit can be set to update calendar information at midnight every day. Furthermore, the data collection unit allows family members to manually enter appointments. For example, the data collection unit provides an interface for entering appointments via a smartphone app. This enables the automatic import of appointments by linking with family calendars. Some or all of the above processes in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input calendar information into a generation AI and have the generation AI perform appointment acquisition.

[0081] The analysis unit can analyze the importance of the schedules collected by the collection unit and reflect this in the prioritization of household chores. The analysis unit can evaluate importance based on factors such as urgency and impact. For example, the analysis unit can assess the urgency of the schedule and prioritize those with high urgency. It can also assess the impact of the schedule and prioritize those with high impact. Furthermore, the analysis unit can determine priorities by considering the needs of the family. For example, the analysis unit can adjust priorities by considering the wishes and requests of family members. This allows the analysis of the importance of the collected schedules to be reflected in the prioritization of household chores. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the collected schedule data into a generating AI and have the generating AI perform the importance analysis.

[0082] The generation unit can reflect not only daily household tasks but also additional tasks for special events. For example, the generation unit generates daily household tasks. For example, the generation unit automatically generates tasks such as daily cleaning, laundry, and cooking. The generation unit can also generate additional tasks for special events (birthdays, trips, parties, etc.). For example, the generation unit automatically generates tasks necessary for special events such as preparing for a birthday party or packing for a trip. Furthermore, the generation unit can adjust tasks based on the schedules of family members. For example, the generation unit adjusts the task schedule to match the schedules of family members. This allows it to reflect additional tasks for special events. Some or all of the above processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can have a generation AI perform the generation of household tasks.

[0083] The task-sharing system can optimize the division of household chores by considering each family member's strengths and weaknesses, schedules, and physical condition. For example, it can assign cooking to a member who is good at cooking, and cleaning to a member who is good at cleaning. For example, it can prioritize assigning household chores that each family member is good at. The task-sharing system can also adjust the division of chores by considering each family member's schedule and physical condition. For example, it can adjust household tasks to match each family member's schedule. Furthermore, the task-sharing system can incorporate children's participation in household chores according to their age. For example, it can assign simple household tasks to children. This allows for the optimization of the division of chores by considering each family member's strengths and weaknesses, schedules, and physical condition. Some or all of the above processes in the task-sharing system may be performed using AI, for example, or not. For example, the task-sharing system can input data such as each family member's strengths and weaknesses, schedule, and physical condition into a generating AI, and have the generating AI perform the optimization of the division of chores.

[0084] The coordination unit can readjust household tasks in response to sudden schedule changes or illness. For example, if a sudden meeting comes up or someone becomes ill, the coordination unit will assign tasks to other members. For example, the coordination unit will readjust household tasks in response to sudden schedule changes. The coordination unit can also monitor the progress of household tasks and assign help if there are delays. For example, the coordination unit will monitor the progress of household tasks in real time and request help from other members if delays occur. This allows for the readjustment of household tasks in response to sudden schedule changes or illness. Some or all of the above processes in the coordination unit may be performed using AI, for example, or not using AI. For example, the coordination unit can have a generation AI perform the readjustment of household tasks.

[0085] The coordination unit can monitor the progress of household chores and assign help if there are delays. For example, the coordination unit can monitor the progress of household chores in real time and request help from other members if delays occur. For example, the coordination unit can monitor the progress of household chores and assign tasks to other members if delays occur. The coordination unit can also periodically check the progress of household chores and take preventative measures before delays occur. For example, the coordination unit can periodically check the progress of household chores and request help from other members before delays occur. This allows the coordination unit to monitor the progress of household chores and assign help if there are delays. Some or all of the above processes in the coordination unit may be performed using AI, for example, or not using AI. For example, the coordination unit can input household chore progress data into a generating AI and have the generating AI perform delay detection and help assignment.

[0086] The generation unit can suggest efficient combinations of multiple household chores. For example, it can suggest saving time and energy by doing laundry and cleaning simultaneously. For example, it can suggest cleaning while the washing machine is running to efficiently complete household chores. The generation unit can also suggest cooking and cleaning simultaneously. For example, it can suggest putting away cooking utensils while cooking to reduce cleanup time. Furthermore, the generation unit can also suggest optimizing the order of household chores. For example, it can suggest cleaning first and then doing laundry to efficiently complete household chores. In this way, it can suggest efficient combinations of multiple household chores. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on household chore tasks into a generation AI and have the generation AI execute suggestions for efficient combinations.

[0087] The generation unit can also perform scheduling while considering the optimization of energy use. For example, the generation unit can schedule household chores to be performed during times of low electricity consumption. For example, it can schedule laundry to be done during nighttime hours when electricity consumption is low. The generation unit can also schedule household chores while considering eco-friendly choices. For example, it can prioritize scheduling times when energy-efficient appliances are used. Furthermore, the generation unit can suggest optimizing energy use by performing multiple household chores simultaneously. For example, it can optimize energy use by performing laundry and cleaning at the same time. This allows for scheduling while considering the optimization of energy use. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input energy usage data into a generation AI and have the generation AI execute the optimal scheduling.

[0088] The coordination unit can send timely reminders via smartphone. For example, the coordination unit can send reminders for household tasks to a smartphone. For example, the coordination unit can send a reminder 10 minutes before the start time of a household task. The coordination unit can also send notifications on the smartphone regarding the progress of household tasks. For example, the coordination unit can send a notification when a household task is completed. Furthermore, the coordination unit can also provide a function to share task completion status among family members. For example, the coordination unit can send a notification to other family members when a family member completes a task. This allows for timely reminder notifications via smartphone. Some or all of the above processes in the coordination unit may be performed using AI, for example, or not using AI. For example, the coordination unit can have a generation AI perform the scheduling of reminder notifications.

[0089] The coordination unit can create an environment for sharing task completion status and mutual support among family members. For example, the coordination unit can send notifications to other members when a family member completes a task. For example, the coordination unit can send notifications in real time when a family member completes a task. The coordination unit can also provide a function for sharing task progress among family members. For example, the coordination unit can provide a platform where family members can check the progress of tasks. Furthermore, the coordination unit can provide a function for creating an environment for mutual support among family members. For example, the coordination unit can provide a function for family members to request help from other members. This creates an environment for sharing task completion status and mutual support among family members. Some or all of the above processes in the coordination unit may be performed using AI, for example, or not using AI. For example, the coordination unit can have a generating AI perform the task completion sharing and mutual support environment.

[0090] The data collection unit can estimate the user's emotions and adjust the timing of schedule acquisition based on the estimated emotions. For example, if the user is feeling stressed, the data collection unit can delay schedule acquisition to allow time for relaxation. For example, the data collection unit estimates the user's emotions and delays schedule acquisition if the user is feeling stressed. The data collection unit can also accelerate schedule acquisition if the user is relaxed to create a more efficient schedule. For example, the data collection unit estimates the user's emotions and accelerates schedule acquisition if the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can quickly acquire the schedule and immediately adjust the schedule. For example, the data collection unit estimates the user's emotions and quickly acquires the schedule if the user is in a hurry. This allows the timing of schedule acquisition to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI adjust the timing of acquiring appointments based on emotion.

[0091] The data collection unit can analyze the past schedule history of family members and select the optimal data acquisition method. For example, the data collection unit can prioritize data acquisition methods that family members have frequently used in the past. For example, the data collection unit can analyze the past schedule history of family members and prioritize frequently used data acquisition methods. The data collection unit can also suggest the optimal data acquisition method for a specific time period based on the past schedule history of family members. For example, the data collection unit can suggest the optimal data acquisition method for a specific time period based on the past schedule history. Furthermore, the data collection unit can analyze the past schedule history of family members and select the most efficient data acquisition method. For example, the data collection unit can select the most efficient data acquisition method based on the past schedule history. This allows the data collection unit to analyze the past schedule history of family members and select the optimal data acquisition method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past schedule history data into a generating AI and have the generating AI select the optimal data acquisition method.

[0092] The data collection unit can filter schedules based on the current living situation and areas of interest of family members when acquiring them. For example, the data collection unit can acquire only relevant schedules based on the current living situation of family members (work, school, etc.). For example, the data collection unit considers the living situation of family members and filters for relevant schedules. The data collection unit can also acquire only relevant schedules based on the areas of interest of family members (hobbies, sports, etc.). For example, the data collection unit considers the areas of interest of family members and filters for relevant schedules. Furthermore, the data collection unit can combine the current living situation and areas of interest of family members to filter for the most suitable schedules. For example, the data collection unit filters for the most suitable schedules based on living situation and areas of interest. This allows filtering based on the current living situation and areas of interest of family members. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0093] The data collection unit can estimate the user's emotions and determine the priority of appointments to retrieve based on the estimated emotions. For example, if the user is feeling stressed, the data collection unit will prioritize retrieving relaxing appointments. For example, the data collection unit estimates the user's emotions and prioritizes relaxing appointments if the user is feeling stressed. The data collection unit can also prioritize retrieving important appointments if the user is relaxed. For example, the data collection unit estimates the user's emotions and prioritizes important appointments if the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can prioritize retrieving appointments that require immediate attention. For example, the data collection unit estimates the user's emotions and prioritizes appointments that require immediate attention if the user is in a hurry. This allows the system to determine the priority of appointments to retrieve based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, which can then perform the task of prioritizing appointments based on those emotions.

[0094] The data collection unit can prioritize the acquisition of highly relevant schedules by considering the geographical location information of family members when acquiring schedules. For example, the data collection unit can prioritize the acquisition of schedules that are close to where the family member is currently located. For example, the data collection unit can prioritize the acquisition of schedules that are close to where the family member is currently located, considering the geographical location information of family members. Furthermore, the data collection unit can prioritize the acquisition of schedules related to places that family members frequently visit, considering the geographical location information of family members. For example, the data collection unit can prioritize the acquisition of schedules related to places that family members frequently visit, considering the geographical location information of family members. In addition, the data collection unit can prioritize the acquisition of the most relevant schedules based on the geographical location information of family members. For example, the data collection unit can prioritize the acquisition of schedules that are highly relevant based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI acquire highly relevant schedules.

[0095] The data collection unit can analyze the social media activity of family members when acquiring schedules and retrieve relevant schedules. For example, the data collection unit can prioritize retrieving events mentioned by family members on social media. For example, the data collection unit can analyze the social media activity of family members and prioritize the mentioned events. The data collection unit can also retrieve events of interest from the social media activity of family members. For example, the data collection unit can retrieve events of interest based on social media activity. Furthermore, the data collection unit can analyze the social media activity of family members and retrieve the most relevant schedules. For example, the data collection unit can retrieve highly relevant schedules based on social media activity. This allows for the analysis of family members' social media activity and the retrieval of relevant schedules. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI retrieve relevant schedules.

[0096] The analysis unit can estimate the user's emotions and adjust the method of analyzing the importance of appointments based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can set the importance of relaxing appointments to a higher level. For example, if the user is feeling stressed, the analysis unit can set the importance of relaxing appointments to a higher level. The analysis unit can also set the importance of important appointments to a higher level if the user is relaxed. For example, if the user is feeling stressed, the analysis unit can set the importance of important appointments to a higher level. Furthermore, if the user is in a hurry, the analysis unit can also set the importance of appointments requiring immediate attention to a higher level. For example, if the user is feeling stressed, the analysis unit can set the importance of appointments requiring immediate attention to a higher level. This allows the analysis method of analyzing the importance of appointments to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI adjust the importance analysis method based on emotion.

[0097] The analysis unit can optimize its analysis algorithm by referring to past importance data when analyzing the importance of a schedule. For example, the analysis unit can analyze the importance of a current schedule based on past importance data. For example, the analysis unit can analyze the importance of a current schedule by referring to past importance data. The analysis unit can also optimize its analysis algorithm by referring to past importance data. For example, the analysis unit optimizes its analysis algorithm based on past importance data. Furthermore, the analysis unit can select the most appropriate analysis algorithm based on past importance data. For example, the analysis unit selects an appropriate analysis algorithm based on past importance data. This allows the analysis algorithm to be optimized by referring to past importance data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past importance data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0098] The analysis unit can analyze the importance of appointments while considering the attribute information of family members. For example, the analysis unit can analyze the importance of appointments while considering the age and gender of family members. For example, the analysis unit can analyze the importance of appointments based on the age and gender of family members. The analysis unit can also analyze the importance of appointments while considering the occupation and hobbies of family members. For example, the analysis unit can analyze the importance of appointments based on the occupation and hobbies of family members. Furthermore, the analysis unit can also analyze the importance of appointments while considering the health status of family members. For example, the analysis unit can analyze the importance of appointments based on the health status of family members. This allows the analysis of appointment importance to take into account the attribute information of family members. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the attribute information of family members into a generating AI and have the generating AI perform the importance analysis.

[0099] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and easy-to-read display method. For example, if the user is nervous, the analysis unit can provide a simple and easy-to-read display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. For example, if the analysis unit estimates the user's emotions and provides a display method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets to the point. For example, if the analysis unit estimates the user's emotions and provides a display method that gets to the point if the user is in a hurry. This allows the display method of the analysis results to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the AI ​​adjust the display method based on that emotion.

[0100] The analysis unit can analyze the importance of appointments while considering the geographical distribution of family members. For example, the analysis unit can analyze the importance of appointments based on the current location of family members. For example, the analysis unit can analyze the importance of appointments based on the current location, taking into account the geographical distribution of family members. The analysis unit can also analyze the importance of appointments based on places that family members frequently visit. For example, the analysis unit can analyze the importance of appointments based on places that family members frequently visit, taking into account the geographical distribution of family members. Furthermore, the analysis unit can select the most appropriate analysis method based on the geographical distribution of family members. For example, the analysis unit can select an appropriate analysis method based on the geographical distribution. This allows the analysis of appointment importance to take into account the geographical distribution of family members. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input geographical distribution data into a generating AI and have the generating AI perform the importance analysis.

[0101] The analysis unit can improve the accuracy of its analysis by referring to relevant literature when analyzing the importance of a schedule. For example, the analysis unit can analyze the importance of a schedule by referring to relevant literature. For example, the analysis unit can analyze the importance of a schedule based on relevant literature. The analysis unit can also optimize its analysis algorithm based on relevant literature. For example, the analysis unit can optimize its analysis algorithm by referring to relevant literature. Furthermore, the analysis unit can select the most appropriate analysis method by referring to relevant literature. For example, the analysis unit selects an appropriate analysis method based on relevant literature. This allows the accuracy of the analysis to be improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0102] The generation unit can estimate the user's emotions and adjust the method of generating household tasks based on the estimated emotions. For example, if the user is feeling stressed, the generation unit will prioritize generating relaxing household tasks. For example, the generation unit will estimate the user's emotions and prioritize relaxing household tasks if the user is feeling stressed. The generation unit can also prioritize generating important household tasks if the user is relaxed. For example, the generation unit will estimate the user's emotions and prioritize important household tasks if the user is relaxed. Furthermore, if the user is in a hurry, the generation unit can prioritize generating household tasks that require immediate attention. For example, the generation unit will estimate the user's emotions and prioritize household tasks that require immediate attention if the user is in a hurry. This allows the method of generating household tasks to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the method of generating household tasks based on emotions.

[0103] The generation unit can optimize its generation algorithm by referring to past task data when generating household tasks. For example, the generation unit generates current household tasks based on past task data. For example, the generation unit generates current household tasks by referring to past task data. The generation unit can also optimize its generation algorithm by referring to past task data. For example, the generation unit optimizes its generation algorithm based on past task data. Furthermore, the generation unit can select the most appropriate generation algorithm based on past task data. For example, the generation unit selects an appropriate generation algorithm based on past task data. This allows the generation algorithm to be optimized by referring to past task data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past task data into a generation AI and have the generation AI perform the optimization of the generation algorithm.

[0104] The generation unit can generate household tasks while considering the attribute information of family members. For example, the generation unit can generate household tasks considering the age and gender of family members. For example, the generation unit can generate household tasks based on the age and gender of family members. The generation unit can also generate household tasks considering the occupation and hobbies of family members. For example, the generation unit can generate household tasks based on the occupation and hobbies of family members. Furthermore, the generation unit can also generate household tasks considering the health status of family members. For example, the generation unit can generate household tasks based on the health status of family members. This allows for the generation of household tasks while considering the attribute information of family members. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the attribute information of family members into a generation AI and have the generation AI execute the generation of household tasks.

[0105] The generation unit can estimate the user's emotions and adjust the display method of the generated household tasks based on the estimated user emotions. For example, if the user is stressed, the generation unit can provide a simple and easy-to-read display method. For example, if the user is stressed, the generation unit can estimate the user's emotions and provide a simple and easy-to-read display method. The generation unit can also provide a display method that includes detailed information if the user is relaxed. For example, if the generation unit estimates the user's emotions and provides a display method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the generation unit can provide a display method that gets to the point. For example, if the generation unit estimates the user's emotions and provides a display method that gets to the point if the user is in a hurry. This allows the display method of the generated household tasks to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform adjustments to the display method based on emotion.

[0106] The generation unit can generate household tasks while considering the geographical distribution of family members. For example, the generation unit can generate household tasks based on the current location of family members. For example, the generation unit can consider the geographical distribution of family members and generate household tasks based on their current location. The generation unit can also generate household tasks based on places that family members frequently visit. For example, the generation unit can consider the geographical distribution of family members and generate household tasks based on places that family members frequently visit. Furthermore, the generation unit can select the most appropriate generation method based on the geographical distribution of family members. For example, the generation unit can select an appropriate generation method based on the geographical distribution. This allows the generation of household tasks to be generated while considering the geographical distribution of family members. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input geographical distribution data into a generation AI and have the generation AI perform the generation of household tasks.

[0107] The generation unit can improve the accuracy of its generation of household tasks by referring to relevant literature. For example, the generation unit generates household tasks by referring to relevant literature. For example, the generation unit generates household tasks based on relevant literature. The generation unit can also optimize its generation algorithm based on relevant literature. For example, the generation unit optimizes its generation algorithm by referring to relevant literature. Furthermore, the generation unit can select the most appropriate generation method by referring to relevant literature. For example, the generation unit selects an appropriate generation method based on relevant literature. This allows the generation unit to improve accuracy by referring to relevant literature. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input relevant literature data into a generation AI and have the generation AI perform the task of improving generation accuracy.

[0108] The task-sharing unit can estimate the user's emotions and adjust the method of dividing household chores based on the estimated emotions. For example, if the user is feeling stressed, the task-sharing unit will prioritize assigning relaxing chores. For example, the task-sharing unit will estimate the user's emotions and prioritize relaxing chores if the user is feeling stressed. The task-sharing unit can also prioritize assigning important chores if the user is relaxed. For example, the task-sharing unit will estimate the user's emotions and prioritize important chores if the user is relaxed. Furthermore, if the user is in a hurry, the task-sharing unit can also prioritize assigning chores that require immediate attention. For example, the task-sharing unit will estimate the user's emotions and prioritize chores that require immediate attention if the user is in a hurry. In this way, the method of dividing household chores can be adjusted based on the user's emotions. 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-described processes in the task-sharing unit may be performed using AI, for example, or without AI. For example, the task-sharing unit can input user emotion data into a generating AI and have the generating AI adjust the method of household chore sharing based on those emotions.

[0109] The task-sharing unit can optimize the task-sharing algorithm by referring to past task-sharing data when optimizing the division of household chores. For example, the task-sharing unit can optimize the current division of household chores based on past task-sharing data. For example, the task-sharing unit can optimize the current division of household chores by referring to past task-sharing data. The task-sharing unit can also optimize the task-sharing algorithm by referring to past task-sharing data. For example, the task-sharing unit can optimize the task-sharing algorithm based on past task-sharing data. Furthermore, the task-sharing unit can select the most appropriate task-sharing algorithm based on past task-sharing data. For example, the task-sharing unit can select an appropriate task-sharing algorithm based on past task-sharing data. This allows the task-sharing algorithm to be optimized by referring to past task-sharing data. Some or all of the above processes in the task-sharing unit may be performed using AI, for example, or without AI. For example, the task-sharing unit can input past task-sharing data into a generating AI and have the generating AI perform the optimization of the task-sharing algorithm.

[0110] The task-sharing unit can optimize household chore distribution by considering the attribute information of family members. For example, the task-sharing unit can optimize household chore distribution by considering the age and gender of family members. For example, the task-sharing unit can optimize household chore distribution based on the age and gender of family members. The task-sharing unit can also optimize household chore distribution by considering the occupation and hobbies of family members. For example, the task-sharing unit can optimize household chore distribution based on the occupation and hobbies of family members. Furthermore, the task-sharing unit can also optimize household chore distribution by considering the health status of family members. For example, the task-sharing unit can optimize household chore distribution based on the health status of family members. This allows for the optimization of household chore distribution by considering the attribute information of family members. Some or all of the above processing in the task-sharing unit may be performed using AI, for example, or without AI. For example, the task-sharing unit can input the attribute information of family members into a generating AI and have the generating AI perform the optimization of household chore distribution.

[0111] The task allocation system can estimate the user's emotions and determine the priority of household chore allocation based on those estimated emotions. For example, if the user is feeling stressed, the task allocation system will prioritize relaxing chores. For example, if the task allocation system estimates the user's emotions and prioritizes relaxing chores if the user is feeling stressed. The task allocation system can also prioritize important chores if the user is relaxed. For example, if the task allocation system estimates the user's emotions and prioritizes important chores if the user is relaxed. Furthermore, if the task allocation system is in a hurry, it can prioritize chores that require immediate attention. For example, if the task allocation system estimates the user's emotions and prioritizes chores that require immediate attention if the user is in a hurry. This allows the task allocation system to determine the priority of household chore allocation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the task-sharing unit may be performed using AI, for example, or without AI. For example, the task-sharing unit can input user emotion data into a generating AI and have the generating AI determine the priority of household chore distribution based on emotions.

[0112] The task-sharing unit can optimize household chore distribution by considering the geographical distribution of family members. For example, the task-sharing unit can optimize chore distribution based on the current location of each family member. For example, the task-sharing unit can optimize chore distribution based on the geographical distribution of family members and their current location. The task-sharing unit can also optimize chore distribution based on places that family members frequently visit. For example, the task-sharing unit can optimize chore distribution based on the geographical distribution of family members and their frequently visited places. Furthermore, the task-sharing unit can select the most appropriate method of division of labor based on the geographical distribution of family members. For example, the task-sharing unit can select an appropriate method of division of labor based on geographical distribution. This allows for the optimization of chore distribution by considering the geographical distribution of family members. Some or all of the above processing in the task-sharing unit may be performed using AI, for example, or without AI. For example, the task-sharing unit can input geographical distribution data into a generating AI and have the generating AI perform the optimization of chore distribution.

[0113] The task-sharing unit can improve the accuracy of task sharing by referring to relevant literature when optimizing the distribution of household chores. For example, the task-sharing unit can optimize the distribution of household chores by referring to relevant literature. For example, the task-sharing unit optimizes the distribution of household chores based on relevant literature. The task-sharing unit can also optimize the task-sharing algorithm based on relevant literature. For example, the task-sharing unit optimizes the task-sharing algorithm by referring to relevant literature. Furthermore, the task-sharing unit can select the most appropriate method of task sharing by referring to relevant literature. For example, the task-sharing unit selects an appropriate method of task sharing based on relevant literature. This allows the accuracy of task sharing to be improved by referring to relevant literature. Some or all of the above processing in the task-sharing unit may be performed using AI, for example, or without using AI. For example, the task-sharing unit can input relevant literature data into a generating AI and have the generating AI perform the task-sharing accuracy improvement.

[0114] The adjustment unit can estimate the user's emotions and adjust how household tasks are readjusted based on the estimated emotions. For example, if the user is feeling stressed, the adjustment unit will prioritize readjusting household tasks to help them relax. For example, the adjustment unit estimates the user's emotions and prioritizes relaxing household tasks if the user is feeling stressed. The adjustment unit can also prioritize readjusting important household tasks if the user is relaxed. For example, the adjustment unit estimates the user's emotions and prioritizes important household tasks if the user is relaxed. Furthermore, if the user is in a hurry, the adjustment unit can prioritize readjusting household tasks that require immediate attention. For example, the adjustment unit estimates the user's emotions and prioritizes household tasks that require immediate attention if the user is in a hurry. This allows the system to adjust how household tasks are readjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user emotion data into a generating AI and cause the generating AI to perform adjustments to the method of readjusting household tasks based on emotions.

[0115] The adjustment unit can optimize the adjustment algorithm by referring to past adjustment data when readjusting household tasks. For example, the adjustment unit readjusts the current household tasks based on past adjustment data. For example, the adjustment unit readjusts the current household tasks by referring to past adjustment data. The adjustment unit can also optimize the adjustment algorithm by referring to past adjustment data. For example, the adjustment unit optimizes the adjustment algorithm based on past adjustment data. Furthermore, the adjustment unit can select the most appropriate adjustment algorithm based on past adjustment data. For example, the adjustment unit selects an appropriate adjustment algorithm based on past adjustment data. This allows the adjustment algorithm to be optimized by referring to past adjustment data. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input past adjustment data into a generating AI and have the generating AI perform the optimization of the adjustment algorithm.

[0116] The adjustment unit can readjust household tasks while considering the attribute information of family members. For example, the adjustment unit can readjust household tasks while considering the age and gender of family members. For example, the adjustment unit can readjust household tasks based on the age and gender of family members. The adjustment unit can also readjust household tasks while considering the occupation and hobbies of family members. For example, the adjustment unit can readjust household tasks based on the occupation and hobbies of family members. Furthermore, the adjustment unit can also readjust household tasks while considering the health status of family members. For example, the adjustment unit can readjust household tasks based on the health status of family members. This allows for the readjustment of household tasks while considering the attribute information of family members. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the attribute information of family members into a generating AI and have the generating AI perform the readjustment of household tasks.

[0117] The adjustment unit can estimate the user's emotions and determine the priority of reallocating household tasks based on the estimated emotions. For example, if the user is feeling stressed, the adjustment unit will prioritize reallocating relaxing household tasks. For example, the adjustment unit estimates the user's emotions and prioritizes relaxing household tasks if the user is feeling stressed. The adjustment unit can also prioritize reallocating important household tasks if the user is relaxed. For example, the adjustment unit estimates the user's emotions and prioritizes important household tasks if the user is relaxed. Furthermore, if the user is in a hurry, the adjustment unit can prioritize reallocating household tasks that require immediate attention. For example, the adjustment unit estimates the user's emotions and prioritizes household tasks that require immediate attention if the user is in a hurry. This allows the system to determine the priority of reallocating household tasks based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user emotion data into a generating AI and have the generating AI determine the priority of readjusting household tasks based on emotions.

[0118] The adjustment unit can readjust household tasks while considering the geographical distribution of family members. For example, the adjustment unit can readjust household tasks based on the current location of family members. For example, the adjustment unit can consider the geographical distribution of family members and readjust household tasks based on their current location. The adjustment unit can also readjust household tasks based on places that family members frequently visit. For example, the adjustment unit can consider the geographical distribution of family members and readjust household tasks based on places that family members frequently visit. Furthermore, the adjustment unit can select the most appropriate adjustment method based on the geographical distribution of family members. For example, the adjustment unit can select an appropriate adjustment method based on geographical distribution. This allows for the readjustment of household tasks while considering the geographical distribution of family members. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input geographical distribution data into a generating AI and have the generating AI perform the readjustment of household tasks.

[0119] The adjustment unit can improve the accuracy of adjustments by referring to relevant literature when readjusting household tasks. For example, the adjustment unit readjusts household tasks by referring to relevant literature. For example, the adjustment unit readjusts household tasks based on relevant literature. The adjustment unit can also optimize the adjustment algorithm based on relevant literature. For example, the adjustment unit optimizes the adjustment algorithm by referring to relevant literature. Furthermore, the adjustment unit can select the most appropriate adjustment method by referring to relevant literature. For example, the adjustment unit selects an appropriate adjustment method based on relevant literature. This allows the adjustment unit to improve the accuracy of adjustments by referring to relevant literature. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input relevant literature data into a generating AI and have the generating AI perform the adjustment accuracy improvement.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The household chore optimization system can also include a health management unit. This unit monitors the health status of family members and reflects this in the assignment of household chores. For example, if a family member is unwell, the health management unit assigns them a less burdensome task. It can also consider the activity levels of family members and assign exercise tasks to those who are not getting enough exercise. Furthermore, the health management unit can monitor the dietary habits of family members and generate meal preparation tasks that consider nutritional balance. This makes it possible to assign household chores while taking the health status of the family into account.

[0122] The household chore optimization system can also include an entertainment section. This section considers the hobbies and interests of family members and provides time for relaxation. For example, it can suggest movies and music that family members enjoy. It can also suggest events and activities tailored to each family member's interests. Furthermore, it can suggest games and activities that the whole family can enjoy, strengthening family bonds. This provides time for relaxation between household chores, reducing stress for the family.

[0123] The household optimization system can also include an educational support unit. This unit monitors children's learning progress and generates learning tasks. For example, it manages children's school homework and projects and sends reminders at appropriate times. It can also analyze children's learning progress and generate additional learning tasks as needed. Furthermore, it can suggest learning resources and activities based on children's interests and strengths. This supports children's learning and optimizes the educational environment within the home.

[0124] The household chore optimization system can also include a communication unit. This unit facilitates communication among family members, sharing task progress and schedules. For example, it can send notifications to other members when a task is completed. It can also provide a platform for family members to exchange messages. Furthermore, it can manage family meeting schedules and send reminders. This streamlines communication among family members and improves the efficiency of household chores.

[0125] The household optimization system can also be equipped with an energy management unit. This unit monitors household energy usage and suggests efficient energy use. For example, it can schedule household tasks during off-peak hours when electricity consumption is low. It can also recommend the use of energy-efficient appliances. Furthermore, it can analyze household energy usage data and provide advice for energy conservation. This optimizes household energy use and enables an environmentally friendly lifestyle.

[0126] The household chore optimization system can also be equipped with an emotion analysis unit. This unit monitors the emotions of family members in real time and adjusts the assignment and schedule of household chores. For example, if a family member is feeling stressed, the emotion analysis unit assigns them a relaxing task. Conversely, if a family member is relaxed, it can assign them an important task. Furthermore, the emotion analysis unit can analyze the emotional data of family members and provide advice for long-term stress management. This makes it possible to assign household chores while considering the emotional state of the family.

[0127] The household optimization system can also be equipped with a safety management unit. The safety management unit monitors the safety situation within the home and ensures the safety of household tasks. For example, the safety management unit monitors the usage of appliances and sends alerts if abnormalities occur. The safety management unit can also assign appropriate tasks to ensure that children can perform household chores safely. Furthermore, the safety management unit can identify hazardous areas within the home and suggest solutions. This ensures safety within the home and allows household chores to be performed with peace of mind.

[0128] The household chore optimization system can also include a feedback unit. This unit collects feedback from family members and incorporates it into system improvements. For example, the feedback unit can collect opinions and requests regarding the assignment of household chore tasks. It can also conduct surveys on the efficiency and satisfaction levels of household chore tasks. Furthermore, the feedback unit can analyze the collected feedback to identify areas for system improvement. This allows for the improvement of the household chore optimization system to meet the needs of the family.

[0129] The household optimization system can also be equipped with an environmental monitoring unit. This unit monitors the environmental conditions within the home and reflects this in the household task schedule. For example, it can monitor room temperature and humidity and suggest cleaning or ventilation at appropriate times. It can also monitor air quality and recommend the use of an air purifier as needed. Furthermore, it can analyze the environmental data within the home and provide advice for maintaining a comfortable living environment. This enables the scheduling of household tasks to take into account the environmental conditions within the home.

[0130] The household chore optimization system can also include a rewards section. The rewards section provides rewards to family members for completing household chores. For example, the rewards section can award points to members who complete chores, and members can redeem points to receive rewards. The rewards section can also offer special rewards when the entire family works together to complete chores. Furthermore, the rewards section can visualize the completion status of chores and encourage competition among family members. This can increase motivation for household chores and create an environment where the whole family cooperates in doing chores.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The data collection unit integrates with the family's calendar and automatically imports each person's schedule. The data collection unit can obtain calendar information using, for example, API integration. The data collection unit can also periodically update calendar information using its scheduling function. Step 2: The analysis unit analyzes the importance of the schedules collected by the collection unit and reflects this in the prioritization of household chores. The analysis unit evaluates importance based on factors such as urgency and impact. The analysis unit can also determine priorities by considering the needs of the family. Step 3: The generation unit automatically generates household tasks based on the results analyzed by the analysis unit. For example, the generation unit generates daily household tasks. The generation unit can also generate additional tasks for special events (birthdays, trips, parties, etc.). Step 4: The task distribution unit optimizes the distribution of household tasks generated by the generation unit, taking into account each family member's strengths and weaknesses, schedule, health, etc. For example, the task distribution unit assigns cooking to members who are good at cooking, and cleaning to members who are good at cleaning. The task distribution unit can also incorporate participation in household chores according to the children's ages. Step 5: The coordination team readjusts household tasks in response to sudden schedule changes or illness. For example, if a sudden meeting comes up or someone becomes ill, the coordination team will assign tasks to other members. The coordination team can also monitor the progress of household tasks and assign help if there are delays.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] For example, the data collection unit is implemented by either the data processing unit 12 or the smart device 14. For example, the specific processing unit 290 of the data processing unit 12 acquires family calendar information using API integration and updates it periodically. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to evaluate the importance of the collected schedules and determine the priority of household chores. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to automatically generate household chore tasks. The task distribution unit is implemented by the control unit 46A of the smart device 14, for example, to distribute household chore tasks considering the strengths and weaknesses and schedules of family members. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to readjust household chore tasks in response to sudden schedule changes or illness. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] For example, the data collection unit is implemented by either the data processing unit 12 or the smart glasses 214. For example, the specific processing unit 290 of the data processing unit 12 acquires family calendar information using API integration and updates it periodically. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which evaluates the importance of the collected schedules and determines the priority of household chores. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically generates household chore tasks. The task distribution unit is implemented by, for example, the control unit 46A of the smart glasses 214, which distributes household chore tasks considering the strengths and weaknesses of family members and their schedules. The adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which readjusts household chore tasks in response to sudden schedule changes or illness. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] For example, the data collection unit is implemented by either the data processing unit 12 or the headset terminal 314. For example, the specific processing unit 290 of the data processing unit 12 acquires family calendar information using API integration and updates it periodically. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to evaluate the importance of the collected schedules and determine the priority of household chores. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to automatically generate household chore tasks. The task distribution unit is implemented by the control unit 46A of the headset terminal 314, for example, to distribute household chore tasks considering the strengths and weaknesses of family members and their schedules. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to readjust household chore tasks in response to sudden schedule changes or illness. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] For example, the data collection unit is implemented by either the data processing unit 12 or the robot 414. For example, the specific processing unit 290 of the data processing unit 12 acquires family calendar information using API integration and updates it periodically. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which evaluates the importance of the collected schedules and determines the priority of household chores. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically generates household chore tasks. The task distribution unit is implemented by, for example, the control unit 46A of the robot 414, which distributes household chore tasks considering the strengths and weaknesses of family members and their schedules. The adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which readjusts household chore tasks in response to sudden schedule changes or illness. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] (Note 1) A data collection unit that automatically imports each person's schedule by linking with the family calendar, An analysis unit analyzes the importance of the schedules collected by the aforementioned collection unit and reflects this in the prioritization of household chores. A generation unit that automatically generates household tasks based on the results of analysis by the aforementioned analysis unit, The household chore tasks generated by the generation unit are divided into a task distribution unit that optimizes the distribution of household chore tasks considering the strengths and weaknesses, schedules, and physical condition of each family member. It includes an adjustment unit that readjusts household tasks in response to sudden schedule changes or illness. A system characterized by the following features. (Note 2) The aforementioned collection unit is It integrates with family calendars and automatically imports each person's schedule. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The importance of the schedules collected by the aforementioned collection unit is analyzed and reflected in the prioritization of household chores. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is It reflects not only daily household tasks but also additional tasks for special events. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned division of labor is, Optimize the division of tasks by considering each family member's strengths and weaknesses, schedule, and health. The system described in Appendix 1, characterized by the features described herein. (Note 6) The adjustment unit is, Readjust household tasks in response to sudden changes in plans or illness. The system described in Appendix 1, characterized by the features described herein. (Note 7) The adjustment unit is, Monitor the progress of household chores and assign help if there are delays. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is We propose ways to efficiently combine multiple household chores. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is Schedules are made with energy use optimization in mind. The system described in Appendix 1, characterized by the features described herein. (Note 10) The adjustment unit is, Send timely reminders via your smartphone. The system described in Appendix 1, characterized by the features described herein. (Note 11) The adjustment unit is, Create an environment where family members can share task completion status and support each other. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of schedule acquisition based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is Analyze the past schedule history of family members and select the optimal method of acquisition. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When retrieving schedules, filtering is performed based on the current living situation and areas of interest of family members. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is The system estimates the user's emotions and determines the priority of what to acquire based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is When retrieving schedules, the system prioritizes retrieving highly relevant schedules by considering the geographical location information of family members. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is When retrieving schedules, the system analyzes the social media activity of family members and retrieves relevant schedules. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis method for determining the importance of appointments based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is When analyzing the importance of a schedule, we optimize the analysis algorithm by referring to past importance data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is When analyzing the importance of a schedule, the analysis should take into account the attribute information of family members. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is When analyzing the importance of appointments, the geographical distribution of family members should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is When analyzing the importance of a schedule, refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is It estimates the user's emotions and adjusts how household tasks are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating household tasks, the generation algorithm is optimized by referring to past task data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is When generating household chore tasks, the system takes into account the attribute information of family members. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is It estimates the user's emotions and adjusts how household tasks are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is When generating household chore tasks, the geographical distribution of family members is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is When generating household chore tasks, refer to relevant literature to improve the accuracy of the generation. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned division of labor is, It estimates the user's emotions and adjusts the method of dividing household chores based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned division of labor is, When optimizing the division of household chores, we optimize the division algorithm by referring to past division data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned division of labor is, When optimizing the division of household chores, consider the attribute information of each family member when assigning tasks. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned division of labor is, It estimates the user's emotions and determines the priority of household chore distribution based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned division of labor is, When optimizing the division of household chores, consider the geographical distribution of family members. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned division of labor is, When optimizing the division of household chores, refer to relevant literature to improve the accuracy of the division. The system described in Appendix 1, characterized by the features described herein. (Note 36) The adjustment unit is, It estimates the user's emotions and adjusts how household tasks are readjusted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The adjustment unit is, When readjusting household tasks, the adjustment algorithm is optimized by referring to past adjustment data. The system described in Appendix 1, characterized by the features described herein. (Note 38) The adjustment unit is, When readjusting household tasks, take into account the attributes of family members. The system described in Appendix 1, characterized by the features described herein. (Note 39) The adjustment unit is, It estimates the user's emotions and determines the priority of readjusting household tasks based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The adjustment unit is, When readjusting household tasks, take into account the geographical distribution of family members. The system described in Appendix 1, characterized by the features described herein. (Note 41) The adjustment unit is, When readjusting household tasks, refer to relevant literature to improve the accuracy of the adjustments. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0205] 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 data collection unit that automatically imports each person's schedule by linking with the family calendar, An analysis unit analyzes the importance of the schedules collected by the aforementioned collection unit and reflects this in the prioritization of household chores. A generation unit that automatically generates household tasks based on the results of analysis by the aforementioned analysis unit, The household chore tasks generated by the generation unit are divided into a task distribution unit that optimizes the distribution of household chore tasks considering the strengths and weaknesses, schedules, and physical condition of each family member. It includes an adjustment unit that readjusts household tasks in response to sudden schedule changes or illness. A system characterized by the following features.

2. The aforementioned collection unit is It integrates with family calendars and automatically imports each person's schedule. The system according to feature 1.

3. The aforementioned analysis unit is The importance of the schedules collected by the aforementioned collection unit is analyzed and reflected in the prioritization of household chores. The system according to feature 1.

4. The generating unit is It reflects not only daily household tasks but also additional tasks for special events. The system according to feature 1.

5. The aforementioned division of labor is, Optimize the division of tasks by considering each family member's strengths and weaknesses, schedule, and health. The system according to feature 1.

6. The adjustment unit is, Readjust household tasks in response to sudden changes in plans or illness. The system according to feature 1.

7. The adjustment unit is, Monitor the progress of household chores and assign help if there are delays. The system according to feature 1.

8. The generating unit is We propose ways to efficiently combine multiple household chores. The system according to feature 1.

Images