System

Abstract

An object of a system according to an embodiment is to optimize the sharing of housekeeping and childcare in a dual-income household and reduce the burden on women.SOLUTION: A system according to an embodiment includes a task visualizer, a schedule analyzer, a sharing plan proposer, and a notification feedback unit. The task visualizing unit visualizes the housekeeping and childcare task. The schedule analysis part analyzes the schedule of the married couple. A sharing plan proposal part proposes an optimum sharing plan on the basis of the housework childcare task visualized by the task visualization part and the schedule analyzed by the schedule analysis part. The notification feedback section notifies the sharing proposal proposed by the sharing proposal section and feeds back the execution status.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, there was an issue of an uneven division of housework and childcare responsibilities in dual-income households, with women bearing a disproportionate burden.

[0005] The system according to the embodiment aims to optimize the division of household chores and childcare in dual-income households and reduce the burden on women. [Means for solving the problem]

[0006] The system according to the embodiment includes a task visualization unit, a schedule analysis unit, a work sharing plan proposal unit, and a notification feedback unit. The task visualization unit visualizes housework and childcare tasks. The schedule analysis unit analyzes the couple's schedule. The work sharing plan proposal unit proposes an optimal work sharing plan based on the housework and childcare tasks visualized by the task visualization unit and the schedule analyzed by the schedule analysis unit. The notification feedback unit notifies the user of the work sharing plan proposed by the work sharing plan proposal unit and provides feedback on the progress of the plan. [Effects of the Invention]

[0007] The system according to the embodiment can optimize the division of household chores and childcare in dual-income households, thereby reducing the burden on women. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The housework and childcare sharing proposal system according to an embodiment of the present invention visualizes housework and childcare tasks, analyzes the schedules of couples, proposes optimal sharing plans, and provides notifications and feedback. As a result, the housework and childcare sharing proposal system can fairly share the burden of housework and childcare and promote women's participation in the workforce.

[0029] A housework and childcare sharing proposal system according to an embodiment includes a task visualization unit, a schedule analysis unit, a sharing plan proposal unit, and a notification and feedback unit. The task visualization unit visualizes housework and childcare tasks. For example, the generation AI lists housework and childcare tasks performed at home and visualizes the importance and required time for each task. The generation AI also compiles a list of tasks such as cleaning, laundry, cooking, and taking children to and from school, and displays the time and frequency required for each task. The schedule analysis unit analyzes the schedules of the couple. For example, the generation AI analyzes each spouse's schedule to determine their free and busy times. The generation AI also takes into account times when the husband is busy at work or when the wife is away in meetings. The sharing plan proposal unit proposes an optimal sharing plan based on the housework and childcare tasks visualized by the task visualization unit and the schedule analyzed by the schedule analysis unit. For example, the generation AI generates a sharing plan tailored to the couple's schedule, such as the husband taking children to and from school in the morning and the wife cooking in the evening. The notification and feedback unit notifies the division of labor proposed by the division of labor proposal unit and provides feedback on the implementation status. For example, the generation AI provides a function to notify the division of labor plan through a smartphone app and record the implementation status. This enables the housework and childcare division proposal system to fairly divide the burden of housework and childcare and promote women's participation in the workforce.

[0030] The task visualization unit can automatically prioritize tasks and categorize them based on urgency and importance. For example, the generation AI creates a list of household chores and childcare tasks and rates each task's urgency and importance. For example, meal preparation has a high urgency and importance, while gardening has a low urgency and importance. To prioritize tasks, the generation AI learns from past data and determines which tasks are most important for the household. For example, helping children with their homework has a high priority, while cleaning on the weekend has a low priority. The generation AI also categorizes tasks based on urgency and importance, and automatically adjusts the task schedule. For example, urgent tasks are executed immediately, while less important tasks are postponed. This enables efficient task management by automatically prioritizing tasks and categorizing them based on urgency and importance.

[0031] The task visualization unit can track the progress of tasks in real time and visualize the progress. For example, the task visualization unit provides a dashboard for the generation AI to track the progress of tasks in real time and visualize the progress. For example, it displays the progress of cleaning in a graph. To track the progress of tasks, the generation AI also works with sensors and smart devices to monitor the actual work status. For example, it checks the operation status of the washing machine in real time. To visualize the progress, the generation AI also displays the completion rate of tasks as a percentage, allowing the user to see at a glance how far along each task is. For example, it shows that cooking preparation is 50% complete. This makes it easier to manage tasks by tracking the progress of tasks in real time and visualizing the progress.

[0032] The task visualization unit can automatically list the resources required to perform a task and generate a purchasing list. In the task visualization unit, for example, the generation AI creates a list of housework and childcare tasks and automatically lists the resources required for each task. For example, the ingredients and cooking utensils required for a cooking task are added to the list. In addition, to list the resources required to perform a task, the generation AI learns from past data and determines which resources are needed. For example, it adds cleaning tools and detergents required for a cleaning task to the list. The generation AI also automatically generates a purchasing list, allowing the user to easily purchase the required resources. For example, a purchasing list for ingredients and cleaning tools is displayed on a smartphone app. In this way, automatically listing the resources required to perform a task and generating a purchasing list makes it easier to manage the required resources.

[0033] The task visualization unit can share the visualization of housework and childcare tasks with other members of the household, introducing a system for everyone to share the tasks. For example, the task visualization unit introduces a system in which the generation AI shares the list of housework and childcare tasks with other members of the household and everyone shares the tasks. For example, tasks can be assigned to children and grandparents. The generation AI also provides a task management app so that other members of the household can check the tasks and share their progress. For example, task progress can be checked through a smartphone app. The generation AI also introduces a system that takes into account the schedules of other members of the household and shares tasks fairly. For example, tasks can be assigned based on the time when children return home from school. In this way, the visualization of housework and childcare tasks can be shared with other members of the household and everyone can share the tasks, allowing the burden of housework and childcare to be shared fairly.

[0034] The schedule analysis unit can learn the couple's past schedule data and predict future schedules. For example, the schedule analysis unit adds a function where the generation AI learns the couple's past schedule data and predicts future schedules. For example, it predicts busy times next month based on past patterns. To predict future schedules, the generation AI also analyzes the couple's past behavioral data and predicts the frequency of specific events and tasks. For example, it predicts family events every weekend. The generation AI also predicts future schedules and adjusts task allocation plans based on that. For example, it predicts meetings and business trips next week and adjusts housework and childcare tasks accordingly. In this way, by learning the couple's past schedule data and predicting future schedules, it can propose more accurate task allocation plans.

[0035] The schedule analysis unit reflects schedule changes and cancellations in real time and can update the task allocation plan based on the latest schedule. For example, the schedule analysis unit allows the generation AI to reflect schedule changes and cancellations between a couple in real time and update the task allocation plan based on the latest schedule. For example, if a meeting is canceled, tasks are immediately redistributed. To reflect schedule changes in real time, the generation AI also works with calendar apps and schedule management tools. For example, changes in Google Calendar are automatically reflected. The generation AI also detects schedule changes and regenerates the task allocation plan based on the latest schedule. For example, tasks are redistributed in the event of a sudden business trip. This allows flexible task management by reflecting schedule changes and cancellations in real time and updating the task allocation plan based on the latest schedule.

[0036] The schedule analysis unit can propose a task sharing plan that takes into account not only work schedules but also personal plans. For example, the generation AI proposes a task sharing plan that takes into account not only the couple's work schedules but also their personal plans. For example, it takes into account time for hobbies and vacation plans. To take personal plans into account, the generation AI also works with calendar apps and schedule management tools to automatically obtain hobby and vacation plans. For example, it reflects schedules from Google Calendar. The generation AI also adjusts the task sharing plan based on the personal plans. For example, it assigns housework and childcare tasks to other time slots to ensure time for hobbies. In this way, by proposing a task sharing plan that takes into account not only work schedules but also personal plans, it is possible to provide a task sharing plan that takes into account the couple's entire lives.

[0037] The schedule analysis unit can link the couple's schedule analysis with the schedules of other household members to optimize the overall schedule. For example, the generation AI can link the couple's schedule analysis with the schedules of other household members to optimize the overall schedule. For example, it takes into account children's school events and grandparents' schedules. To link the schedules of other household members, the generation AI can also link with calendar apps and schedule management tools to automatically obtain everyone's schedules. For example, it can integrate the Google Calendars of all family members. The generation AI can also adjust task allocation plans based on the overall schedule. For example, on days when there are children's school events, other members can be in charge of housework. In this way, the couple's schedule analysis can be linked with the schedules of other household members to optimize the overall schedule, making it easier to manage the schedule of the entire household.

[0038] The task sharing plan proposal unit can learn from past task sharing records and propose more effective task sharing plans. The task sharing plan proposal unit adds a function that allows the generation AI to learn from past task sharing records and propose more effective task sharing plans. For example, it analyzes which task sharing plan was most effective based on past data. In addition, to learn from past task sharing records, the generation AI collects task execution status and feedback data to identify effective task sharing plans. For example, if performing a specific task at a specific time period is effective, it will propose that pattern. In addition, the generation AI makes specific suggestions for adjusting task sharing plans based on past task sharing records. For example, it will re-propose a task sharing pattern that was successful in the past. In this way, housework and childcare can be shared more efficiently by learning from past task sharing records and proposing more effective task sharing plans.

[0039] The task sharing plan proposal unit can generate a task sharing plan that is optimal for each task performer, taking into account the strengths and preferences of the task performer. For example, the generation AI generates a task sharing plan that is optimal for each task performer, taking into account the strengths and preferences of the task performer. For example, it assigns cooking tasks to someone who is good at cooking, and cleaning tasks to someone who is good at cleaning. In addition, to take into account strengths and preferences, the generation AI learns past task execution data and determines which task is suitable for which performer. For example, it proposes a task sharing plan based on past successful task execution patterns. In addition, the generation AI makes specific suggestions for adjusting the task sharing plan based on the preferences of the task performer. For example, it assigns a particular task to a performer who prefers that task. In this way, by generating a task sharing plan that is optimal for each task performer, taking into account the strengths and preferences of the task performer, housework and childcare can be shared more effectively.

[0040] The task sharing plan proposal unit can generate a task sharing plan that includes other household members and introduce a system in which everyone cooperates to complete tasks. For example, the task sharing plan proposal unit generates a task sharing plan that includes other household members and introduces a system in which everyone cooperates to complete tasks. For example, tasks may be assigned to children and grandparents. The task sharing plan proposal unit also provides a task management app so that other household members can check the tasks and share their completion status. For example, task progress may be checked through a smartphone app. The task sharing plan proposal unit also introduces a system in which tasks are divided fairly, taking into account the schedules of other household members. For example, tasks may be assigned according to the time when children return home from school. In this way, by generating a task sharing plan that includes other household members and having everyone cooperate to complete tasks, the burden of housework and childcare can be divided fairly.

[0041] The task sharing plan proposal unit can compare the plan with examples and best practices from other households to propose a more effective task sharing plan. For example, the generation AI learns from examples and best practices from other households to propose a more effective task sharing plan. For example, it uses the task sharing patterns that have been successful in other households as a reference. In addition, to compare the examples of other households, the generation AI builds a database and collects successful and unsuccessful examples. For example, it makes proposals based on examples where a specific task sharing was effective. In addition, the generation AI makes specific proposals for adjusting the task sharing plan based on best practices. For example, it proposes a task sharing pattern that has been effective in other households. In this way, by comparing with examples and best practices from other households to propose a more effective task sharing plan, household chores and childcare can be shared more efficiently.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The housework and childcare sharing suggestion system can also monitor the health of task performers and adjust task sharing according to their health status. For example, the generation AI collects health data from task performers and makes suggestions to reduce the burden of tasks if they are feeling unwell. To monitor health status, the generation AI also connects with wearable devices to obtain heart rate and sleep data in real time. For example, if a person is sleep-deprived, it will assign less burdensome tasks. The generation AI also makes specific suggestions for adjusting the task sharing plan based on the health data. For example, it could concentrate important tasks on days when they are feeling well. This allows the system to propose sharing plans that take into account the health status of task performers, making it possible to more appropriately share the burden of housework and childcare.

[0044] The housework and childcare sharing suggestion system can also evaluate the skill level of task performers and suggest task sharing based on their skills. For example, the generation AI can study the past performance of task performers to determine which tasks they are good at. To evaluate skill level, the generation AI can monitor task execution status and analyze the performer's performance. For example, cooking tasks can be assigned preferentially to someone who is good at cooking. The generation AI can also make specific suggestions for adjusting the task sharing plan based on skill level. For example, beginners can be assigned easy tasks, and more difficult tasks can be assigned to experienced users. This can improve the efficiency of housework and childcare by proposing sharing plans that take into account the skill level of the task performer.

[0045] The housework and childcare sharing suggestion system can also incorporate a reward system to increase the motivation of task performers. For example, the generation AI could monitor task completion status and award points to those who complete the task. To implement the reward system, the generation AI could set reward rules within the household and provide rewards based on the points. For example, once a certain number of points are accumulated, the generation AI could suggest a family meal out or a special event. The generation AI could also make specific suggestions to increase motivation. For example, it could send an encouraging message each time a task is completed. By introducing a reward system to increase the motivation of task performers, housework and childcare sharing can be carried out more smoothly.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The task visualization unit visualizes household and childcare tasks. For example, the generation AI lists household and childcare tasks to be performed at home and visualizes the importance and required time for each task. The generation AI also compiles a list of tasks such as cleaning, laundry, cooking, and taking children to and from school, and displays the time and frequency required for each task. Step 2: The schedule analysis unit analyzes the couple's schedules. For example, the generation AI analyzes each spouse's schedule and identifies free and busy times. The generation AI also takes into account times when the husband is busy at work or when the wife is away in meetings. Step 3: The division of labor proposal unit proposes an optimal division of labor based on the household chores and childcare tasks visualized by the task visualization unit and the schedule analyzed by the schedule analysis unit. For example, the generation AI generates a division of labor that matches the couple's schedule, such as the husband taking the children to and from school in the morning and the wife cooking in the evening. Step 4: The notification and feedback unit notifies the user of the allocation plan proposed by the allocation plan proposal unit and provides feedback on the implementation status. For example, the generation AI can notify the user of the allocation plan through a smartphone app and provide a function to record the implementation status.

[0048] (Example 2) The housework and childcare sharing proposal system according to an embodiment of the present invention visualizes housework and childcare tasks, analyzes the schedules of couples, proposes optimal sharing plans, and provides notifications and feedback. As a result, the housework and childcare sharing proposal system can fairly share the burden of housework and childcare and promote women's participation in the workforce.

[0049] A housework and childcare sharing proposal system according to an embodiment includes a task visualization unit, a schedule analysis unit, a sharing plan proposal unit, and a notification and feedback unit. The task visualization unit visualizes housework and childcare tasks. For example, the generation AI lists housework and childcare tasks performed at home and visualizes the importance and required time for each task. The generation AI also compiles a list of tasks such as cleaning, laundry, cooking, and taking children to and from school, and displays the time and frequency required for each task. The schedule analysis unit analyzes the schedules of the couple. For example, the generation AI analyzes each spouse's schedule to determine their free and busy times. The generation AI also takes into account times when the husband is busy at work or when the wife is away in meetings. The sharing plan proposal unit proposes an optimal sharing plan based on the housework and childcare tasks visualized by the task visualization unit and the schedule analyzed by the schedule analysis unit. For example, the generation AI generates a sharing plan tailored to the couple's schedule, such as the husband taking children to and from school in the morning and the wife cooking in the evening. The notification and feedback unit notifies the division of labor proposed by the division of labor proposal unit and provides feedback on the implementation status. For example, the generation AI provides a function to notify the division of labor plan through a smartphone app and record the implementation status. This enables the housework and childcare division proposal system to fairly divide the burden of housework and childcare and promote women's participation in the workforce.

[0050] The task visualization unit can automatically prioritize tasks and categorize them based on urgency and importance. For example, the generation AI creates a list of household chores and childcare tasks and rates each task's urgency and importance. For example, meal preparation has a high urgency and importance, while gardening has a low urgency and importance. To prioritize tasks, the generation AI learns from past data and determines which tasks are most important for the household. For example, helping children with their homework has a high priority, while cleaning on the weekend has a low priority. The generation AI also categorizes tasks based on urgency and importance, and automatically adjusts the task schedule. For example, urgent tasks are executed immediately, while less important tasks are postponed. This enables efficient task management by automatically prioritizing tasks and categorizing them based on urgency and importance.

[0051] The task visualization unit can track the progress of tasks in real time and visualize the progress. For example, the task visualization unit provides a dashboard for the generation AI to track the progress of tasks in real time and visualize the progress. For example, it displays the progress of cleaning in a graph. To track the progress of tasks, the generation AI also works with sensors and smart devices to monitor the actual work status. For example, it checks the operation status of the washing machine in real time. To visualize the progress, the generation AI also displays the completion rate of tasks as a percentage, allowing the user to see at a glance how far along each task is. For example, it shows that cooking preparation is 50% complete. This makes it easier to manage tasks by tracking the progress of tasks in real time and visualizing the progress.

[0052] The task visualization unit uses the emotion estimation function to analyze the emotions of the task performer, identify tasks that increase stress, and make suggestions to reduce the burden. For example, the task visualization unit uses a generation AI to monitor the emotions of the task performer in real time and identify tasks that increase stress. For example, if tasks such as cleaning or laundry cause stress, the unit suggests assigning those tasks to another member. The emotion estimation function also collects emotional data from the task performer and analyzes the time periods and tasks that increase stress. For example, if cooking in the evening causes stress, the unit adjusts the tasks for that time period. The generation AI also makes specific suggestions based on the emotional data to reduce tasks that increase stress. For example, it suggests reducing the frequency of cleaning or using a housekeeping service. This allows the task performer's stress to be reduced by analyzing the emotions of the task performer, identifying tasks that increase stress, and making suggestions to reduce the burden.

[0053] The task visualization unit can automatically list the resources required to perform a task and generate a purchasing list. In the task visualization unit, for example, the generation AI creates a list of housework and childcare tasks and automatically lists the resources required for each task. For example, the ingredients and cooking utensils required for a cooking task are added to the list. In addition, to list the resources required to perform a task, the generation AI learns from past data and determines which resources are needed. For example, it adds cleaning tools and detergents required for a cleaning task to the list. The generation AI also automatically generates a purchasing list, allowing the user to easily purchase the required resources. For example, a purchasing list for ingredients and cleaning tools is displayed on a smartphone app. In this way, automatically listing the resources required to perform a task and generating a purchasing list makes it easier to manage the required resources.

[0054] The task visualization unit can share the visualization of housework and childcare tasks with other members of the household, introducing a system for everyone to share the tasks. For example, the task visualization unit introduces a system in which the generation AI shares the list of housework and childcare tasks with other members of the household and everyone shares the tasks. For example, tasks can be assigned to children and grandparents. The generation AI also provides a task management app so that other members of the household can check the tasks and share their progress. For example, task progress can be checked through a smartphone app. The generation AI also introduces a system that takes into account the schedules of other members of the household and shares tasks fairly. For example, tasks can be assigned based on the time when children return home from school. In this way, the visualization of housework and childcare tasks can be shared with other members of the household and everyone can share the tasks, allowing the burden of housework and childcare to be shared fairly.

[0055] The task visualization unit can use the emotion estimation function to monitor the user's emotions in real time while the user is performing housework and childcare tasks, and provide music and messages to elicit positive emotions. For example, the task visualization unit uses a generation AI to monitor the user's emotions in real time while the user is performing housework and childcare tasks, and provide music to elicit positive emotions. For example, relaxing music can be played while the user is cleaning. The emotion estimation function can also be used to collect the user's emotion data and provide messages to elicit positive emotions. For example, an encouraging message can be displayed while the user is cooking. The generation AI can also make specific suggestions based on the emotion data to keep the user's emotions positive. For example, if the user is feeling stressed, it can suggest a relaxing activity. In this way, the system can monitor the user's emotions in real time while the user is performing housework and childcare tasks, and provide music and messages to elicit positive emotions, thereby reducing the user's stress and eliciting positive emotions.

[0056] The schedule analysis unit can learn the couple's past schedule data and predict future schedules. For example, the schedule analysis unit adds a function where the generation AI learns the couple's past schedule data and predicts future schedules. For example, it predicts busy times next month based on past patterns. To predict future schedules, the generation AI also analyzes the couple's past behavioral data and predicts the frequency of specific events and tasks. For example, it predicts family events every weekend. The generation AI also predicts future schedules and adjusts task allocation plans based on that. For example, it predicts meetings and business trips next week and adjusts housework and childcare tasks accordingly. In this way, by learning the couple's past schedule data and predicting future schedules, it can propose more accurate task allocation plans.

[0057] The schedule analysis unit reflects schedule changes and cancellations in real time and can update the task allocation plan based on the latest schedule. For example, the schedule analysis unit allows the generation AI to reflect schedule changes and cancellations between a couple in real time and update the task allocation plan based on the latest schedule. For example, if a meeting is canceled, tasks are immediately redistributed. To reflect schedule changes in real time, the generation AI also works with calendar apps and schedule management tools. For example, changes in Google Calendar are automatically reflected. The generation AI also detects schedule changes and regenerates the task allocation plan based on the latest schedule. For example, tasks are redistributed in the event of a sudden business trip. This allows flexible task management by reflecting schedule changes and cancellations in real time and updating the task allocation plan based on the latest schedule.

[0058] The schedule analysis unit uses the emotion estimation function to estimate stress levels based on how busy the schedule is and adjusts the task allocation plan to avoid times when stress is high. For example, the schedule analysis unit uses the generation AI to estimate stress levels based on how busy the couple's schedule is and adjusts the task allocation plan to avoid times when stress is high. For example, reducing housework tasks on days with many meetings. The emotion estimation function also monitors the stress levels of the couple's schedule in real time and identifies times when stress is high. For example, avoiding times when long meetings occur. The generation AI also makes specific suggestions for adjusting the task allocation plan based on the stress levels. For example, assigning relaxing tasks to times when stress is high. In this way, stress for the couple can be reduced by estimating stress levels based on how busy the schedule is and adjusting the task allocation plan to avoid times when stress is high.

[0059] The schedule analysis unit can propose a task sharing plan that takes into account not only work schedules but also personal plans. For example, the generation AI proposes a task sharing plan that takes into account not only the couple's work schedules but also their personal plans. For example, it takes into account time for hobbies and vacation plans. To take personal plans into account, the generation AI also works with calendar apps and schedule management tools to automatically obtain hobby and vacation plans. For example, it reflects schedules from Google Calendar. The generation AI also adjusts the task sharing plan based on the personal plans. For example, it assigns housework and childcare tasks to other time slots to ensure time for hobbies. In this way, by proposing a task sharing plan that takes into account not only work schedules but also personal plans, it is possible to provide a task sharing plan that takes into account the couple's entire lives.

[0060] The schedule analysis unit can link the couple's schedule analysis with the schedules of other household members to optimize the overall schedule. For example, the generation AI can link the couple's schedule analysis with the schedules of other household members to optimize the overall schedule. For example, it takes into account children's school events and grandparents' schedules. To link the schedules of other household members, the generation AI can also link with calendar apps and schedule management tools to automatically obtain everyone's schedules. For example, it can integrate the Google Calendars of all family members. The generation AI can also adjust task allocation plans based on the overall schedule. For example, on days when there are children's school events, other members can be in charge of housework. In this way, the couple's schedule analysis can be linked with the schedules of other household members to optimize the overall schedule, making it easier to manage the schedule of the entire household.

[0061] The schedule analysis unit can use the emotion estimation function to analyze the couple's emotional reactions to their schedule and send reminders and messages to elicit positive emotions. For example, the schedule analysis unit uses the generation AI to analyze the couple's emotional reactions to their schedule and send reminders and messages to elicit positive emotions. For example, sending an encouraging message before an important meeting. The emotion estimation function can also be used to collect emotional data about the couple's schedule and make specific suggestions to elicit positive emotions. For example, sending a relaxing message during times of high stress. The generation AI can also set reminders based on the emotional data to elicit positive emotions about the couple's schedule. For example, it can remind the couple of upcoming family events or vacations. In this way, by analyzing the couple's emotional reactions to their schedule and sending reminders and messages to elicit positive emotions, the couple can be reduced stress and elicit positive emotions.

[0062] The task sharing plan proposal unit can learn from past task sharing records and propose more effective task sharing plans. The task sharing plan proposal unit adds a function that allows the generation AI to learn from past task sharing records and propose more effective task sharing plans. For example, it analyzes which task sharing plan was most effective based on past data. In addition, to learn from past task sharing records, the generation AI collects task execution status and feedback data to identify effective task sharing plans. For example, if performing a specific task at a specific time period is effective, it will propose that pattern. In addition, the generation AI makes specific suggestions for adjusting task sharing plans based on past task sharing records. For example, it will re-propose a task sharing pattern that was successful in the past. In this way, housework and childcare can be shared more efficiently by learning from past task sharing records and proposing more effective task sharing plans.

[0063] The task sharing plan proposal unit can generate a task sharing plan that is optimal for each task performer, taking into account the strengths and preferences of the task performer. For example, the generation AI generates a task sharing plan that is optimal for each task performer, taking into account the strengths and preferences of the task performer. For example, it assigns cooking tasks to someone who is good at cooking, and cleaning tasks to someone who is good at cleaning. In addition, to take into account strengths and preferences, the generation AI learns past task execution data and determines which task is suitable for which performer. For example, it proposes a task sharing plan based on past successful task execution patterns. In addition, the generation AI makes specific suggestions for adjusting the task sharing plan based on the preferences of the task performer. For example, it assigns a particular task to a performer who prefers that task. In this way, by generating a task sharing plan that is optimal for each task performer, taking into account the strengths and preferences of the task performer, housework and childcare can be shared more effectively.

[0064] The chore sharing plan proposal unit can use the emotion estimation function to analyze emotional reactions to the chore sharing plan and propose a chore sharing plan that elicits positive emotions. For example, the generation AI in the chore sharing plan proposal unit analyzes emotional reactions to the chore sharing plan and proposes a chore sharing plan that elicits positive emotions. For example, if performing a specific task elicits positive emotions, it prioritizes allocating that task. The emotion estimation function also collects emotional data about the chore sharing plan and makes specific proposals for eliciting positive emotions. For example, it prioritizes allocating tasks that are less stressful. The generation AI also makes specific proposals for adjusting the chore sharing plan based on the emotional data. For example, it prioritizes allocating tasks that elicit positive emotions. In this way, by analyzing emotional reactions to the chore sharing plan and proposing a chore sharing plan that elicits positive emotions, household chores and childcare can be shared more smoothly.

[0065] The task sharing plan proposal unit can generate a task sharing plan that includes other household members and introduce a system in which everyone cooperates to complete tasks. For example, the task sharing plan proposal unit generates a task sharing plan that includes other household members and introduces a system in which everyone cooperates to complete tasks. For example, tasks may be assigned to children and grandparents. The task sharing plan proposal unit also provides a task management app so that other household members can check the tasks and share their completion status. For example, task progress may be checked through a smartphone app. The task sharing plan proposal unit also introduces a system in which tasks are divided fairly, taking into account the schedules of other household members. For example, tasks may be assigned according to the time when children return home from school. In this way, by generating a task sharing plan that includes other household members and having everyone cooperate to complete tasks, the burden of housework and childcare can be divided fairly.

[0066] The task sharing plan proposal unit can compare the plan with examples and best practices from other households to propose a more effective task sharing plan. For example, the generation AI learns from examples and best practices from other households to propose a more effective task sharing plan. For example, it uses the task sharing patterns that have been successful in other households as a reference. In addition, to compare the examples of other households, the generation AI builds a database and collects successful and unsuccessful examples. For example, it makes proposals based on examples where a specific task sharing was effective. In addition, the generation AI makes specific proposals for adjusting the task sharing plan based on best practices. For example, it proposes a task sharing pattern that has been effective in other households. In this way, by comparing with examples and best practices from other households to propose a more effective task sharing plan, household chores and childcare can be shared more efficiently.

[0067] The sharing plan proposal unit can use the emotion estimation function to monitor the user's emotions in real time while the sharing plan is being executed and provide feedback to elicit positive emotions. For example, the sharing plan proposal unit monitors the user's emotions in real time while the generation AI is executing the sharing plan and provides feedback to elicit positive emotions. For example, it sends encouraging messages depending on the progress of the task. The emotion estimation function also collects the user's emotion data and makes specific suggestions to elicit positive emotions. For example, it suggests relaxing activities during times of high stress. The generation AI also provides feedback to elicit positive emotions while the sharing plan is being executed based on the emotion data. For example, it displays positive messages depending on the progress of the task. In this way, the sharing of housework and childcare can be done more smoothly by monitoring the user's emotions in real time while the sharing plan is being executed and providing feedback to elicit positive emotions.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The housework and childcare sharing suggestion system can also monitor the health of task performers and adjust task sharing according to their health status. For example, the generation AI collects health data from task performers and makes suggestions to reduce the burden of tasks if they are feeling unwell. To monitor health status, the generation AI also connects with wearable devices to obtain heart rate and sleep data in real time. For example, if a person is sleep-deprived, it will assign less burdensome tasks. The generation AI also makes specific suggestions for adjusting the task sharing plan based on the health data. For example, it could concentrate important tasks on days when they are feeling well. This allows the system to propose sharing plans that take into account the health status of task performers, making it possible to more appropriately share the burden of housework and childcare.

[0070] The housework and childcare sharing suggestion system can also evaluate the skill level of task performers and suggest task sharing based on their skills. For example, the generation AI can study the past performance of task performers to determine which tasks they are good at. To evaluate skill level, the generation AI can monitor task execution status and analyze the performer's performance. For example, cooking tasks can be assigned preferentially to someone who is good at cooking. The generation AI can also make specific suggestions for adjusting the task sharing plan based on skill level. For example, beginners can be assigned easy tasks, and more difficult tasks can be assigned to experienced users. This can improve the efficiency of housework and childcare by proposing sharing plans that take into account the skill level of the task performer.

[0071] The housework and childcare sharing suggestion system can also incorporate a reward system to increase the motivation of task performers. For example, the generation AI could monitor task completion status and award points to those who complete the task. To implement the reward system, the generation AI could set reward rules within the household and provide rewards based on the points. For example, once a certain number of points are accumulated, the generation AI could suggest a family meal out or a special event. The generation AI could also make specific suggestions to increase motivation. For example, it could send an encouraging message each time a task is completed. By introducing a reward system to increase the motivation of task performers, housework and childcare sharing can be carried out more smoothly.

[0072] The housework and childcare sharing suggestion system can also estimate the emotions of the task performer and suggest task sharing based on those emotions. For example, the generation AI collects emotional data from the task performer and suggests sharing stressful tasks with other members. It also uses the emotion estimation function to monitor the emotions of the task performer in real time and assign tasks that elicit positive emotions. For example, it prioritizes the allocation of relaxing tasks. The generation AI also makes specific suggestions for adjusting the task sharing plan based on the emotional data. For example, it could assign less burdensome tasks during times of high stress. In this way, the burden of housework and childcare can be reduced by proposing sharing plans that take into account the emotions of the task performer.

[0073] The housework and childcare sharing suggestion system can also estimate the emotions of the task performer and provide feedback based on those emotions. For example, the generation AI collects emotional data from the task performer and provides feedback designed to elicit positive emotions. The emotion estimation function can also be used to monitor the emotions of the task performer in real time and send encouraging messages. For example, positive messages can be displayed according to the task progress. The generation AI can also make specific suggestions based on the emotional data to keep the task performer's emotions positive. For example, it can suggest relaxing activities during times of high stress. This allows for smoother sharing of housework and childcare by providing feedback that takes into account the emotions of the task performer.

[0074] The housework and childcare sharing suggestion system can also estimate the emotions of the task performer and send reminders based on those emotions. For example, the generation AI collects emotional data from the task performer and sends reminders designed to elicit positive emotions. The emotion estimation function can also be used to monitor the emotions of the task performer in real time and send relaxing messages during times of high stress. For example, it could send an encouraging message before an important task. The generation AI can also use the emotional data to make specific suggestions to keep the task performer's emotions positive. For example, it could remind them of family events or vacation plans. This allows for smoother division of housework and childcare by sending reminders that take the task performer's emotions into consideration.

[0075] The housework and childcare sharing suggestion system can also estimate the emotions of the task performer and visualize the progress of tasks according to their emotions. For example, the generation AI collects emotional data from the task performer and identifies the progress of tasks that cause stress. The emotion estimation function can also be used to monitor the emotions of the task performer in real time and display the progress in a graph. For example, the progress of tasks that cause stress can be displayed in a color-coded manner. The generation AI can also make specific suggestions for adjusting the progress of tasks based on the emotional data. For example, it can suggest slowing down the progress of tasks that cause stress. This allows the division of housework and childcare responsibilities to be more smoothly achieved by visualizing progress that takes into account the emotions of the task performer.

[0076] The housework and childcare sharing suggestion system can also estimate the emotions of the task performer and set task priorities based on those emotions. For example, the generation AI collects emotional data from the task performer and makes suggestions to lower the priority of tasks that cause stress. The emotion estimation function can also be used to monitor the emotions of the task performer in real time and prioritize tasks that will elicit positive emotions. For example, tasks that are relaxing can be prioritized. The generation AI can also make specific suggestions for adjusting task priorities based on the emotional data. For example, tasks that are less burdensome can be prioritized during times of high stress. This allows for smoother division of housework and childcare responsibilities by setting priorities that take into account the emotions of the task performer.

[0077] The housework and childcare sharing suggestion system can also estimate the emotions of the task performer and visualize the progress of tasks according to their emotions. For example, the generation AI collects emotional data from the task performer and identifies the progress of tasks that cause stress. The emotion estimation function can also be used to monitor the emotions of the task performer in real time and display the progress in a graph. For example, the progress of tasks that cause stress can be displayed in a color-coded manner. The generation AI can also make specific suggestions for adjusting the progress of tasks based on the emotional data. For example, it can suggest slowing down the progress of tasks that cause stress. This allows the division of housework and childcare responsibilities to be more smoothly achieved by visualizing progress that takes into account the emotions of the task performer.

[0078] The housework and childcare sharing suggestion system can also estimate the emotions of the task performer and set task priorities based on those emotions. For example, the generation AI collects emotional data from the task performer and makes suggestions to lower the priority of tasks that cause stress. The emotion estimation function can also be used to monitor the emotions of the task performer in real time and prioritize tasks that will elicit positive emotions. For example, tasks that are relaxing can be prioritized. The generation AI can also make specific suggestions for adjusting task priorities based on the emotional data. For example, tasks that are less burdensome can be prioritized during times of high stress. This allows for smoother division of housework and childcare responsibilities by setting priorities that take into account the emotions of the task performer.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The task visualization unit visualizes household and childcare tasks. For example, the generation AI lists household and childcare tasks to be performed at home and visualizes the importance and required time for each task. The generation AI also compiles a list of tasks such as cleaning, laundry, cooking, and taking children to and from school, and displays the time and frequency required for each task. Step 2: The schedule analysis unit analyzes the couple's schedules. For example, the generation AI analyzes each spouse's schedule and identifies free and busy times. The generation AI also takes into account times when the husband is busy at work or when the wife is away in meetings. Step 3: The division of labor proposal unit proposes an optimal division of labor based on the household chores and childcare tasks visualized by the task visualization unit and the schedule analyzed by the schedule analysis unit. For example, the generation AI generates a division of labor that matches the couple's schedule, such as the husband taking the children to and from school in the morning and the wife cooking in the evening. Step 4: The notification and feedback unit notifies the user of the allocation plan proposed by the allocation plan proposal unit and provides feedback on the implementation status. For example, the generation AI can notify the user of the allocation plan through a smartphone app and provide a function to record the implementation status.

[0081] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0087] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0088] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0089] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0090] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0091] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0092] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0094] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0095] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0122] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0131] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0132] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0133] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0134] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0135] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0136] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0137] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0138] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0139] 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.

[0140] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0141] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0142] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0143] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0144] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0145] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0146] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0147] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A task visualization section that visualizes housework and childcare tasks, A schedule analysis section that analyzes the couple's schedules; a task sharing plan proposal unit that proposes an optimal task sharing plan based on the housework and childcare tasks visualized by the task visualization unit and the schedule analyzed by the schedule analysis unit; a notification feedback unit that notifies the sharing plan proposed by the sharing plan proposal unit and feeds back the execution status. A system characterized by:

2. The task visualization unit Automatically prioritize tasks and categorize them based on urgency and importance 2. The system of claim 1.

3. The task visualization unit Track task progress in real time and visualize progress 2. The system of claim 1.

4. The task visualization unit Analyze the emotions of the task performer, identify the tasks that cause stress, and make suggestions to reduce the burden 2. The system of claim 1.

5. The task visualization unit Automatically list the resources required to complete a task and generate a purchasing list 2. The system of claim 1.

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