System

Abstract

An object of a system according to an embodiment is to optimize task sharing of housework childcare and equalize burdens.SOLUTION: A system according to an embodiment includes a housekeeping and childcare task visualizer, a schedule analyzer, and a sharing plan proposer. The housekeeping child care task visualization unit visualizes the housekeeping child care 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 information obtained by the housekeeping and childcare task visualization part and the schedule analysis part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has led to an uneven division of household and childcare tasks, with the burden falling disproportionately on women.

[0005] The system according to the embodiment aims to optimize the division of household chores and childcare tasks and to equalize the burden. [Means for solving the problem]

[0006] The system according to the embodiment includes a housework and childcare task visualization unit, a schedule analysis unit, and a sharing plan proposal unit. The housework and childcare task visualization unit visualizes housework and childcare tasks. The schedule analysis unit analyzes the couple's schedule. The sharing plan proposal unit proposes an optimal sharing plan based on information obtained by the housework and childcare task visualization unit and the schedule analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can optimize the division of household chores and childcare tasks and evenly distribute the burden. [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, and proposes optimal sharing plans. 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 housework and childcare task visualization unit, a schedule analysis unit, and a sharing plan proposal unit. The housework and childcare task visualization unit visualizes housework and childcare tasks. For example, the generation AI lists the housework and childcare tasks performed at home and visualizes the content and required time of each task. The generation AI receives input from a user prompt containing instructions on what the user wants the generation AI to do, and the generation AI lists tasks based on the prompt. The schedule analysis unit analyzes the couple's schedule. For example, the generation AI analyzes each spouse's schedule to determine free and busy times. The generation AI receives input from the couple's schedule information, and the generation AI analyzes the schedule based on that information. The sharing plan proposal unit proposes an optimal sharing plan based on the information obtained by the housework and childcare task visualization unit and the schedule analysis unit. For example, the generation AI proposes an optimal daily sharing plan based on the visualized housework and childcare tasks and the couple's schedule. The AI ​​generation system generates a division of labor plan based on the inputs of household chores and childcare tasks and the couple's schedule. As a result, the system can fairly share the burden of household chores and childcare, promoting women's participation in the workforce.

[0030] The household and childcare task visualization unit can automatically prioritize tasks and dynamically update the list according to their importance. For example, when the generation AI visualizes household and childcare tasks, the household and childcare task visualization unit automatically evaluates the importance of each task and sets priorities. For example, tasks that are time-sensitive, such as taking children to and from school or preparing meals, are displayed first in the list. In addition, when prioritizing tasks, the system dynamically updates the list, taking into account the family's lifestyle and individual needs. For example, important tasks for specific days of the week or time periods are automatically placed at the top. The generation AI also monitors task progress in real time and resets priorities if there are any incomplete tasks. For example, if cleaning is not completed, cleaning is displayed as the next task. This allows important tasks to be prioritized by dynamically updating task priorities.

[0031] The household and childcare task visualization component can track task progress in real time and automatically remind users of incomplete tasks. For example, the generation AI tracks the progress of household and childcare tasks in real time and automatically sends reminders when there are incomplete tasks. For example, if cleaning is not completed, a reminder notification is sent. To track task progress, a shared task list accessible to all family members can be created, allowing each member to update their task completion status. For example, a smartphone app can be used to manage task progress. The incomplete task reminder function has also been enhanced, allowing users to customize the frequency and timing of reminders. For example, users can set frequent reminders for important tasks. This improves task completion rates by reminding users of incomplete tasks.

[0032] The household chore and childcare task visualization unit can use a voice input function to allow users to add and edit tasks by voice. For example, the household chore and childcare task visualization unit adds a voice input function to the household chore and childcare task visualization unit, allowing users to add and edit tasks by voice. For example, simply saying "add cleaning" adds a task to the task list. The voice input function can also be enhanced to allow users to specify task details by voice. For example, by saying "add taking out the trash at 8 a.m. tomorrow," specific times and contents can be set. Furthermore, voice recognition technology can be used to accurately analyze the user's voice instructions and reflect them in the task list. For example, adding multiple tasks at once can be easily done by voice input. This allows users to easily add and edit tasks using the voice input function.

[0033] The household chore and childcare task visualization unit can work in conjunction with smart home devices to automatically reflect the usage status of home appliances in the task list. For example, the household chore and childcare task visualization unit can link visualization of household chore and childcare tasks with smart home devices and automatically reflect the usage status of home appliances in the task list. For example, when a washing machine is running, "laundry completed" is automatically added to the list. In addition, the progress of household chore and childcare tasks is updated in real time based on data from smart home devices. For example, when a cleaning robot finishes cleaning, "cleaning completed" is reflected in the list. In addition, by reflecting the usage status of home appliances in the task list, the visualization of household chore and childcare tasks can be made more accurate. For example, a "shopping list" can be automatically generated based on the inventory status of the refrigerator. In this way, the usage status of home appliances can be automatically reflected in the task list, making the visualization of household chore and childcare tasks more accurate.

[0034] The schedule analysis unit can learn from past schedule data and predict future schedules. For example, the schedule analysis unit adds a function where the generation AI learns the past schedule data of a couple and predicts future schedules. For example, it automatically generates the next week's schedule based on past patterns. It also analyzes past schedule data and predicts the frequency of specific events and tasks. For example, it automatically adds a regular meeting that takes place every Friday to the schedule. The generation AI also predicts future schedules based on past schedule data and suggests optimal task allocation. For example, it allocates tasks that avoid busy times based on past data. This allows for more accurate schedule management by learning from past schedule data and predicting future schedules.

[0035] The schedule analysis unit can automatically notify and reanalyze if there is a change in the schedule. The schedule analysis unit adds a function that allows the generation AI to automatically notify and reanalyze if there is a change in a couple's schedule, for example. For example, if a meeting time is changed, the schedule is automatically updated. In addition, if there is a change in the schedule, the generation AI reanalyzes and re-proposes the optimal task allocation. For example, it adjusts task allocation in response to sudden changes in plans. In addition, a function will be added that allows the generation AI to detect schedule changes in real time and automatically send notifications. For example, if an appointment is canceled, a notification will be sent immediately. This will improve the accuracy of schedule management by automatically notifying and reanalyzing if there is a change in the schedule.

[0036] The schedule analysis unit can analyze the schedules of the entire family in an integrated manner, including the schedules of children in addition to the analysis of the couple's schedule. The schedule analysis unit adds a function to analyze the schedules of the entire family in an integrated manner, including the schedules of children in addition to the analysis of the couple's schedule. For example, it integrates the schedules of children's school events and extracurricular activities. It also analyzes the schedules of the entire family and suggests optimal task allocation. For example, it allocates tasks taking into consideration the schedules of all family members. It also makes the schedules of the entire family, including the schedules of children, visible so that all family members can access them. For example, it manages schedules using a shared calendar. This allows for integrated analysis of the schedules of the entire family, enabling more accurate schedule management.

[0037] The schedule analysis unit can link the couple's schedule analysis with the workplace scheduling system to take work schedules into account. For example, the schedule analysis unit can link the couple's schedule analysis with the workplace scheduling system to take work schedules into account. For example, it can reflect workplace meetings and business trip plans in the schedule. It can also link with the workplace scheduling system to suggest optimal task allocation based on the work schedule. For example, it can reduce housework and childcare tasks during busy hours. It can also obtain data from the workplace scheduling system and reflect it in the couple's schedule analysis. For example, it can link with the workplace calendar to automatically update the schedule. This allows for more accurate schedule management by taking work schedules into account.

[0038] The sharing plan proposal unit can learn from past sharing history and generate a fairer sharing plan. For example, the sharing plan proposal unit adds a function that allows the generation AI to learn from past sharing history and generate a fairer sharing plan. For example, it adjusts the sharing plan based on past data so that the husband and wife's burden is equal. It also analyzes past sharing history and, if a particular task is unevenly distributed, proposes to divide that task fairly. For example, if a particular task is unevenly distributed, it will be adjusted in the next sharing plan. It also generates a sharing plan based on past sharing history so that the husband and wife's burden is equal. For example, it assigns tasks based on past data so that the husband and wife's burden is equal. In this way, the generation AI can learn from past sharing history and generate a fairer sharing plan, thereby enabling the burden of housework and childcare to be shared equally.

[0039] The sharing plan proposal unit can adjust the burden so that it is evenly distributed, taking into account the difficulty and time required for the task. For example, the sharing plan proposal unit adds a function whereby the generation AI considers the difficulty and time required for the task and adjusts the sharing plan so that it is evenly distributed. For example, it distributes highly difficult tasks evenly. It also adjusts the sharing plan so that the burden between husband and wife is equal, based on the time required for the task. For example, it distributes tasks that require a long time evenly. The generation AI also analyzes the difficulty and time required for the task and generates a sharing plan so that the burden is evenly distributed. For example, it distributes highly difficult tasks evenly. In this way, it takes the difficulty and time required for the task into account and adjusts the burden so that it is evenly distributed, allowing for a fair sharing of the burden of housework and childcare.

[0040] The task sharing plan proposal unit can propose an optimal task sharing plan by taking into consideration the health and fatigue level of the entire family. The task sharing plan proposal unit adds a function in which the generation AI proposes an optimal task sharing plan by taking into consideration the health and fatigue level of the entire family. For example, it assigns lighter tasks to members who are highly fatigued. It also adjusts the task sharing plan so that the burden on the entire family is equal based on the health and fatigue level. For example, it assigns lighter tasks to members who are not in good health. The generation AI also analyzes the health and fatigue level of the entire family and proposes an optimal task sharing plan. For example, it assigns lighter tasks to members who are highly fatigued. In this way, the burden on the entire family is reduced by proposing a task sharing plan that takes into consideration the health and fatigue level of the entire family.

[0041] The allocation plan proposal unit can optimize tasks that require going outside by linking with local events and weather information. For example, the generation AI in the allocation plan proposal unit takes into account local events and weather information and proposes an allocation plan that optimizes tasks that require going outside. For example, indoor tasks are prioritized on days with bad weather. Also, based on local event information, it proposes an allocation plan that optimizes tasks that require going outside. For example, going outside is avoided on days when there is a local event. Also, based on weather information, it proposes an allocation plan that optimizes tasks that require going outside. For example, going outside is prioritized on days with good weather. In this way, by linking with local events and weather information, it is possible to optimize tasks that require going outside.

[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 household chore and childcare task visualization feature can track task progress in real time and automatically remind you of incomplete tasks. For example, if cleaning is not completed, a reminder notification is sent. To track task progress, a shared task list can be created that all family members can access, allowing each member to update their task completion status. For example, a smartphone app can be used to manage task progress. The incomplete task reminder function has also been enhanced, allowing you to customize the frequency and timing of reminders. For example, you can set frequent reminders for important tasks. This will help improve task completion rates by reminding you of incomplete tasks.

[0044] The household chore and childcare task visualization unit can use the voice input function to allow users to add and edit tasks by voice. For example, simply saying "add cleaning" will add it to the task list. The voice input function has also been enhanced to allow users to specify task details by voice. For example, by saying "add taking out the trash at 8 a.m. tomorrow," specific times and contents can be set. Voice recognition technology is also used to accurately analyze the user's voice instructions and reflect them in the task list. For example, adding multiple tasks at once can be easily done by voice input. This allows users to easily add and edit tasks using the voice input function.

[0045] The housework and childcare task visualization unit works in conjunction with smart home devices to automatically reflect the usage status of home appliances in the task list. For example, when the washing machine is running, "Laundry completed" is automatically added to the list. The progress of housework and childcare tasks is also updated in real time based on data from smart home devices. For example, when a cleaning robot finishes cleaning, "Cleaning completed" is reflected in the list. Furthermore, by reflecting the usage status of home appliances in the task list, housework and childcare tasks can be visualized more accurately. For example, a "shopping list" can be automatically generated based on the inventory status of the refrigerator. This allows the usage status of home appliances to be automatically reflected in the task list, making housework and childcare tasks more accurately visualized.

[0046] The schedule analysis unit can learn from past schedule data and predict future schedules. For example, it can automatically generate the next week's schedule based on past patterns. It can also analyze past schedule data and predict the frequency of specific events and tasks. For example, it can automatically add a regular meeting that takes place every Friday to the schedule. The generation AI can also predict future schedules based on past schedule data and propose optimal task allocation. For example, it can allocate tasks that avoid busy times based on past data. This allows for more accurate schedule management by learning from past schedule data and predicting future schedules.

[0047] The schedule analysis unit can automatically notify and reanalyze if there is a change in the schedule. For example, if a meeting time is changed, the schedule is automatically updated. Furthermore, if there is a change in the schedule, the generation AI reanalyzes and re-proposes the optimal task allocation. For example, it adjusts task allocation in response to sudden schedule changes. In addition, a function will be added where the generation AI detects schedule changes in real time and automatically sends notifications. For example, if an appointment is canceled, a notification will be sent immediately. This will improve the accuracy of schedule management by automatically notifying and reanalyzing if there is a change in the schedule.

[0048] The schedule analysis unit can analyze the schedules of the entire family in an integrated manner, including the schedules of children in addition to the analysis of the couple's schedules. For example, it can integrate schedules of children's school events and extracurricular activities. It can also analyze the schedules of the entire family and propose optimal task allocation. For example, it can allocate tasks taking into account the schedules of all family members. It can also visualize the schedules of the entire family, including the children's schedules, so that all family members can access them. For example, it can manage schedules using a shared calendar. This allows for an integrated analysis of the schedules of the entire family, enabling more accurate schedule management.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The household chore and childcare task visualization unit visualizes household chore and childcare tasks. For example, the generation AI lists the household chore and childcare tasks to be performed at home and visualizes the content and required time for each task. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI lists tasks based on that prompt. Step 2: The schedule analysis unit analyzes the couple's schedule. For example, the generation AI analyzes each spouse's schedule and identifies free and busy times. The input to the generation AI is the couple's schedule information, and the generation AI analyzes the schedule based on that information. Step 3: The division of labor proposal unit proposes an optimal division of labor plan based on the information obtained by the household chore and childcare task visualization unit and the schedule analysis unit. For example, the generation AI proposes an optimal daily division of labor plan based on the visualized household chore and childcare tasks and the couple's schedule. The input to the generation AI is information about the household chore and childcare tasks and the couple's schedule, and the generation AI generates a division of labor plan based on that information.

[0051] (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, and proposes optimal sharing plans. 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.

[0052] A housework and childcare sharing proposal system according to an embodiment includes a housework and childcare task visualization unit, a schedule analysis unit, and a sharing plan proposal unit. The housework and childcare task visualization unit visualizes housework and childcare tasks. For example, the generation AI lists the housework and childcare tasks performed at home and visualizes the content and required time of each task. The generation AI receives input from a user prompt containing instructions on what the user wants the generation AI to do, and the generation AI lists tasks based on the prompt. The schedule analysis unit analyzes the couple's schedule. For example, the generation AI analyzes each spouse's schedule to determine free and busy times. The generation AI receives input from the couple's schedule information, and the generation AI analyzes the schedule based on that information. The sharing plan proposal unit proposes an optimal sharing plan based on the information obtained by the housework and childcare task visualization unit and the schedule analysis unit. For example, the generation AI proposes an optimal daily sharing plan based on the visualized housework and childcare tasks and the couple's schedule. The AI ​​generation system generates a division of labor plan based on the inputs of household chores and childcare tasks and the couple's schedule. As a result, the system can fairly share the burden of household chores and childcare, promoting women's participation in the workforce.

[0053] The household and childcare task visualization unit can automatically prioritize tasks and dynamically update the list according to their importance. For example, when the generation AI visualizes household and childcare tasks, the household and childcare task visualization unit automatically evaluates the importance of each task and sets priorities. For example, tasks that are time-sensitive, such as taking children to and from school or preparing meals, are displayed first in the list. In addition, when prioritizing tasks, the system dynamically updates the list, taking into account the family's lifestyle and individual needs. For example, important tasks for specific days of the week or time periods are automatically placed at the top. The generation AI also monitors task progress in real time and resets priorities if there are any incomplete tasks. For example, if cleaning is not completed, cleaning is displayed as the next task. This allows important tasks to be prioritized by dynamically updating task priorities.

[0054] The household and childcare task visualization component can track task progress in real time and automatically remind users of incomplete tasks. For example, the generation AI tracks the progress of household and childcare tasks in real time and automatically sends reminders when there are incomplete tasks. For example, if cleaning is not completed, a reminder notification is sent. To track task progress, a shared task list accessible to all family members can be created, allowing each member to update their task completion status. For example, a smartphone app can be used to manage task progress. The incomplete task reminder function has also been enhanced, allowing users to customize the frequency and timing of reminders. For example, users can set frequent reminders for important tasks. This improves task completion rates by reminding users of incomplete tasks.

[0055] The household chore and childcare task visualization unit can use the emotion estimation function to analyze the user's emotional state and prioritize visualization of tasks that are estimated to be high stress. The household chore and childcare task visualization unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and prioritize displaying tasks that are estimated to be high stress in a list. For example, tasks that are likely to cause the user stress may be placed at the top. Furthermore, based on the user's emotion data, tasks that are estimated to be high stress may be identified and support and advice for those tasks may be provided. For example, tips for reducing stress may be displayed. Furthermore, the emotion estimation function may be used to periodically monitor the user's emotional state and prioritize visualization of tasks that are estimated to be high stress. For example, if the user's emotion score is low, relaxing tasks may be suggested. In this way, by prioritizing visualization of tasks that are high stress, the user's stress is reduced.

[0056] The household chore and childcare task visualization unit can use a voice input function to allow users to add and edit tasks by voice. For example, the household chore and childcare task visualization unit adds a voice input function to the household chore and childcare task visualization unit, allowing users to add and edit tasks by voice. For example, simply saying "add cleaning" adds a task to the task list. The voice input function can also be enhanced to allow users to specify task details by voice. For example, by saying "add taking out the trash at 8 a.m. tomorrow," specific times and contents can be set. Furthermore, voice recognition technology can be used to accurately analyze the user's voice instructions and reflect them in the task list. For example, adding multiple tasks at once can be easily done by voice input. This allows users to easily add and edit tasks using the voice input function.

[0057] The household chore and childcare task visualization unit can work in conjunction with smart home devices to automatically reflect the usage status of home appliances in the task list. For example, the household chore and childcare task visualization unit can link visualization of household chore and childcare tasks with smart home devices and automatically reflect the usage status of home appliances in the task list. For example, when a washing machine is running, "laundry completed" is automatically added to the list. In addition, the progress of household chore and childcare tasks is updated in real time based on data from smart home devices. For example, when a cleaning robot finishes cleaning, "cleaning completed" is reflected in the list. In addition, by reflecting the usage status of home appliances in the task list, the visualization of household chore and childcare tasks can be made more accurate. For example, a "shopping list" can be automatically generated based on the inventory status of the refrigerator. In this way, the usage status of home appliances can be automatically reflected in the task list, making the visualization of household chore and childcare tasks more accurate.

[0058] The household chore and childcare task visualization unit can use the emotion estimation function to analyze the emotions of the user when entering tasks in real time and make suggestions to elicit positive emotions. The household chore and childcare task visualization unit, for example, uses the emotion estimation function to analyze the emotions of the user when entering tasks in real time and make suggestions to elicit positive emotions. For example, if the user is feeling stressed, it can suggest tasks that will help them relax. It also provides an interface to elicit positive emotions when entering tasks based on the user's emotional state. For example, it can present encouraging messages and success stories. It also provides advice to elicit positive emotions when the user enters tasks based on the emotion estimation data. For example, it can display appropriate encouragement or praise according to the input content. In this way, it analyzes the user's emotions and elicits positive emotions, thereby reducing the stress of entering tasks.

[0059] The schedule analysis unit can learn from past schedule data and predict future schedules. For example, the schedule analysis unit adds a function where the generation AI learns the past schedule data of a couple and predicts future schedules. For example, it automatically generates the next week's schedule based on past patterns. It also analyzes past schedule data and predicts the frequency of specific events and tasks. For example, it automatically adds a regular meeting that takes place every Friday to the schedule. The generation AI also predicts future schedules based on past schedule data and suggests optimal task allocation. For example, it allocates tasks that avoid busy times based on past data. This allows for more accurate schedule management by learning from past schedule data and predicting future schedules.

[0060] The schedule analysis unit can automatically notify and reanalyze if there is a change in the schedule. The schedule analysis unit adds a function that allows the generation AI to automatically notify and reanalyze if there is a change in a couple's schedule, for example. For example, if a meeting time is changed, the schedule is automatically updated. In addition, if there is a change in the schedule, the generation AI reanalyzes and re-proposes the optimal task allocation. For example, it adjusts task allocation in response to sudden changes in plans. In addition, a function will be added that allows the generation AI to detect schedule changes in real time and automatically send notifications. For example, if an appointment is canceled, a notification will be sent immediately. This will improve the accuracy of schedule management by automatically notifying and reanalyzing if there is a change in the schedule.

[0061] The schedule analysis unit can use the emotion estimation function to identify time periods estimated to be high stress based on the results of schedule analysis, and make suggestions to avoid those time periods. The schedule analysis unit, for example, uses the emotion estimation function to identify time periods estimated to be high stress based on the results of schedule analysis. For example, it detects that stress is high during specific time periods based on past data. It also makes suggestions to avoid time periods estimated to be high stress. For example, it suggests tasks that allow you to relax during high stress time periods. It also adjusts the schedule analysis results based on the emotion estimation data and makes suggestions to avoid high stress time periods. For example, it avoids important tasks during high stress time periods. In this way, suggestions to avoid high stress time periods are made, thereby reducing the user's stress.

[0062] The schedule analysis unit can analyze the schedules of the entire family in an integrated manner, including the schedules of children in addition to the analysis of the couple's schedule. The schedule analysis unit adds a function to analyze the schedules of the entire family in an integrated manner, including the schedules of children in addition to the analysis of the couple's schedule. For example, it integrates the schedules of children's school events and extracurricular activities. It also analyzes the schedules of the entire family and suggests optimal task allocation. For example, it allocates tasks taking into consideration the schedules of all family members. It also makes the schedules of the entire family, including the schedules of children, visible so that all family members can access them. For example, it manages schedules using a shared calendar. This allows for integrated analysis of the schedules of the entire family, enabling more accurate schedule management.

[0063] The schedule analysis unit can link the couple's schedule analysis with the workplace scheduling system to take work schedules into account. For example, the schedule analysis unit can link the couple's schedule analysis with the workplace scheduling system to take work schedules into account. For example, it can reflect workplace meetings and business trip plans in the schedule. It can also link with the workplace scheduling system to suggest optimal task allocation based on the work schedule. For example, it can reduce housework and childcare tasks during busy hours. It can also obtain data from the workplace scheduling system and reflect it in the couple's schedule analysis. For example, it can link with the workplace calendar to automatically update the schedule. This allows for more accurate schedule management by taking work schedules into account.

[0064] The schedule analysis unit can use the emotion estimation function to analyze the user's emotions when entering a schedule and propose a schedule that elicits positive emotions. The schedule analysis unit, for example, uses the emotion estimation function to analyze the user's emotions in real time when entering a schedule and propose a schedule that elicits positive emotions. For example, it places tasks in time periods when the user can relax. It also provides an interface for eliciting positive emotions when entering a schedule based on the user's emotional state. For example, it presents encouraging messages and success stories. It also provides advice for eliciting positive emotions when the user enters a schedule based on the emotion estimation data. For example, it displays appropriate encouragement or praise according to the input content. In this way, it analyzes the user's emotions and elicits positive emotions, thereby reducing the stress of entering a schedule.

[0065] The sharing plan proposal unit can learn from past sharing history and generate a fairer sharing plan. For example, the sharing plan proposal unit adds a function that allows the generation AI to learn from past sharing history and generate a fairer sharing plan. For example, it adjusts the sharing plan based on past data so that the husband and wife's burden is equal. It also analyzes past sharing history and, if a particular task is unevenly distributed, proposes to divide that task fairly. For example, if a particular task is unevenly distributed, it will be adjusted in the next sharing plan. It also generates a sharing plan based on past sharing history so that the husband and wife's burden is equal. For example, it assigns tasks based on past data so that the husband and wife's burden is equal. In this way, the generation AI can learn from past sharing history and generate a fairer sharing plan, thereby enabling the burden of housework and childcare to be shared equally.

[0066] The sharing plan proposal unit can adjust the burden so that it is evenly distributed, taking into account the difficulty and time required for the task. For example, the sharing plan proposal unit adds a function whereby the generation AI considers the difficulty and time required for the task and adjusts the sharing plan so that it is evenly distributed. For example, it distributes highly difficult tasks evenly. It also adjusts the sharing plan so that the burden between husband and wife is equal, based on the time required for the task. For example, it distributes tasks that require a long time evenly. The generation AI also analyzes the difficulty and time required for the task and generates a sharing plan so that the burden is evenly distributed. For example, it distributes highly difficult tasks evenly. In this way, it takes the difficulty and time required for the task into account and adjusts the burden so that it is evenly distributed, allowing for a fair sharing of the burden of housework and childcare.

[0067] The sharing plan proposal unit can use the emotion estimation function to analyze the user's emotional response to the proposed sharing plan, and prioritize proposing sharing plans that will elicit a positive response. The sharing plan proposal unit, for example, uses the emotion estimation function to analyze the user's emotional response to the proposed sharing plan in real time, and prioritize proposing sharing plans that will elicit a positive response. For example, it prioritizes proposing sharing plans that satisfy the user. Furthermore, based on the user's emotional response data, it identifies sharing plans that will elicit a positive response and prioritizes proposing those sharing plans. For example, it proposes sharing plans that do not cause the user stress. Furthermore, based on the emotion estimation data, it analyzes the user's emotional response, and prioritizes proposing sharing plans that will elicit a positive response. For example, it proposes sharing plans that allow the user to relax. In this way, by analyzing the user's emotional response and preferentially proposing sharing plans that will elicit a positive response, user satisfaction is improved.

[0068] The task sharing plan proposal unit can propose an optimal task sharing plan by taking into consideration the health and fatigue level of the entire family. The task sharing plan proposal unit adds a function in which the generation AI proposes an optimal task sharing plan by taking into consideration the health and fatigue level of the entire family. For example, it assigns lighter tasks to members who are highly fatigued. It also adjusts the task sharing plan so that the burden on the entire family is equal based on the health and fatigue level. For example, it assigns lighter tasks to members who are not in good health. The generation AI also analyzes the health and fatigue level of the entire family and proposes an optimal task sharing plan. For example, it assigns lighter tasks to members who are highly fatigued. In this way, the burden on the entire family is reduced by proposing a task sharing plan that takes into consideration the health and fatigue level of the entire family.

[0069] The allocation plan proposal unit can optimize tasks that require going outside by linking with local events and weather information. For example, the generation AI in the allocation plan proposal unit takes into account local events and weather information and proposes an allocation plan that optimizes tasks that require going outside. For example, indoor tasks are prioritized on days with bad weather. Also, based on local event information, it proposes an allocation plan that optimizes tasks that require going outside. For example, going outside is avoided on days when there is a local event. Also, based on weather information, it proposes an allocation plan that optimizes tasks that require going outside. For example, going outside is prioritized on days with good weather. In this way, by linking with local events and weather information, it is possible to optimize tasks that require going outside.

[0070] The sharing plan proposal unit can use the emotion estimation function to analyze the user's emotions in real time when proposing a sharing plan, and make a proposal that elicits positive emotions. The sharing plan proposal unit, for example, uses the emotion estimation function to analyze the user's emotions in real time when proposing a sharing plan, and make a proposal that elicits positive emotions. For example, it proposes a sharing plan that satisfies the user. Furthermore, based on the user's emotional state, it provides an interface for eliciting positive emotions when proposing a sharing plan. For example, it presents encouraging messages and success stories. Furthermore, based on the emotion estimation data, it provides advice to make the user more likely to accept the sharing plan. For example, it displays appropriate encouragement or praise according to the input content. In this way, the user's emotions are analyzed in real time and positive emotions are elicited, thereby improving the ease of acceptance of the sharing plan.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The household chore and childcare task visualization feature can track task progress in real time and automatically remind you of incomplete tasks. For example, if cleaning is not completed, a reminder notification is sent. To track task progress, a shared task list can be created that all family members can access, allowing each member to update their task completion status. For example, a smartphone app can be used to manage task progress. The incomplete task reminder function has also been enhanced, allowing you to customize the frequency and timing of reminders. For example, you can set frequent reminders for important tasks. This will help improve task completion rates by reminding you of incomplete tasks.

[0073] The household chore and childcare task visualization unit can use the voice input function to allow users to add and edit tasks by voice. For example, simply saying "add cleaning" will add it to the task list. The voice input function has also been enhanced to allow users to specify task details by voice. For example, by saying "add taking out the trash at 8 a.m. tomorrow," specific times and contents can be set. Voice recognition technology is also used to accurately analyze the user's voice instructions and reflect them in the task list. For example, adding multiple tasks at once can be easily done by voice input. This allows users to easily add and edit tasks using the voice input function.

[0074] The housework and childcare task visualization unit works in conjunction with smart home devices to automatically reflect the usage status of home appliances in the task list. For example, when the washing machine is running, "Laundry completed" is automatically added to the list. The progress of housework and childcare tasks is also updated in real time based on data from smart home devices. For example, when a cleaning robot finishes cleaning, "Cleaning completed" is reflected in the list. Furthermore, by reflecting the usage status of home appliances in the task list, housework and childcare tasks can be visualized more accurately. For example, a "shopping list" can be automatically generated based on the inventory status of the refrigerator. This allows the usage status of home appliances to be automatically reflected in the task list, making housework and childcare tasks more accurately visualized.

[0075] The household and childcare task visualization unit uses the emotion estimation function to analyze the user's emotional state and prioritize visualizing tasks that are estimated to be high stress. For example, it places tasks that are likely to cause stress at the top. It also identifies tasks that are estimated to be high stress based on the user's emotional data and provides support and advice for those tasks. For example, it displays tips for reducing stress. It also regularly monitors the user's emotional state using the emotion estimation function and prioritizes visualizing tasks that are estimated to be high stress. For example, if the user's emotion score is low, it suggests tasks that will help them relax. This prioritizes visualizing tasks that are high stress, thereby reducing the user's stress.

[0076] The household and childcare task visualization unit uses the emotion estimation function to analyze the user's emotions in real time when entering tasks and can make suggestions to elicit positive emotions. For example, if the user is feeling stressed, it can suggest relaxing tasks. It also provides an interface to elicit positive emotions when entering tasks based on the user's emotional state. For example, it can present encouraging messages and success stories. It also provides advice to elicit positive emotions when the user enters tasks based on the emotion estimation data. For example, it can display appropriate encouragement or praise according to the input content. In this way, it analyzes the user's emotions and elicits positive emotions, thereby reducing the stress of entering tasks.

[0077] The schedule analysis unit can learn from past schedule data and predict future schedules. For example, it can automatically generate the next week's schedule based on past patterns. It can also analyze past schedule data and predict the frequency of specific events and tasks. For example, it can automatically add a regular meeting that takes place every Friday to the schedule. The generation AI can also predict future schedules based on past schedule data and propose optimal task allocation. For example, it can allocate tasks that avoid busy times based on past data. This allows for more accurate schedule management by learning from past schedule data and predicting future schedules.

[0078] The schedule analysis unit can automatically notify and reanalyze if there is a change in the schedule. For example, if a meeting time is changed, the schedule is automatically updated. Furthermore, if there is a change in the schedule, the generation AI reanalyzes and re-proposes the optimal task allocation. For example, it adjusts task allocation in response to sudden schedule changes. In addition, a function will be added where the generation AI detects schedule changes in real time and automatically sends notifications. For example, if an appointment is canceled, a notification will be sent immediately. This will improve the accuracy of schedule management by automatically notifying and reanalyzing if there is a change in the schedule.

[0079] The schedule analysis unit can use the emotion estimation function to identify time periods estimated to be high stress based on the results of schedule analysis, and make suggestions to avoid those time periods. For example, it can detect that stress is high during specific time periods based on past data. It can also make suggestions to avoid time periods estimated to be high stress. For example, it can suggest relaxing tasks during high stress times. It can also adjust the schedule analysis results based on the emotion estimation data, and make suggestions to avoid high stress time periods. For example, it can avoid important tasks during high stress time periods. In this way, suggestions to avoid high stress time periods are made, thereby reducing the user's stress.

[0080] The schedule analysis unit can analyze the schedules of the entire family in an integrated manner, including the schedules of children in addition to the analysis of the couple's schedules. For example, it can integrate schedules of children's school events and extracurricular activities. It can also analyze the schedules of the entire family and propose optimal task allocation. For example, it can allocate tasks taking into account the schedules of all family members. It can also visualize the schedules of the entire family, including the children's schedules, so that all family members can access them. For example, it can manage schedules using a shared calendar. This allows for an integrated analysis of the schedules of the entire family, enabling more accurate schedule management.

[0081] The sharing plan proposing unit uses the emotion estimation function to analyze the user's emotional response to the proposed sharing plan, and can prioritize proposing sharing plans that will elicit a positive response. For example, it prioritizes proposing sharing plans that will satisfy the user. Furthermore, it identifies sharing plans that will elicit a positive response based on the user's emotional response data, and prioritizes proposing those sharing plans. For example, it proposes sharing plans that will not cause the user stress. Furthermore, it analyzes the user's emotional response based on the emotion estimation data, and prioritizes proposing sharing plans that will elicit a positive response. For example, it proposes sharing plans that will allow the user to relax. In this way, by analyzing the user's emotional response and prioritize proposing sharing plans that will elicit a positive response, user satisfaction is improved.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The household chore and childcare task visualization unit visualizes household chore and childcare tasks. For example, the generation AI lists the household chore and childcare tasks to be performed at home and visualizes the content and required time for each task. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI lists tasks based on that prompt. Step 2: The schedule analysis unit analyzes the couple's schedule. For example, the generation AI analyzes each spouse's schedule and identifies free and busy times. The input to the generation AI is the couple's schedule information, and the generation AI analyzes the schedule based on that information. Step 3: The division of labor proposal unit proposes an optimal division of labor plan based on the information obtained by the household chore and childcare task visualization unit and the schedule analysis unit. For example, the generation AI proposes an optimal daily division of labor plan based on the visualized household chore and childcare tasks and the couple's schedule. The input to the generation AI is information about the household chore and childcare tasks and the couple's schedule, and the generation AI generates a division of labor plan based on that information.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 housework and childcare 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 information obtained by the housework and childcare task visualization unit and the schedule analysis unit. A system characterized by:

2. The housework and childcare task visualization unit Automatically prioritize tasks and dynamically update the list based on importance 2. The system of claim 1.

3. The housework and childcare task visualization unit Track task progress in real time and automatically remind you of incomplete tasks 2. The system of claim 1.

4. The housework and childcare task visualization unit Analyzes the user's emotional state and prioritizes tasks that are estimated to be stressful.

2. The system of claim 1.

5. The housework and childcare task visualization unit Use voice input to allow users to add and edit tasks by voice 2. The system of claim 1.

6. The housework and childcare task visualization unit Link with smart home devices to automatically update your task list with the usage of your home appliances 2. The system of claim 1.

7. The housework and childcare task visualization unit Analyzes emotions in real time as users enter tasks and makes suggestions to elicit positive emotions 2. The system of claim 1.

8. The schedule analysis unit Learn from past schedule data and predict future schedules 2. The system of claim 1.

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