System

Abstract

An object of a system according to an embodiment is to perform housework efficiently and fairly by visualizing the sharing and progress status of the housework.SOLUTION: A system includes a housekeeping recording part, a housekeeping visualization part, a housekeeping sharing management part, and a progress management part. The housework recording part records an execution state of housework. The housework visualizing part visualizes housework to be done now on the basis of the execution situation of the housework recorded by the housework recording part. A housework sharing management part manages the sharing situation of housework. The progress manager tracks the progress of household chores.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology makes it difficult to see the division of household chores and their progress, making it difficult to improve efficiency and ensure fair division of household chores.

[0005] The system according to the embodiment aims to visualize the division of household chores and their progress, and to perform household chores efficiently and fairly. [Means for solving the problem]

[0006] The system according to the embodiment includes a housework recording unit, a housework visualization unit, a housework allocation management unit, and a progress management unit. The housework recording unit records the status of housework being performed. The housework visualization unit visualizes the housework that needs to be performed now based on the status of housework being performed recorded by the housework recording unit. The housework allocation management unit manages the status of housework allocation. The progress management unit tracks the progress of housework. [Effects of the Invention]

[0007] The system according to the embodiment visualizes the division of household chores and their progress, enabling household chores to be performed efficiently and fairly. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) A housework schedule efficiency improvement system according to an embodiment of the present invention is a system that improves the efficiency of housework schedules for people who are not good at housework. This system uses an AI housework assistant to record the housework being performed by couples, roommates, and housemates, and visualizes the housework that needs to be done now, thereby improving the efficiency of housework and eliminating discrepancies in understanding. As a result, the housework schedule efficiency improvement system allows even people who are not good at housework to perform housework efficiently and eliminates discrepancies in understanding between couples, roommates, and housemates.

[0029] A housework schedule efficiency improvement system according to an embodiment includes a housework recording unit, a housework visualization unit, a housework allocation management unit, and a progress management unit. The housework recording unit records the status of housework completion. For example, the housework recording unit stores the time, frequency, and completion status of housework in a database. The housework recording unit can also estimate the emotions of the person performing the housework and calculate an emotion score. The housework visualization unit visualizes the housework that needs to be done now based on the status of housework completion recorded by the housework recording unit. For example, the housework visualization unit lists the housework that needs to be done now, taking into account the priority and urgency of the housework. The housework visualization unit can also suggest the optimal timing for housework completion, taking into account the environment in which the housework is performed (such as the weather, time of day, and whether family members are at home). The housework allocation management unit manages the status of housework assignment. For example, the housework allocation management unit records the person responsible for housework and the allocation ratio, and tracks the progress. The housework allocation management unit can also estimate the emotions of each member and adjust the assignment based on their emotions. The progress management unit tracks the progress of housework. For example, the progress management unit proposes an optimal allocation schedule taking into account the frequency of housework and past performance. The progress management unit can also monitor the progress of housework in real time and automatically redistribute tasks to other members if progress is behind. This allows the housework schedule efficiency system according to the embodiment to eliminate discrepancies in housework efficiency and awareness. For example, the housework progress can be linked to a smartphone app or wearable device, allowing progress to be checked even when away from home. The allocation and progress management of housework can be applied not only within the home but also in shared houses, student dormitories, and other places where people live together, thereby improving the efficiency of communal living.

[0030] The housework recording unit records the time and required time for housework in detail, and can suggest an efficient housework schedule. For example, the housework recording unit records the time and required time for housework in detail and stores the data in a database. For example, the time required for cleaning and the time required for food preparation are recorded in minutes. The housework recording unit also suggests an efficient housework schedule based on the recorded data. For example, it suggests that time can be saved by doing laundry and cleaning at the same time. This makes it possible to suggest an efficient housework schedule.

[0031] The household chore recording unit can suggest the timing of household chores taking into account the environment in which the chores are performed. For example, the household chore recording unit monitors the environment in which the chores are performed in real time and suggests a household chore schedule taking into account the weather and time of day. For example, it suggests doing laundry on a sunny day. The household chore recording unit also adjusts the timing of household chores taking into account whether or not family members are at home. For example, it suggests cleaning common spaces when all family members are at home. This makes it possible to suggest the optimal timing for household chores.

[0032] The household chore recording unit and the household chore visualization unit can be applied to other environments, such as the workplace and school, to perform task management, not just within the home. For example, the household chore recording unit and the household chore visualization unit can also apply the household chore recording and visualization system to the workplace or school to perform comprehensive task management. For example, they can record and visualize workplace cleaning and equipment management. Furthermore, the household chore recording unit and the household chore visualization unit can manage workplace and school tasks and track their progress. For example, they can check the progress of workplace tasks in real time and send reminders as needed. This allows for comprehensive task management.

[0033] The household chore recording unit can link with smart home devices to reflect the operating status of home appliances and the inventory status of consumables in real time. For example, the household chore recording unit links a household chore recording system with smart home devices to reflect the operating status of home appliances in real time. For example, it records the operating status of a washing machine and notifies the user when the laundry is complete. The household chore recording unit also reflects the inventory status of consumables in real time. For example, it records the inventory of detergent and toilet paper and suggests replenishing when stocks are low. This makes it possible to reflect the operating status of home appliances and the inventory status of consumables in real time.

[0034] The progress management unit can propose a division of labor schedule taking into consideration the frequency of housework and past performance. The progress management unit, for example, records the frequency of housework and past performance in detail and saves it in a database. For example, it records the frequency of cleaning and cooking performance. The progress management unit also proposes an optimal division of labor schedule based on the recorded data. For example, members who clean frequently are assigned other housework. This makes it possible to propose an optimal division of labor schedule.

[0035] The progress management unit monitors the progress in real time, and can automatically redistribute tasks to other members if progress is behind. The progress management unit, for example, monitors the progress of housework in real time, and sends a notification if progress is behind. For example, if cleaning is not progressing as planned, a reminder is sent. The progress management unit also automatically redistributes tasks to other members if progress is behind. For example, if cleaning is behind, cleaning is assigned to another member. In this way, tasks can be automatically redistributed if progress is behind.

[0036] The housework allocation management unit and progress management unit can be applied not only within a home but also in shared houses and student dormitories where people live together, thereby improving the efficiency of communal living. For example, the housework allocation management unit and progress management unit can be applied to shared houses and student dormitories to improve the efficiency of communal living. For example, the housework allocation management unit and progress management unit can manage the allocation of responsibilities for cleaning common spaces and taking out the trash. In addition, in order to improve the efficiency of communal living, the housework allocation management unit and progress management unit can monitor the progress of housework in real time and send reminders as necessary. This can improve the efficiency of communal living.

[0037] The housework sharing management unit can link with a smartphone app or wearable device, allowing progress to be checked even when away from home. For example, the housework sharing management unit links the housework sharing status with a smartphone app, allowing progress to be checked even when away from home. For example, the app displays the housework progress in real time. The housework sharing management unit also links with a wearable device to notify progress. For example, the housework progress can be checked on a smartwatch and reminders can be received. This allows progress to be checked even when away from home.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The housework schedule efficiency improvement system can further include a health management unit. The health management unit monitors the user's health condition and adjusts the housework schedule. For example, it records the user's heart rate and sleep time, and makes suggestions to reduce the burden of housework if fatigue accumulates. The health management unit can also make housework suggestions to support healthy lifestyle habits based on the user's diet and exercise records. This not only improves the efficiency of housework, but also supports the user's health management.

[0040] The housework schedule efficiency improvement system may further include an entertainment provider. The entertainment provider provides content that the user can enjoy while doing housework. For example, it may play music or podcasts to coincide with the time spent doing housework. The entertainment provider may also suggest movies or dramas to watch based on the user's preferences. Furthermore, it may provide games or quizzes according to the progress of housework, making housework more enjoyable. This reduces the burden of housework and allows the user to do it while having fun.

[0041] The housework schedule efficiency improvement system may further include a communication promotion unit. The communication promotion unit promotes communication by sharing the progress and allocation of housework with family members and housemates. For example, the progress of housework may be shared in real time so that all family members can understand the status of the housework. The communication promotion unit may also provide a chat function for giving feedback and exchanging opinions regarding housework. Furthermore, it may send notifications regarding the allocation and progress of housework so that all family members can work together to do the housework. This facilitates communication regarding housework and promotes cooperation among family members.

[0042] The housework schedule efficiency improvement system can further include an energy management unit. The energy management unit monitors energy consumption associated with housework and suggests efficient energy use. For example, it records the usage of home appliances and suggests performing housework during times of low energy consumption. The energy management unit can also provide advice on reducing energy consumption. Furthermore, the energy management unit can promote the use of renewable energy and support environmentally friendly housework. This not only improves the efficiency of housework, but also achieves efficient energy use and environmental protection.

[0043] The housework schedule efficiency improvement system may further include a learning support unit. The learning support unit supports the user in acquiring new skills and knowledge through housework. For example, it may provide cooking recipes and cleaning techniques so that the user can learn while doing housework. The learning support unit may also suggest online courses and workshops related to housework. Furthermore, the learning support unit may provide feedback to the user according to the progress of the housework and support the improvement of skills. This not only enables the user to perform housework efficiently, but also provides the user with opportunities to acquire new skills and knowledge.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The housework recording unit records the status of housework. For example, the housework recording unit stores the time, frequency, and completion status of housework in a database. The housework recording unit can also estimate the emotions of the person performing the housework and calculate an emotion score. Step 2: The housework visualization unit visualizes the housework that needs to be done now based on the housework status recorded by the housework recording unit. For example, the housework visualization unit makes a list of the housework that needs to be done now, taking into account the priority and urgency of the housework. The housework visualization unit can also suggest the optimal timing for housework, taking into account the environment in which the housework is being done (weather, time of day, whether family members are at home, etc.). Step 3: The household chore management unit manages the household chore allocation status. For example, the household chore allocation management unit records who is responsible for household chores and their share, and tracks progress. The household chore allocation management unit can also estimate each member's emotions and adjust the allocation based on their emotions. Step 4: The progress management unit tracks the progress of housework. For example, the progress management unit may propose an optimal schedule for dividing up household chores, taking into account the frequency of chores and past performance. The progress management unit can also monitor the progress of housework in real time and automatically redistribute tasks to other members if progress is behind schedule.

[0046] (Example 2) A housework schedule efficiency improvement system according to an embodiment of the present invention is a system that improves the efficiency of housework schedules for people who are not good at housework. This system uses an AI housework assistant to record the housework being performed by couples, roommates, and housemates, and visualizes the housework that needs to be done now, thereby improving the efficiency of housework and eliminating discrepancies in understanding. As a result, the housework schedule efficiency improvement system allows even people who are not good at housework to perform housework efficiently and eliminates discrepancies in understanding between couples, roommates, and housemates.

[0047] A housework schedule efficiency improvement system according to an embodiment includes a housework recording unit, a housework visualization unit, a housework allocation management unit, and a progress management unit. The housework recording unit records the status of housework completion. For example, the housework recording unit stores the time, frequency, and completion status of housework in a database. The housework recording unit can also estimate the emotions of the person performing the housework and calculate an emotion score. The housework visualization unit visualizes the housework that needs to be done now based on the status of housework completion recorded by the housework recording unit. For example, the housework visualization unit lists the housework that needs to be done now, taking into account the priority and urgency of the housework. The housework visualization unit can also suggest the optimal timing for housework completion, taking into account the environment in which the housework is performed (such as the weather, time of day, and whether family members are at home). The housework allocation management unit manages the status of housework assignment. For example, the housework allocation management unit records the person responsible for housework and the allocation ratio, and tracks the progress. The housework allocation management unit can also estimate the emotions of each member and adjust the assignment based on their emotions. The progress management unit tracks the progress of housework. For example, the progress management unit proposes an optimal allocation schedule taking into account the frequency of housework and past performance. The progress management unit can also monitor the progress of housework in real time and automatically redistribute tasks to other members if progress is behind. This allows the housework schedule efficiency system according to the embodiment to eliminate discrepancies in housework efficiency and awareness. For example, the housework progress can be linked to a smartphone app or wearable device, allowing progress to be checked even when away from home. The allocation and progress management of housework can be applied not only within the home but also in shared houses, student dormitories, and other places where people live together, thereby improving the efficiency of communal living.

[0048] The housework recording unit can estimate the emotions of the person performing the housework and adjust the priority of the housework based on the emotions. For example, when recording the housework, the housework recording unit analyzes the facial expressions and voice of the person performing the housework to estimate the emotions. For example, it detects stress and fatigue when performing the housework and calculates an emotion score. The housework recording unit also adjusts the priority of the housework based on the emotion score. For example, if stress is high, it preferentially suggests housework that is relaxing. In this way, the priority of the housework can be adjusted based on emotions.

[0049] The housework recording unit records the time and required time for housework in detail, and can suggest an efficient housework schedule. For example, the housework recording unit records the time and required time for housework in detail and stores the data in a database. For example, the time required for cleaning and the time required for food preparation are recorded in minutes. The housework recording unit also suggests an efficient housework schedule based on the recorded data. For example, it suggests that time can be saved by doing laundry and cleaning at the same time. This makes it possible to suggest an efficient housework schedule.

[0050] The household chore recording unit can suggest the timing of household chores taking into account the environment in which the chores are performed. For example, the household chore recording unit monitors the environment in which the chores are performed in real time and suggests a household chore schedule taking into account the weather and time of day. For example, it suggests doing laundry on a sunny day. The household chore recording unit also adjusts the timing of household chores taking into account whether or not family members are at home. For example, it suggests cleaning common spaces when all family members are at home. This makes it possible to suggest the optimal timing for household chores.

[0051] The household chore recording unit and the household chore visualization unit can be applied to other environments, such as the workplace and school, to perform task management, not just within the home. For example, the household chore recording unit and the household chore visualization unit can also apply the household chore recording and visualization system to the workplace or school to perform comprehensive task management. For example, they can record and visualize workplace cleaning and equipment management. Furthermore, the household chore recording unit and the household chore visualization unit can manage workplace and school tasks and track their progress. For example, they can check the progress of workplace tasks in real time and send reminders as needed. This allows for comprehensive task management.

[0052] The household chore recording unit can link with smart home devices to reflect the operating status of home appliances and the inventory status of consumables in real time. For example, the household chore recording unit links a household chore recording system with smart home devices to reflect the operating status of home appliances in real time. For example, it records the operating status of a washing machine and notifies the user when the laundry is complete. The household chore recording unit also reflects the inventory status of consumables in real time. For example, it records the inventory of detergent and toilet paper and suggests replenishing when stocks are low. This makes it possible to reflect the operating status of home appliances and the inventory status of consumables in real time.

[0053] The housework recording unit can use the emotion estimation function to analyze the user's emotions and make housework suggestions to elicit positive emotions. The housework recording unit, for example, uses the emotion estimation function to analyze the user's emotions in real time while recording housework. For example, it analyzes facial expressions and voices while performing housework and calculates an emotion score. The housework recording unit also makes housework suggestions to elicit positive emotions based on the emotion score. For example, it suggests relaxing or enjoyable housework. This makes it possible to make housework suggestions to elicit positive emotions.

[0054] The housework sharing management unit can estimate the emotions of each member and adjust the sharing of housework based on their emotions. For example, when managing the sharing of housework, the housework sharing management unit analyzes the facial expressions and voices of each member to estimate their emotions. For example, it detects stress and fatigue when doing housework and calculates an emotion score. The housework sharing management unit also adjusts the sharing of housework based on the emotion score. For example, it assigns less burdensome housework to members who are highly stressed. This makes it possible to adjust the sharing of housework based on their emotions.

[0055] The progress management unit can propose a division of labor schedule taking into consideration the frequency of housework and past performance. The progress management unit, for example, records the frequency of housework and past performance in detail and saves it in a database. For example, it records the frequency of cleaning and cooking performance. The progress management unit also proposes an optimal division of labor schedule based on the recorded data. For example, members who clean frequently are assigned other housework. This makes it possible to propose an optimal division of labor schedule.

[0056] The progress management unit monitors the progress in real time, and can automatically redistribute tasks to other members if progress is behind. The progress management unit, for example, monitors the progress of housework in real time, and sends a notification if progress is behind. For example, if cleaning is not progressing as planned, a reminder is sent. The progress management unit also automatically redistributes tasks to other members if progress is behind. For example, if cleaning is behind, cleaning is assigned to another member. In this way, tasks can be automatically redistributed if progress is behind.

[0057] The housework allocation management unit and progress management unit can be applied not only within a home but also in shared houses and student dormitories where people live together, thereby improving the efficiency of communal living. For example, the housework allocation management unit and progress management unit can be applied to shared houses and student dormitories to improve the efficiency of communal living. For example, the housework allocation management unit and progress management unit can manage the allocation of responsibilities for cleaning common spaces and taking out the trash. In addition, in order to improve the efficiency of communal living, the housework allocation management unit and progress management unit can monitor the progress of housework in real time and send reminders as necessary. This can improve the efficiency of communal living.

[0058] The housework sharing management unit can link with a smartphone app or wearable device, allowing progress to be checked even when away from home. For example, the housework sharing management unit links the housework sharing status with a smartphone app, allowing progress to be checked even when away from home. For example, the app displays the housework progress in real time. The housework sharing management unit also links with a wearable device to notify progress. For example, the housework progress can be checked on a smartwatch and reminders can be received. This allows progress to be checked even when away from home.

[0059] The housework sharing management unit can use the emotion estimation function to analyze the user's emotions and make suggestions for sharing chores to reduce stress. The housework sharing management unit, for example, uses the emotion estimation function to analyze the user's emotions in real time when sharing housework. For example, it analyzes facial expressions and voices when doing housework and calculates an emotion score. The housework sharing management unit also makes suggestions for sharing chores to reduce stress based on the emotion score. For example, it suggests less burdensome housework to a user who is highly stressed. This makes it possible to make suggestions for sharing chores to reduce stress.

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

[0061] The housework schedule efficiency improvement system can further include a health management unit. The health management unit monitors the user's health condition and adjusts the housework schedule. For example, it records the user's heart rate and sleep time, and makes suggestions to reduce the burden of housework if fatigue accumulates. The health management unit can also make housework suggestions to support healthy lifestyle habits based on the user's diet and exercise records. This not only improves the efficiency of housework, but also supports the user's health management.

[0062] The housework schedule efficiency improvement system may further include an entertainment provider. The entertainment provider provides content that the user can enjoy while doing housework. For example, it may play music or podcasts to coincide with the time spent doing housework. The entertainment provider may also suggest movies or dramas to watch based on the user's preferences. Furthermore, it may provide games or quizzes according to the progress of housework, making housework more enjoyable. This reduces the burden of housework and allows the user to do it while having fun.

[0063] The housework schedule efficiency improvement system may further include a communication promotion unit. The communication promotion unit promotes communication by sharing the progress and allocation of housework with family members and housemates. For example, the progress of housework may be shared in real time so that all family members can understand the status of the housework. The communication promotion unit may also provide a chat function for giving feedback and exchanging opinions regarding housework. Furthermore, it may send notifications regarding the allocation and progress of housework so that all family members can work together to do the housework. This facilitates communication regarding housework and promotes cooperation among family members.

[0064] The housework schedule efficiency improvement system can further include an energy management unit. The energy management unit monitors energy consumption associated with housework and suggests efficient energy use. For example, it records the usage of home appliances and suggests performing housework during times of low energy consumption. The energy management unit can also provide advice on reducing energy consumption. Furthermore, the energy management unit can promote the use of renewable energy and support environmentally friendly housework. This not only improves the efficiency of housework, but also achieves efficient energy use and environmental protection.

[0065] The housework schedule efficiency improvement system may further include a learning support unit. The learning support unit supports the user in acquiring new skills and knowledge through housework. For example, it may provide cooking recipes and cleaning techniques so that the user can learn while doing housework. The learning support unit may also suggest online courses and workshops related to housework. Furthermore, the learning support unit may provide feedback to the user according to the progress of the housework and support the improvement of skills. This not only enables the user to perform housework efficiently, but also provides the user with opportunities to acquire new skills and knowledge.

[0066] The housework recording unit can use the emotion estimation function to analyze the user's emotions and suggest housework to reduce stress. For example, it can analyze facial expressions and voices while performing housework and calculate an emotion score. It can also suggest relaxing or enjoyable housework based on the emotion score. For example, if stress is high, it can suggest light cleaning or housework related to a hobby. It can also use the emotion estimation function to provide positive feedback according to the progress of housework, increasing the user's motivation. This can reduce the user's stress and make housework more enjoyable.

[0067] The housework sharing management unit can use the emotion estimation function to analyze the user's emotions and adjust the allocation of housework. For example, it can analyze facial expressions and voices while performing housework and calculate an emotion score. It can also adjust the allocation of housework based on the emotion score. For example, it can assign less burdensome housework to a user who is highly stressed. It can also use the emotion estimation function to provide positive feedback according to the housework allocation status, thereby increasing the user's motivation. This allows the allocation of housework to take the user's emotions into consideration, thereby improving the efficiency of housework.

[0068] The progress management unit can use the emotion estimation function to analyze the user's emotions and adjust the progress of housework. For example, it can analyze facial expressions and voices while doing housework and calculate an emotion score. It can also adjust the progress of housework based on the emotion score. For example, if the user is under high stress, it can suggest slowing down the progress of housework. It can also use the emotion estimation function to provide positive feedback according to the progress of housework, thereby increasing the user's motivation. This allows for housework progress management that takes the user's emotions into consideration, and makes housework more efficient.

[0069] The housework visualization unit can use the emotion estimation function to analyze the user's emotions and adjust the priority of housework. For example, it can analyze facial expressions and voices while performing housework to calculate an emotion score. It can also adjust the priority of housework based on the emotion score. For example, if stress is high, it can prioritize relaxing housework. It can also use the emotion estimation function to provide positive feedback according to the progress of housework, increasing the user's motivation. This allows housework prioritization to be achieved taking into account the user's emotions, thereby improving the efficiency of housework.

[0070] The housework sharing management unit can use the emotion estimation function to analyze the user's emotions and adjust the allocation of housework. For example, it can analyze facial expressions and voices while performing housework and calculate an emotion score. It can also adjust the allocation of housework based on the emotion score. For example, it can assign less burdensome housework to a user who is highly stressed. It can also use the emotion estimation function to provide positive feedback according to the housework allocation status, thereby increasing the user's motivation. This allows the allocation of housework to take the user's emotions into consideration, thereby improving the efficiency of housework.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The housework recording unit records the status of housework. For example, the housework recording unit stores the time, frequency, and completion status of housework in a database. The housework recording unit can also estimate the emotions of the person performing the housework and calculate an emotion score. Step 2: The housework visualization unit visualizes the housework that needs to be done now based on the housework status recorded by the housework recording unit. For example, the housework visualization unit makes a list of the housework that needs to be done now, taking into account the priority and urgency of the housework. The housework visualization unit can also suggest the optimal timing for housework, taking into account the environment in which the housework is being done (weather, time of day, whether family members are at home, etc.). Step 3: The household chore management unit manages the household chore allocation status. For example, the household chore allocation management unit records who is responsible for household chores and their share, and tracks progress. The household chore allocation management unit can also estimate each member's emotions and adjust the allocation based on their emotions. Step 4: The progress management unit tracks the progress of housework. For example, the progress management unit may propose an optimal schedule for dividing up household chores, taking into account the frequency of chores and past performance. The progress management unit can also monitor the progress of housework in real time and automatically redistribute tasks to other members if progress is behind schedule.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0086] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0101] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

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

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0119] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0121] The data processing system 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 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 record section for recording the status of housework; a housework visualization unit that visualizes housework that needs to be done now based on the housework implementation status recorded by the housework recording unit; A housework division management department that manages the division of housework; A progress management unit that tracks the progress of housework. A system characterized by:

2. The household chore recording unit Estimating the emotion of the person performing the housework and adjusting the priority of the housework based on the emotion. The system of claim 1 .

3. The housework recording unit and the housework visualization unit are It can be applied to task management not only at home but also in other environments such as at work and school. The system of claim 1 .

4. The housework sharing management unit Estimate the emotions of each member and adjust the division of labor based on those emotions. The system of claim 1 .

5. The progress management unit Monitor progress in real time and automatically redistribute tasks to other members if progress is slow. The system of claim 1 .

6. The household chore recording unit The time and required time for performing the housework are recorded in detail, and an efficient housework schedule is proposed based on the recorded data. The system of claim 1 .

7. The household chore recording unit Links with smart home devices to reflect the operating status of home appliances and the inventory status of consumables in real time The system of claim 1 .

8. The housework sharing management unit Analyzes user emotions using emotion estimation function and suggests ways to reduce stress The system of claim 1 .

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