System

Abstract

An object of the system according to the embodiment is to individually optimize the environment when using a toilet and to improve the comfort of the user.SOLUTION: A system includes an environment optimization part, a music adjustment part, a fragrance adjustment part, and a learning part. The environment optimization unit adjusts illumination and temperature based on a user's instruction. The music adjustment unit selects music based on the environment adjusted by the environment optimization unit. The aroma adjustment unit adjusts the aroma based on the music selected by the music adjustment unit. The learning unit learns preferences and habits of the user based on the scent adjusted by the scent adjusting unit.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 technologies have had the problem that it is difficult to individually optimize the environment when using the toilet, and it is not possible to sufficiently improve the comfort of users.

[0005] The system according to the embodiment aims to individually optimize the environment when using the toilet and improve the comfort of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes an environment optimization unit, a music adjustment unit, a fragrance adjustment unit, and a learning unit. The environment optimization unit adjusts lighting and temperature based on user instructions. The music adjustment unit selects music based on the environment adjusted by the environment optimization unit. The fragrance adjustment unit adjusts the fragrance based on the music selected by the music adjustment unit. The learning unit learns the user's preferences and habits based on the fragrance adjusted by the fragrance adjustment unit. [Effects of the Invention]

[0007] The system according to the embodiment can individually optimize the environment when using the toilet, improving the comfort of the user. [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) In a toilet usage assistance system according to an embodiment of the present invention, when a user enters a toilet, a generative AI optimizes the toilet environment through voice and text dialogue, providing a comfortable toilet experience. This allows the toilet usage assistance system to adjust lighting, temperature, music, and fragrance based on the user's instructions, learning the user's preferences and habits to provide a more personalized toilet environment.

[0029] A toilet usage support system according to an embodiment includes an environment optimization unit, a music adjustment unit, a fragrance adjustment unit, and a learning unit. The environment optimization unit adjusts lighting and temperature based on user instructions. For example, if a user instructs the environment optimization unit to "brighten the lights," the environment optimization unit understands the instruction and adjusts the brightness of the lighting. Similarly, if a user instructs the environment optimization unit to "lower the temperature a little," the environment optimization unit appropriately adjusts the temperature in the toilet. The music adjustment unit selects music based on the environment adjusted by the environment optimization unit. For example, if a user instructs the environment optimization unit to "play relaxing music," the music adjustment unit selects and plays appropriate music. The fragrance adjustment unit adjusts the fragrance based on the music selected by the music adjustment unit. For example, if a user instructs the environment optimization unit to "play a floral fragrance," the fragrance adjustment unit adjusts the fragrance in the toilet. The learning unit learns the user's preferences and habits based on the fragrance adjusted by the fragrance adjustment unit. For example, if a user prefers the same temperature setting or music every time, the learning unit learns that pattern and automatically applies those settings from the next time onwards. User instructions are input via voice commands, touch panel operation, or the like. The range and method of adjusting lighting and temperature include the brightness, color temperature, and temperature setting range of lighting. The criteria and type of music selection include genre, tempo, and volume. The method and type of fragrance adjustment include the strength and type of fragrance (floral, citrus, etc.). The method of learning preferences and habits includes past selection history and survey results. This allows the toilet usage assistance system to optimize the environment in the toilet based on the user's instructions and provide a comfortable toilet experience.

[0030] The environment optimization unit can analyze the tone and speed of the voice, estimate the stress level, and adjust the environment accordingly. For example, the environment optimization unit analyzes the tone and speed of the voice in real time when the user enters the restroom and estimates the stress level. For example, if the voice is high-pitched and fast, it may determine that stress is high, and soften the lighting and adjust the temperature to a comfortable level. The method for analyzing the tone and speed of the voice uses voice recognition technology and analysis algorithms. The method for estimating the stress level is based on the results of voice analysis and psychological evaluation. This makes it possible to provide a more comfortable restroom experience by adjusting the environment according to the user's stress level.

[0031] The environment optimization unit can predict the next instruction based on the instruction history and prepare the environment in advance. For example, the environment optimization unit stores the user's past instruction history in a database and prepares the environment in advance based on that history the next time the toilet is used. For example, for a user who prefers the same temperature setting every time, the temperature is set to that temperature before entering the toilet. The instruction history is saved using a database format or an analysis algorithm. The next instruction is predicted based on a machine learning algorithm or past pattern analysis. This makes it possible to predict the next instruction based on the user's past instruction history and prepare the environment in advance, thereby reducing the user's effort.

[0032] The environment optimization unit can automatically adjust lighting according to the season and time of day. The environment optimization unit, for example, builds a system that automatically adjusts lighting in a restroom according to the season. For example, warm-colored lighting is used in winter, and cool-colored lighting is used in summer. The adjustment criteria and method according to the season and time of day are based on lighting settings for each season and temperature adjustments for each time of day. In this way, a more comfortable restroom environment can be provided by automatically adjusting lighting according to the season and time of day.

[0033] The environment optimization unit works in conjunction with a smartphone, saving environmental settings in the cloud so that the same settings can be applied in other restrooms. The environment optimization unit, for example, works in conjunction with a user's smartphone to build a system that saves individual environmental settings in the cloud. For example, the user's preferred lighting and temperature settings can be saved in the cloud, and the same settings can be applied in other restrooms. The method of connecting with the smartphone is via Bluetooth, Wi-Fi, or a dedicated app. The method of using the cloud is determined based on the type of cloud service and the data encryption method. This allows the user's environmental settings to be saved in the cloud, and the same settings can be applied in other restrooms, improving user convenience.

[0034] The music adjustment unit can analyze the heart rate and breathing patterns and select music with a high relaxing effect. The music adjustment unit, for example, builds a system that analyzes the user's heart rate and breathing patterns in real time and selects music with a high relaxing effect. For example, if the heart rate is high, relaxing music is played. The heart rate and breathing patterns are measured using wearable devices and sensor technology. The evaluation criteria for the relaxation effect are based on psychological evaluations and feedback surveys. This makes it possible to provide a more comfortable toilet experience by selecting music with a high relaxing effect based on the user's heart rate and breathing patterns.

[0035] The music adjustment unit can suggest music that the user will like next based on the selection history. For example, the music adjustment unit stores the user's past selection history in a database and builds a system that suggests music that the user is likely to like next. For example, it analyzes patterns of music selected in the past and suggests music that the user will like next. The selection history is saved using a database format or an analysis algorithm. The next music that the user will like is predicted based on a machine learning algorithm or past pattern analysis. This makes it possible to provide music that suits the user's preferences by suggesting music that the user is likely to like next based on the user's past selection history.

[0036] The music adjustment unit can work with a smart home system to ensure consistency in the music. For example, the music adjustment unit builds a system that works with the user's smart home system to ensure consistency in the music in the toilet. For example, the music in the toilet is adjusted based on the smart home settings. The method of working with the smart home system uses IoT devices and communication protocols. This makes it possible to provide a more comfortable toilet experience by working with the user's smart home system to ensure consistency in the music.

[0037] The fragrance adjuster can analyze the heart rate and breathing pattern and select a fragrance with a high relaxation effect. The fragrance adjuster can, for example, analyze the user's heart rate and breathing pattern in real time to build a system that selects a fragrance with a high relaxation effect. For example, if the heart rate is high, a fragrance with a relaxing effect is selected. The heart rate and breathing pattern are measured using wearable devices and sensor technology. The evaluation criteria for the relaxation effect are based on psychological evaluations and feedback surveys. This makes it possible to provide a more comfortable toilet experience by selecting a fragrance with a high relaxation effect based on the user's heart rate and breathing pattern.

[0038] The fragrance adjustment unit can suggest the next most popular fragrance based on the selection history. For example, the fragrance adjustment unit stores the user's past selection history in a database and builds a system that suggests the next most likely fragrance. For example, it analyzes the patterns of fragrances selected in the past and suggests the next most likely fragrance. The selection history is saved using a database format or an analysis algorithm. The next most likely fragrance is predicted based on a machine learning algorithm or past pattern analysis. This makes it possible to provide fragrances that suit the user's preferences by suggesting the next most likely fragrance based on the user's past selection history.

[0039] The scent adjustment unit can work with a smart home system to provide a consistent scent. For example, the scent adjustment unit builds a system that works with the user's smart home system to provide a consistent scent in the toilet. For example, the scent in the toilet is adjusted based on the smart home settings. The method of working with the smart home system uses IoT devices and communication protocols. This makes it possible to provide a more comfortable toilet experience by working with the user's smart home system to provide a consistent scent.

[0040] The scent adjustment unit can customize scent patterns according to user preferences and periodically provide new options. The scent adjustment unit, for example, builds a system that customizes scent patterns according to user preferences. For example, it stores the scent patterns preferred by the user in a database and periodically provides new options. Preferences are identified based on survey results and past selection history. Scent patterns are customized based on strength, type, duration, etc. This allows the scent patterns to be customized according to the user's preferences and periodically provide new options, thereby improving user satisfaction.

[0041] The learning unit can analyze behavioral patterns over the long term and learn optimal settings according to seasons and time periods. The learning unit, for example, builds a system that analyzes a user's behavioral patterns over the long term and learns optimal settings according to seasons and time periods. For example, it learns warm temperature settings in winter and cool temperature settings in summer. The method of analyzing behavioral patterns is based on daily behavior history and data collected by sensors. The criteria and method for settings according to seasons and time periods are based on temperature settings for each season and lighting settings for each time period. In this way, by analyzing a user's behavioral patterns over the long term and learning optimal settings according to seasons and time periods, it is possible to provide a more personalized and comfortable toilet experience.

[0042] The learning unit can work in conjunction with a smart device to optimize the toilet environment based on lifestyle habit data. The learning unit, for example, works in conjunction with the user's smart device to build a system that optimizes the toilet environment based on other lifestyle habit data. For example, it analyzes heart rate and stress levels based on data from a smartwatch and adjusts the toilet environment. The system can connect to a smart device via Bluetooth, Wi-Fi, or a dedicated app. The type and collection method of lifestyle habit data is based on sleep patterns, food records, exercise history, etc. This allows the system to work in conjunction with the user's smart device and optimize the toilet environment based on other lifestyle habit data, providing a more personalized and comfortable toilet experience.

[0043] The learning unit stores preferences and habits in the cloud, and can apply similar settings in other toilets. The learning unit, for example, builds a system that stores a user's preferences and habits in the cloud and applies similar settings in other toilets. For example, the user's preferred temperature and lighting settings are stored in the cloud, and the same settings are applied in other toilets. The method of using the cloud is determined based on the type of cloud service and the data encryption method. This allows the user's preferences and habits to be stored in the cloud, and the same settings to be applied in other toilets, improving user convenience.

[0044] The learning unit can share preferences and habits with family members or housemates and suggest common settings. The learning unit, for example, builds a system that shares a user's preferences and habits with family members or housemates and suggests common settings. For example, the learning unit shares the temperature and lighting settings preferred by all family members and suggests common settings. The method of sharing data with family members or housemates is based on data sharing consent and access control. In this way, the user's preferences and habits can be shared with family members or housemates and common settings can be suggested, thereby improving the satisfaction of all family members.

[0045] The health monitoring unit can analyze excrement and monitor the health condition in detail. The health monitoring unit, for example, analyzes the user's excrement and builds a system that monitors the health condition in detail. For example, the color and shape of the excrement are analyzed to evaluate the health condition. The excrement is analyzed based on component analysis and evaluation of color and shape. The health condition evaluation criteria are based on medical standards and health scores. In this way, the user's health management can be supported by analyzing the user's excrement and monitoring the health condition in detail.

[0046] The health monitoring unit accumulates data on body temperature and heart rate over the long term, enabling early detection of abnormalities. The health monitoring unit, for example, accumulates data on a user's body temperature and heart rate over the long term, building a system for early detection of abnormalities. For example, it analyzes fluctuations in body temperature and heart rate to detect abnormalities. Body temperature and heart rate are measured using wearable devices and sensor technology. The criteria and method for early detection of abnormalities are based on abnormality threshold settings and an alert system. This allows the system to accumulate data on a user's body temperature and heart rate over the long term, enabling early detection of abnormalities, and supporting the user's health management.

[0047] The health monitoring unit can link health data with medical institutions and support remote diagnosis. The health monitoring unit, for example, links the user's health data with medical institutions and builds a system to support remote diagnosis. For example, body temperature and heart rate data is sent to a medical institution for diagnosis. The type and collection method of health data are determined based on body temperature, heart rate, blood pressure, etc. The method of linking with medical institutions is determined based on the data sharing method and security measures. In this way, by linking the user's health data with medical institutions and supporting remote diagnosis, the user's health can be managed more effectively.

[0048] The health monitoring unit optimizes the toilet environment according to the user's health condition, thereby enhancing the relaxation effect. The health monitoring unit, for example, builds a system that optimizes the toilet environment according to the user's health condition. For example, the temperature and lighting are adjusted based on body temperature and heart rate data. The health condition evaluation criteria are based on medical standards and health scores. The toilet environment is optimized based on adjustments to temperature, lighting, fragrance, etc. This makes it possible to support the user's health management by optimizing the toilet environment according to the user's health condition and enhancing the relaxation effect.

[0049] The cleaning management unit can analyze the degree of dirt in detail and automatically determine cleaning priorities. The cleaning management unit, for example, builds a system that analyzes the degree of dirt in a toilet in detail and automatically determines cleaning priorities. For example, it analyzes the type and extent of dirt and determines cleaning priorities. The method of analyzing the degree of dirt is based on sensor technology and image analysis. The method of determining cleaning priorities is based on the degree of dirt and frequency of use. In this way, by analyzing the degree of dirt in a toilet in detail and automatically determining cleaning priorities, it is possible to maintain the cleanliness of the toilet.

[0050] The cleaning management unit can generate an optimal cleaning schedule based on the work history of cleaning staff. The cleaning management unit, for example, builds a system that generates an optimal cleaning schedule based on the work history of cleaning staff. For example, it analyzes past cleaning history and proposes an optimal cleaning schedule. The cleaning staff work history is stored using a database format and analysis algorithm. The cleaning schedule is generated based on the analysis results of the work history and frequency of use. In this way, efficient cleaning management can be achieved by generating an optimal cleaning schedule based on the work history of cleaning staff.

[0051] The cleaning management unit stores the cleaning status in the cloud, and similar cleaning management can be applied to other toilets. The cleaning management unit, for example, builds a system that stores the cleaning status of a toilet in the cloud and applies similar cleaning management to other toilets. For example, cleaning history and dirtiness are stored in the cloud, and similar cleaning management is applied to other toilets. The method for storing the cleaning status uses a database format and an analysis algorithm. The method for using the cloud is determined based on the type of cloud service and the data encryption method. In this way, the cleanliness of the toilets can be maintained by storing the cleaning status in the cloud and applying similar cleaning management to other toilets.

[0052] The cleaning management unit can work in cooperation with a cleaning robot to perform cleaning automatically. The cleaning management unit, for example, works in cooperation with a cleaning robot to build a system that performs cleaning automatically. For example, a sensor detects the degree of dirtiness of the toilet and issues instructions to the cleaning robot. The method of working with the cleaning robot uses a communication protocol and a control algorithm. The method of performing automatic cleaning is based on the setting of a cleaning route and cleaning frequency. In this way, the cleanliness of the toilet can be maintained by working in cooperation with the cleaning robot and performing cleaning automatically.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The toilet usage assistance system can also be equipped with a health monitoring unit that monitors the user's health condition. For example, the health monitoring unit measures the user's body temperature and heart rate in real time and issues an alert if an abnormality is detected. Body temperature and heart rate are measured using wearable devices and sensor technology. Abnormality detection criteria are based on medical standards and individual health data. This allows the user's health condition to be constantly monitored and any abnormalities to be addressed quickly.

[0055] The toilet usage support system can further include a behavior analysis unit that analyzes the user's behavioral patterns. The behavior analysis unit, for example, analyzes the frequency and time of the user's toilet use and suggests the optimal timing for use. The behavioral pattern analysis method involves collecting data using sensors and cameras and analyzing it using a machine learning algorithm. This allows the system to understand the user's behavioral patterns and suggest the optimal timing for toilet use, thereby avoiding crowded toilets.

[0056] The toilet usage assistance system can further include a stress analysis unit that analyzes the user's stress level and configures the environment to reduce stress. The stress analysis unit, for example, analyzes the user's heart rate and breathing pattern in real time to assess the stress level. The heart rate and breathing pattern are measured using wearable devices and sensor technology. The criteria for evaluating the stress level are based on psychological evaluations and feedback surveys. This allows the environment to be configured according to the user's stress level, reducing stress and providing a more comfortable toilet experience.

[0057] The toilet usage support system can further include a health linking unit that links the user's health data with medical institutions and supports remote diagnosis. The health linking unit, for example, sends the user's body temperature and heart rate data to the medical institution for diagnosis. The type and collection method of health data are determined based on body temperature, heart rate, blood pressure, etc. The method of linking with the medical institution is determined based on the data sharing method and security measures. In this way, by linking the user's health data with the medical institution and supporting remote diagnosis, the user's health can be managed more effectively.

[0058] The toilet usage support system can further include a learning unit that analyzes the user's behavioral patterns over the long term and learns optimal settings according to the season and time of day. The learning unit, for example, builds a system that analyzes the user's behavioral patterns over the long term and learns optimal settings according to the season and time of day. For example, it learns a warm temperature setting in winter and a cool temperature setting in summer. The method of analyzing behavioral patterns is based on daily behavior history and data collected by sensors. The criteria and method for settings according to the season and time of day are based on the temperature settings for each season and the lighting settings for each time of day. In this way, by analyzing the user's behavioral patterns over the long term and learning optimal settings according to the season and time of day, it is possible to provide a more personalized and comfortable toilet experience.

[0059] The toilet usage support system can further include a health optimization unit that optimizes the toilet environment according to the user's health condition. The health optimization unit adjusts the temperature and lighting based on the user's body temperature and heart rate data, for example. The health condition evaluation criteria are based on medical standards and health scores. The toilet environment is optimized by adjusting the temperature, lighting, fragrance, etc. This makes it possible to support the user's health management by optimizing the toilet environment according to the user's health condition and increasing the relaxation effect.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The environment optimization unit adjusts the lighting and temperature based on the user's instructions. For example, if the user instructs the unit to "brighten the lights," the environment optimization unit understands the instruction and adjusts the brightness of the lights. Similarly, if the user instructs the unit to "lower the temperature a little," the environment optimization unit adjusts the temperature in the toilet appropriately. Step 2: The music adjustment unit selects music based on the environment adjusted by the environment optimization unit. For example, if the user instructs the unit to "play relaxing music," the music adjustment unit selects and plays appropriate music. Step 3: The scent adjuster adjusts the scent based on the music selected by the music adjuster. For example, if the user requests a "floral scent," the scent adjuster adjusts the scent in the toilet. Step 4: The learning unit learns the user's preferences and habits based on the scent adjusted by the scent adjustment unit. For example, if the user prefers the same temperature setting or music every time, the learning unit will learn that pattern and automatically apply those settings from the next time onwards.

[0062] (Example 2) In a toilet usage assistance system according to an embodiment of the present invention, when a user enters a toilet, a generative AI optimizes the toilet environment through voice and text dialogue, providing a comfortable toilet experience. This allows the toilet usage assistance system to adjust lighting, temperature, music, and fragrance based on the user's instructions, learning the user's preferences and habits to provide a more personalized toilet environment.

[0063] A toilet usage support system according to an embodiment includes an environment optimization unit, a music adjustment unit, a fragrance adjustment unit, and a learning unit. The environment optimization unit adjusts lighting and temperature based on user instructions. For example, if a user instructs the environment optimization unit to "brighten the lights," the environment optimization unit understands the instruction and adjusts the brightness of the lighting. Similarly, if a user instructs the environment optimization unit to "lower the temperature a little," the environment optimization unit appropriately adjusts the temperature in the toilet. The music adjustment unit selects music based on the environment adjusted by the environment optimization unit. For example, if a user instructs the environment optimization unit to "play relaxing music," the music adjustment unit selects and plays appropriate music. The fragrance adjustment unit adjusts the fragrance based on the music selected by the music adjustment unit. For example, if a user instructs the environment optimization unit to "play a floral fragrance," the fragrance adjustment unit adjusts the fragrance in the toilet. The learning unit learns the user's preferences and habits based on the fragrance adjusted by the fragrance adjustment unit. For example, if a user prefers the same temperature setting or music every time, the learning unit learns that pattern and automatically applies those settings from the next time onwards. User instructions are input via voice commands, touch panel operation, or the like. The range and method of adjusting lighting and temperature include the brightness, color temperature, and temperature setting range of lighting. The criteria and type of music selection include genre, tempo, and volume. The method and type of fragrance adjustment include the strength and type of fragrance (floral, citrus, etc.). The method of learning preferences and habits includes past selection history and survey results. This allows the toilet usage assistance system to optimize the environment in the toilet based on the user's instructions and provide a comfortable toilet experience.

[0064] The environment optimization unit can analyze the tone and speed of the voice, estimate the stress level, and adjust the environment accordingly. For example, the environment optimization unit analyzes the tone and speed of the voice in real time when the user enters the restroom and estimates the stress level. For example, if the voice is high-pitched and fast, it may determine that stress is high, and soften the lighting and adjust the temperature to a comfortable level. The method for analyzing the tone and speed of the voice uses voice recognition technology and analysis algorithms. The method for estimating the stress level is based on the results of voice analysis and psychological evaluation. This makes it possible to provide a more comfortable restroom experience by adjusting the environment according to the user's stress level.

[0065] The environment optimization unit can predict the next instruction based on the instruction history and prepare the environment in advance. For example, the environment optimization unit stores the user's past instruction history in a database and prepares the environment in advance based on that history the next time the toilet is used. For example, for a user who prefers the same temperature setting every time, the temperature is set to that temperature before entering the toilet. The instruction history is saved using a database format or an analysis algorithm. The next instruction is predicted based on a machine learning algorithm or past pattern analysis. This makes it possible to predict the next instruction based on the user's past instruction history and prepare the environment in advance, thereby reducing the user's effort.

[0066] The environment optimization unit can automatically set the environment according to the user's emotional state using an emotion estimation function. The environment optimization unit, for example, analyzes the user's facial expressions and voice and uses the emotion estimation function to grasp the user's emotional state in real time. For example, if the user is relaxed, the lighting is softened and the temperature is adjusted to a comfortable level. The emotion estimation function uses facial expression recognition, voice analysis, and biometrics. Classification and evaluation criteria for emotional states are based on emotion categories such as joy, sadness, and anger. This allows the environment to be automatically set according to the user's emotional state, providing a more personalized and comfortable toilet experience.

[0067] The environment optimization unit can automatically adjust lighting according to the season and time of day. The environment optimization unit, for example, builds a system that automatically adjusts lighting in a restroom according to the season. For example, warm-colored lighting is used in winter, and cool-colored lighting is used in summer. The adjustment criteria and method according to the season and time of day are based on lighting settings for each season and temperature adjustments for each time of day. In this way, a more comfortable restroom environment can be provided by automatically adjusting lighting according to the season and time of day.

[0068] The environment optimization unit works in conjunction with a smartphone, saving environmental settings in the cloud so that the same settings can be applied in other restrooms. The environment optimization unit, for example, works in conjunction with a user's smartphone to build a system that saves individual environmental settings in the cloud. For example, the user's preferred lighting and temperature settings can be saved in the cloud, and the same settings can be applied in other restrooms. The method of connecting with the smartphone is via Bluetooth, Wi-Fi, or a dedicated app. The method of using the cloud is determined based on the type of cloud service and the data encryption method. This allows the user's environmental settings to be saved in the cloud, and the same settings can be applied in other restrooms, improving user convenience.

[0069] The environment optimization unit can use the emotion estimation function to estimate the emotional state and prepare the environment in advance. For example, the environment optimization unit uses the emotion estimation function to build a system that estimates the emotional state of the user before entering the restroom. For example, the emotional state is estimated by analyzing the user's facial expressions and voice. The emotion estimation function uses technology such as facial expression recognition, voice analysis, and biometrics. The classification and evaluation criteria of the emotional state are based on emotion categories such as joy, sadness, and anger. This makes it possible to estimate the emotional state of the user before entering the restroom and prepare an optimal environment in advance, thereby providing a more comfortable restroom experience.

[0070] The music adjustment unit can analyze the heart rate and breathing patterns and select music with a high relaxing effect. The music adjustment unit, for example, builds a system that analyzes the user's heart rate and breathing patterns in real time and selects music with a high relaxing effect. For example, if the heart rate is high, relaxing music is played. The heart rate and breathing patterns are measured using wearable devices and sensor technology. The evaluation criteria for the relaxation effect are based on psychological evaluations and feedback surveys. This makes it possible to provide a more comfortable toilet experience by selecting music with a high relaxing effect based on the user's heart rate and breathing patterns.

[0071] The music adjustment unit can suggest music that the user will like next based on the selection history. For example, the music adjustment unit stores the user's past selection history in a database and builds a system that suggests music that the user is likely to like next. For example, it analyzes patterns of music selected in the past and suggests music that the user will like next. The selection history is saved using a database format or an analysis algorithm. The next music that the user will like is predicted based on a machine learning algorithm or past pattern analysis. This makes it possible to provide music that suits the user's preferences by suggesting music that the user is likely to like next based on the user's past selection history.

[0072] The music adjustment unit can automatically select music according to the user's emotional state using the emotion estimation function. For example, the music adjustment unit uses the emotion estimation function to analyze the user's emotional state in real time and build a system that automatically selects music according to the emotional state. For example, if the user is relaxed, music with a relaxing effect is played. The emotion estimation function uses facial expression recognition, voice analysis, and biometrics. Classification and evaluation criteria for the emotional state are based on emotion categories such as joy, sadness, and anger. This allows for the automatic selection of music according to the user's emotional state, providing a more comfortable toilet experience.

[0073] The music adjustment unit can work with a smart home system to ensure consistency in the music. For example, the music adjustment unit builds a system that works with the user's smart home system to ensure consistency in the music in the toilet. For example, the music in the toilet is adjusted based on the smart home settings. The method of working with the smart home system uses IoT devices and communication protocols. This makes it possible to provide a more comfortable toilet experience by working with the user's smart home system to ensure consistency in the music.

[0074] The music adjustment unit can use the emotion estimation function to estimate the user's emotional state and prepare optimal music in advance. The music adjustment unit, for example, uses the emotion estimation function to build a system that estimates the user's emotional state before entering the restroom. For example, the emotional state is estimated by analyzing the user's facial expressions and voice. The emotion estimation function uses technology such as facial expression recognition, voice analysis, and biometrics. The classification and evaluation criteria of the emotional state are based on emotion categories such as joy, sadness, and anger. This makes it possible to estimate the user's emotional state before entering the restroom and prepare optimal music in advance, thereby providing a more comfortable restroom experience.

[0075] The fragrance adjuster can analyze the heart rate and breathing pattern and select a fragrance with a high relaxation effect. The fragrance adjuster can, for example, analyze the user's heart rate and breathing pattern in real time to build a system that selects a fragrance with a high relaxation effect. For example, if the heart rate is high, a fragrance with a relaxing effect is selected. The heart rate and breathing pattern are measured using wearable devices and sensor technology. The evaluation criteria for the relaxation effect are based on psychological evaluations and feedback surveys. This makes it possible to provide a more comfortable toilet experience by selecting a fragrance with a high relaxation effect based on the user's heart rate and breathing pattern.

[0076] The fragrance adjustment unit can suggest the next most popular fragrance based on the selection history. For example, the fragrance adjustment unit stores the user's past selection history in a database and builds a system that suggests the next most likely fragrance. For example, it analyzes the patterns of fragrances selected in the past and suggests the next most likely fragrance. The selection history is saved using a database format or an analysis algorithm. The next most likely fragrance is predicted based on a machine learning algorithm or past pattern analysis. This makes it possible to provide fragrances that suit the user's preferences by suggesting the next most likely fragrance based on the user's past selection history.

[0077] The scent adjuster can automatically select a scent according to the user's emotional state using the emotion estimation function. For example, the scent adjuster uses the emotion estimation function to analyze the user's emotional state in real time and build a system that automatically selects a scent according to the emotional state. For example, if the user is relaxed, a scent with a relaxing effect is selected. The emotion estimation function uses facial recognition, voice analysis, and biometrics. Classification and evaluation criteria for emotional states are based on emotion categories such as joy, sadness, and anger. This allows the user to automatically select a scent according to their emotional state, providing a more comfortable toilet experience.

[0078] The scent adjustment unit can work with a smart home system to provide a consistent scent. For example, the scent adjustment unit builds a system that works with the user's smart home system to provide a consistent scent in the toilet. For example, the scent in the toilet is adjusted based on the smart home settings. The method of working with the smart home system uses IoT devices and communication protocols. This makes it possible to provide a more comfortable toilet experience by working with the user's smart home system to provide a consistent scent.

[0079] The scent adjustment unit can customize scent patterns according to user preferences and periodically provide new options. The scent adjustment unit, for example, builds a system that customizes scent patterns according to user preferences. For example, it stores the scent patterns preferred by the user in a database and periodically provides new options. Preferences are identified based on survey results and past selection history. Scent patterns are customized based on strength, type, duration, etc. This allows the scent patterns to be customized according to the user's preferences and periodically provide new options, thereby improving user satisfaction.

[0080] The scent adjuster can use the emotion estimation function to estimate the user's emotional state and prepare an optimal scent in advance. The scent adjuster, for example, uses the emotion estimation function to build a system that estimates the user's emotional state before entering the restroom. For example, the emotion estimation function analyzes the user's facial expressions and voice to estimate the emotional state. The emotion estimation function uses facial expression recognition, voice analysis, and biometrics. The classification and evaluation criteria for the emotional state are based on emotion categories such as joy, sadness, and anger. This makes it possible to estimate the user's emotional state before entering the restroom and prepare an optimal scent in advance, providing a more comfortable restroom experience.

[0081] The learning unit can analyze behavioral patterns over the long term and learn optimal settings according to seasons and time periods. The learning unit, for example, builds a system that analyzes a user's behavioral patterns over the long term and learns optimal settings according to seasons and time periods. For example, it learns warm temperature settings in winter and cool temperature settings in summer. The method of analyzing behavioral patterns is based on daily behavior history and data collected by sensors. The criteria and method for settings according to seasons and time periods are based on temperature settings for each season and lighting settings for each time period. In this way, by analyzing a user's behavioral patterns over the long term and learning optimal settings according to seasons and time periods, it is possible to provide a more personalized and comfortable toilet experience.

[0082] The learning unit can work in conjunction with a smart device to optimize the toilet environment based on lifestyle habit data. The learning unit, for example, works in conjunction with the user's smart device to build a system that optimizes the toilet environment based on other lifestyle habit data. For example, it analyzes heart rate and stress levels based on data from a smartwatch and adjusts the toilet environment. The system can connect to a smart device via Bluetooth, Wi-Fi, or a dedicated app. The type and collection method of lifestyle habit data is based on sleep patterns, food records, exercise history, etc. This allows the system to work in conjunction with the user's smart device and optimize the toilet environment based on other lifestyle habit data, providing a more personalized and comfortable toilet experience.

[0083] The learning unit uses the emotion estimation function to learn habits based on the user's emotional state and provide a more personalized environment. The learning unit, for example, uses the emotion estimation function to build a system that learns habits based on the user's emotional state. For example, the learning unit learns the environmental settings used when the user is relaxed and applies them from the next time onwards. The emotion estimation function uses facial expression recognition, voice analysis, and biometrics. Classification and evaluation criteria for emotional states are based on emotion categories such as joy, sadness, and anger. This makes it possible to learn habits based on the user's emotional state and provide a more personalized environment, thereby improving user satisfaction.

[0084] The learning unit stores preferences and habits in the cloud, and can apply similar settings in other toilets. The learning unit, for example, builds a system that stores a user's preferences and habits in the cloud and applies similar settings in other toilets. For example, the user's preferred temperature and lighting settings are stored in the cloud, and the same settings are applied in other toilets. The method of using the cloud is determined based on the type of cloud service and the data encryption method. This allows the user's preferences and habits to be stored in the cloud, and the same settings to be applied in other toilets, improving user convenience.

[0085] The learning unit can share preferences and habits with family members or housemates and suggest common settings. The learning unit, for example, builds a system that shares a user's preferences and habits with family members or housemates and suggests common settings. For example, the learning unit shares the temperature and lighting settings preferred by all family members and suggests common settings. The method of sharing data with family members or housemates is based on data sharing consent and access control. In this way, the user's preferences and habits can be shared with family members or housemates and common settings can be suggested, thereby improving the satisfaction of all family members.

[0086] The health monitoring unit can analyze excrement and monitor the health condition in detail. The health monitoring unit, for example, analyzes the user's excrement and builds a system that monitors the health condition in detail. For example, the color and shape of the excrement are analyzed to evaluate the health condition. The excrement is analyzed based on component analysis and evaluation of color and shape. The health condition evaluation criteria are based on medical standards and health scores. In this way, the user's health management can be supported by analyzing the user's excrement and monitoring the health condition in detail.

[0087] The health monitoring unit accumulates data on body temperature and heart rate over the long term, enabling early detection of abnormalities. The health monitoring unit, for example, accumulates data on a user's body temperature and heart rate over the long term, building a system for early detection of abnormalities. For example, it analyzes fluctuations in body temperature and heart rate to detect abnormalities. Body temperature and heart rate are measured using wearable devices and sensor technology. The criteria and method for early detection of abnormalities are based on abnormality threshold settings and an alert system. This allows the system to accumulate data on a user's body temperature and heart rate over the long term, enabling early detection of abnormalities, and supporting the user's health management.

[0088] The health monitoring unit can use the emotion estimation function to analyze the relationship between the emotional state and the health state and support health management. The health monitoring unit, for example, uses the emotion estimation function to build a system that analyzes the relationship between the user's emotional state and the health state. For example, the emotional state and body temperature and heart rate data are analyzed to evaluate the health state. The emotion estimation function uses facial expression recognition, voice analysis, and biometrics as its technology. The classification and evaluation criteria for the emotional state are based on emotion categories such as joy, sadness, and anger. The analysis method for the relationship between the health state is based on statistical analysis and machine learning algorithms. This allows the system to analyze the relationship between the user's emotional state and the health state and support health management, thereby enabling the user's health to be managed more effectively.

[0089] The health monitoring unit can link health data with medical institutions and support remote diagnosis. The health monitoring unit, for example, links the user's health data with medical institutions and builds a system to support remote diagnosis. For example, body temperature and heart rate data is sent to a medical institution for diagnosis. The type and collection method of health data are determined based on body temperature, heart rate, blood pressure, etc. The method of linking with medical institutions is determined based on the data sharing method and security measures. In this way, by linking the user's health data with medical institutions and supporting remote diagnosis, the user's health can be managed more effectively.

[0090] The health monitoring unit optimizes the toilet environment according to the user's health condition, thereby enhancing the relaxation effect. The health monitoring unit, for example, builds a system that optimizes the toilet environment according to the user's health condition. For example, the temperature and lighting are adjusted based on body temperature and heart rate data. The health condition evaluation criteria are based on medical standards and health scores. The toilet environment is optimized based on adjustments to temperature, lighting, fragrance, etc. This makes it possible to support the user's health management by optimizing the toilet environment according to the user's health condition and enhancing the relaxation effect.

[0091] The health monitoring unit can estimate the emotional state using an emotion estimation function to enhance health status monitoring. The health monitoring unit, for example, uses the emotion estimation function to build a system that estimates the emotional state of a user before entering the restroom. For example, the emotional state is estimated by analyzing the user's facial expressions and voice. The emotion estimation function uses facial expression recognition, voice analysis, and biometrics. The classification and evaluation criteria of the emotional state are based on emotion categories such as joy, sadness, and anger. The health status monitoring method is based on regular data collection and abnormal value detection. This allows the emotional state of the user before entering the restroom to be estimated and health status monitoring to be enhanced, thereby enabling more effective health management of the user.

[0092] The cleaning management unit can analyze the degree of dirt in detail and automatically determine cleaning priorities. The cleaning management unit, for example, builds a system that analyzes the degree of dirt in a toilet in detail and automatically determines cleaning priorities. For example, it analyzes the type and extent of dirt and determines cleaning priorities. The method of analyzing the degree of dirt is based on sensor technology and image analysis. The method of determining cleaning priorities is based on the degree of dirt and frequency of use. In this way, by analyzing the degree of dirt in a toilet in detail and automatically determining cleaning priorities, it is possible to maintain the cleanliness of the toilet.

[0093] The cleaning management unit can generate an optimal cleaning schedule based on the work history of cleaning staff. The cleaning management unit, for example, builds a system that generates an optimal cleaning schedule based on the work history of cleaning staff. For example, it analyzes past cleaning history and proposes an optimal cleaning schedule. The cleaning staff work history is stored using a database format and analysis algorithm. The cleaning schedule is generated based on the analysis results of the work history and frequency of use. In this way, efficient cleaning management can be achieved by generating an optimal cleaning schedule based on the work history of cleaning staff.

[0094] The cleaning management unit can use the emotion estimation function to adjust the cleaning frequency based on the user's emotional state. The cleaning management unit, for example, uses the emotion estimation function to build a system that adjusts the cleaning frequency based on the user's emotional state. For example, if the user feels uncomfortable, the cleaning frequency is increased. The emotion estimation function uses facial expression recognition, voice analysis, and biometrics. The classification and evaluation criteria for the emotional state are based on emotion categories such as joy, sadness, and anger. The cleaning frequency is adjusted based on the frequency of use and the degree of dirtiness. In this way, by adjusting the cleaning frequency based on the user's emotional state, a more comfortable toilet environment can be provided.

[0095] The cleaning management unit stores the cleaning status in the cloud, and similar cleaning management can be applied to other toilets. The cleaning management unit, for example, builds a system that stores the cleaning status of a toilet in the cloud and applies similar cleaning management to other toilets. For example, cleaning history and dirtiness are stored in the cloud, and similar cleaning management is applied to other toilets. The method for storing the cleaning status uses a database format and an analysis algorithm. The method for using the cloud is determined based on the type of cloud service and the data encryption method. In this way, the cleanliness of the toilets can be maintained by storing the cleaning status in the cloud and applying similar cleaning management to other toilets.

[0096] The cleaning management unit can work in cooperation with a cleaning robot to perform cleaning automatically. The cleaning management unit, for example, works in cooperation with a cleaning robot to build a system that performs cleaning automatically. For example, a sensor detects the degree of dirtiness of the toilet and issues instructions to the cleaning robot. The method of working with the cleaning robot uses a communication protocol and a control algorithm. The method of performing automatic cleaning is based on the setting of a cleaning route and cleaning frequency. In this way, the cleanliness of the toilet can be maintained by working in cooperation with the cleaning robot and performing cleaning automatically.

[0097] The cleaning management unit can estimate the emotional state using the emotion estimation function and optimize the timing of cleaning. For example, the cleaning management unit uses the emotion estimation function to build a system that estimates the emotional state of a user before entering the toilet. For example, the emotional state is estimated by analyzing the user's facial expressions and voice. The emotion estimation function uses technology such as facial expression recognition, voice analysis, and biometrics. The classification and evaluation criteria of the emotional state are based on emotion categories such as joy, sadness, and anger. The method for optimizing the timing of cleaning is based on the frequency of use, degree of dirt, and emotional state. In this way, a more comfortable toilet environment can be provided by estimating the emotional state of the user before entering the toilet and optimizing the timing of cleaning.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The toilet usage assistance system can also be equipped with a health monitoring unit that monitors the user's health condition. For example, the health monitoring unit measures the user's body temperature and heart rate in real time and issues an alert if an abnormality is detected. Body temperature and heart rate are measured using wearable devices and sensor technology. Abnormality detection criteria are based on medical standards and individual health data. This allows the user's health condition to be constantly monitored and any abnormalities to be addressed quickly.

[0100] The toilet usage support system can further include a behavior analysis unit that analyzes the user's behavioral patterns. The behavior analysis unit, for example, analyzes the frequency and time of the user's toilet use and suggests the optimal timing for use. The behavioral pattern analysis method involves collecting data using sensors and cameras and analyzing it using a machine learning algorithm. This allows the system to understand the user's behavioral patterns and suggest the optimal timing for toilet use, thereby avoiding crowded toilets.

[0101] The toilet usage assistance system can further include an emotion estimation unit that estimates the user's emotional state and adjusts the toilet environment based on the emotional state. The emotion estimation unit, for example, analyzes the user's facial expressions and voice to grasp the user's emotional state in real time. The emotion estimation function uses facial expression recognition, voice analysis, and biometrics. The classification and evaluation criteria of the emotional state are based on emotion categories such as joy, sadness, and anger. This allows the environment to be automatically configured according to the user's emotional state, providing a more personalized and comfortable toilet experience.

[0102] The toilet usage assistance system can further include a stress analysis unit that analyzes the user's stress level and configures the environment to reduce stress. The stress analysis unit, for example, analyzes the user's heart rate and breathing pattern in real time to assess the stress level. The heart rate and breathing pattern are measured using wearable devices and sensor technology. The criteria for evaluating the stress level are based on psychological evaluations and feedback surveys. This allows the environment to be configured according to the user's stress level, reducing stress and providing a more comfortable toilet experience.

[0103] The restroom assistance system can further include a music selection unit that estimates the user's emotional state and selects music based on that emotional state. The music selection unit, for example, analyzes the user's facial expressions and voice to grasp the user's emotional state in real time. The emotion estimation function uses facial expression recognition, voice analysis, and biometrics. The classification and evaluation criteria for the emotional state are based on emotional categories such as joy, sadness, and anger. This allows the system to automatically select music according to the user's emotional state, providing a more comfortable restroom experience.

[0104] The toilet usage support system can further include a health linking unit that links the user's health data with medical institutions and supports remote diagnosis. The health linking unit, for example, sends the user's body temperature and heart rate data to the medical institution for diagnosis. The type and collection method of health data are determined based on body temperature, heart rate, blood pressure, etc. The method of linking with the medical institution is determined based on the data sharing method and security measures. In this way, by linking the user's health data with the medical institution and supporting remote diagnosis, the user's health can be managed more effectively.

[0105] The restroom assistance system can further include a scent selection unit that estimates the user's emotional state and selects a scent based on that emotional state. The scent selection unit, for example, analyzes the user's facial expressions and voice to grasp the user's emotional state in real time. The emotion estimation function uses facial expression recognition, voice analysis, and biometrics. The classification and evaluation criteria for the emotional state are based on emotional categories such as joy, sadness, and anger. This allows the system to automatically select a scent that matches the user's emotional state, providing a more comfortable restroom experience.

[0106] The toilet usage support system can further include a learning unit that analyzes the user's behavioral patterns over the long term and learns optimal settings according to the season and time of day. The learning unit, for example, builds a system that analyzes the user's behavioral patterns over the long term and learns optimal settings according to the season and time of day. For example, it learns a warm temperature setting in winter and a cool temperature setting in summer. The method of analyzing behavioral patterns is based on daily behavior history and data collected by sensors. The criteria and method for settings according to the season and time of day are based on the temperature settings for each season and the lighting settings for each time of day. In this way, by analyzing the user's behavioral patterns over the long term and learning optimal settings according to the season and time of day, it is possible to provide a more personalized and comfortable toilet experience.

[0107] The toilet usage assistance system can further include an emotion learning unit that estimates the user's emotional state and learns habits based on the emotional state. The emotion learning unit, for example, uses an emotion estimation function to build a system that learns habits based on the user's emotional state. For example, it learns the environmental settings used when the user is relaxed and applies them to future visits. The emotion estimation function uses facial expression recognition, voice analysis, and biometrics. Classification and evaluation criteria for emotional states are based on emotion categories such as joy, sadness, and anger. This allows the system to learn habits based on the user's emotional state and provide a more personalized environment, thereby improving user satisfaction.

[0108] The toilet usage support system can further include a health optimization unit that optimizes the toilet environment according to the user's health condition. The health optimization unit adjusts the temperature and lighting based on the user's body temperature and heart rate data, for example. The health condition evaluation criteria are based on medical standards and health scores. The toilet environment is optimized by adjusting the temperature, lighting, fragrance, etc. This makes it possible to support the user's health management by optimizing the toilet environment according to the user's health condition and increasing the relaxation effect.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The environment optimization unit adjusts the lighting and temperature based on the user's instructions. For example, if the user instructs the unit to "brighten the lights," the environment optimization unit understands the instruction and adjusts the brightness of the lights. Similarly, if the user instructs the unit to "lower the temperature a little," the environment optimization unit adjusts the temperature in the toilet appropriately. Step 2: The music adjustment unit selects music based on the environment adjusted by the environment optimization unit. For example, if the user instructs the unit to "play relaxing music," the music adjustment unit selects and plays appropriate music. Step 3: The scent adjuster adjusts the scent based on the music selected by the music adjuster. For example, if the user requests a "floral scent," the scent adjuster adjusts the scent in the toilet. Step 4: The learning unit learns the user's preferences and habits based on the scent adjusted by the scent adjustment unit. For example, if the user prefers the same temperature setting or music every time, the learning unit will learn that pattern and automatically apply those settings from the next time onwards.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

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

[0117] The 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.

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

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 7, a 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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, in order to avoid confusion and to 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.

[0177] 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]

[0178] 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. an environmental optimization unit that adjusts lighting and temperature based on user instructions; a music adjustment unit that selects music based on the environment adjusted by the environment optimization unit; a fragrance adjustment unit that adjusts the fragrance based on the music selected by the music adjustment unit; a learning unit that learns the preferences and habits of the user based on the scent adjusted by the scent adjustment unit. A system characterized by:

2. The environment optimization unit Analyzing the tone and speed of the voice, estimating stress levels and adjusting the environment accordingly 2. The system of claim 1.

3. The environment optimization unit Connect to your smartphone to save your preferences in the cloud and apply the same settings to other toilets.

2. The system of claim 1.

4. The music adjustment unit Analyze heart rate and breathing patterns to select music with a high relaxing effect 2. The system of claim 1.

5. The learning unit Analyzes behavioral patterns over the long term and learns optimal settings according to season and time of day 2. The system of claim 1.

6. The Health Monitoring Department Supports health management by analyzing the relationship between emotional state and health status using emotion estimation function 2. The system of claim 1.

7. The Cleaning Management Department Use emotion estimation to adjust cleaning frequency based on emotional state 2. The system of claim 1.

8. The environment optimization unit Automatically configure the environment according to your emotional state using emotion estimation function 2. The system of claim 1.

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