System

Abstract

An object of a system according to an embodiment is to automatically arrange an optimal sleeping environment in consideration of a required sleeping time of a user.SOLUTION: A system according to an embodiment includes a required sleep time calculation unit, a calendar linking unit, and a night mode lighting control unit. The required sleep time calculation unit calculates a required sleep time of the user. The calendar linking unit sets a wake-up time and a bedtime from a schedule of the calendar based on the required sleep time calculated by the required sleep time calculation unit. The night mode lighting control unit switches the illumination to the night mode three hours before the bedtime set by the calendar linking 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 not adequately provided a sleep environment that takes into account the user's required sleep time, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically create an optimal sleeping environment in consideration of the amount of sleep required by the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a sleep requirement calculation unit, a calendar linkage unit, and a night mode lighting control unit. The sleep requirement calculation unit calculates the user's sleep requirement. The calendar linkage unit sets a wake-up time and a bedtime based on the calendar schedule based on the sleep requirement calculated by the sleep requirement calculation unit. The night mode lighting control unit switches the lighting to night mode three hours before the bedtime set by the calendar linkage unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically create an optimal sleeping environment by taking into account the amount of sleep required by 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) The sleep management system according to an embodiment of the present invention automatically calculates the amount of sleep a user needs, sets wake-up and bedtimes based on a calendar schedule, and uses night mode lighting to induce natural sleep. This allows the sleep management system to automatically create a user's sleeping environment and provide efficient sleep management.

[0029] A sleep management system according to an embodiment includes a sleep requirement calculation unit, a calendar linkage unit, and a night mode lighting control unit. The sleep requirement calculation unit calculates a user's sleep requirement. For example, the sleep requirement calculation unit calculates the user's sleep requirement using a general average based on basic information such as the user's age, gender, and lifestyle. The sleep requirement calculation unit can also calculate an optimal sleep duration for each individual user by continuously collecting and analyzing personal data. The calendar linkage unit sets a wake-up time and a bedtime based on a calendar schedule based on the sleep requirement calculated by the sleep requirement calculation unit. For example, the calendar linkage unit links with the user's work and personal calendars to set a wake-up time based on the schedule and automatically adjust the bedtime to ensure the required sleep duration. The night mode lighting control unit switches the lighting to night mode three hours before the bedtime set by the calendar linkage unit. For example, the night mode lighting control unit links with the user's home electrical system to switch to warm lighting with reduced blue light to naturally induce sleep. This allows the sleep management system according to an embodiment to automatically create a user's sleep environment and provide efficient sleep management. For example, users can effortlessly create an optimal sleeping environment and ensure high-quality sleep. Night mode lighting also improves sleep quality by guiding users into a natural sleep state. Furthermore, customized sleep management using personal data can provide the optimal sleeping environment for each individual user.

[0030] The sleep requirement calculation unit uses a generation AI to analyze the user's past sleep data and daytime activity data and calculate the optimal sleep time for each individual in real time. For example, the sleep requirement calculation unit collects the user's past sleep data and daytime activity data and analyzes it using the generation AI. For example, it analyzes which time of day the user is most active and calculates the optimal sleep time based on that data. The sleep requirement calculation unit also uses the generation AI to analyze the user's sleep patterns and daytime activity level in real time and dynamically adjust the required sleep time. For example, on days when the user exercises a lot, the required sleep time can be increased. The sleep requirement calculation unit also predicts the optimal sleep time based on the user's past sleep data and daytime activity data and suggests it in real time. For example, on days when the user is feeling stressed, the required sleep time can be increased. This allows the optimal sleep time to be calculated in real time based on the user's past data.

[0031] The sleep requirement calculation unit allows the user to input their dietary and exercise habits and dynamically adjusts the required sleep time based on that data. For example, the user inputs their daily dietary and exercise habits into the app, and the sleep requirement calculation unit dynamically adjusts the required sleep time based on that data. For example, on days when they consume a lot of caffeine, they can delay their bedtime. The sleep requirement calculation unit also analyzes the user's dietary and exercise habits and allows the generation AI to dynamically adjust the required sleep time. For example, on days when they exercise a lot, they can increase the required sleep time. The sleep requirement calculation unit also allows the generation AI to suggest the optimal sleep time based on the user's dietary and exercise habits. For example, they can adjust their bedtime depending on the timing and content of their meals. This allows the required sleep time to be dynamically adjusted based on the user's dietary and exercise habits.

[0032] The calendar linkage unit can use generation AI to predict a user's stress level from calendar schedules and suggest going to bed earlier the night before a high-stress day. The calendar linkage unit can, for example, use generation AI to predict a user's stress level from calendar schedules and suggest going to bed earlier the night before a high-stress day. For example, it can suggest going to bed earlier the night before an important meeting. The calendar linkage unit also analyzes calendar schedules, and the generation AI predicts a user's stress level and suggests going to bed earlier the night before a high-stress day. For example, it can suggest going to bed earlier the night before a presentation. The calendar linkage unit can also use generation AI to predict a user's stress level from calendar schedules and suggest going to bed earlier the night before a high-stress day. For example, it can suggest going to bed earlier the night before an exam. This makes it possible to predict stress levels based on calendar schedules and suggest appropriate bedtimes.

[0033] The calendar linkage unit can also automatically adjust the user's meal and exercise schedules based on the calendar schedule, allowing for overall health management. The calendar linkage unit can, for example, automatically adjust the user's meal and exercise schedules based on the calendar schedule, allowing for overall health management. For example, it can suggest a lighter meal before a busy day. The calendar linkage unit also analyzes the calendar schedule, and the generation AI automatically adjusts the user's meal and exercise schedules. For example, it can suggest a nutritious meal before a day when the user will be exercising heavily. The calendar linkage unit can also automatically adjust the user's meal and exercise schedules based on the calendar schedule, allowing for overall health management. For example, it can suggest a relaxing exercise before a day when the user is under a lot of stress. This allows for adjustment of the meal and exercise schedules based on the calendar schedule, allowing for overall health management.

[0034] The night mode lighting control unit can use the generation AI to analyze the user's past sleep data and lighting environment and automatically adjust the optimal night mode lighting settings. The night mode lighting control unit can, for example, use the generation AI to analyze the user's past sleep data and lighting environment and automatically adjust the optimal night mode lighting settings. For example, it can suggest lighting settings that will help the user relax. The night mode lighting control unit can also use the generation AI to suggest the optimal night mode lighting settings based on the user's past sleep data and lighting environment. For example, it can adjust the color temperature of the lighting that will help the user to relax the most. The night mode lighting control unit can also use the generation AI to analyze the user's past sleep data and lighting environment and automatically adjust the optimal night mode lighting settings. For example, it can adjust the brightness of the lighting that will help the user to relax. This makes it possible to automatically adjust the optimal night mode lighting settings based on the user's past sleep data and lighting environment.

[0035] The night mode lighting control unit can provide a relaxing environment by stimulating the five senses with music and fragrance in addition to the night mode lighting. For example, the night mode lighting control unit adds a function to automatically play relaxing music in addition to the night mode lighting. For example, it can play relaxing music before going to bed. The night mode lighting control unit can also add a function to automatically adjust fragrance in addition to the night mode lighting. For example, it can automatically diffuse a relaxing aroma. The night mode lighting control unit can also provide a relaxing environment by stimulating the five senses with music and fragrance in addition to the night mode lighting. For example, it can provide relaxing music and fragrance simultaneously. This allows the night mode lighting to be linked with music and fragrance in addition to the night mode lighting, providing a relaxing environment by stimulating the five senses.

[0036] The night mode lighting control unit can add a function to automatically adjust the optimal lighting settings according to the season and weather. The night mode lighting control unit can add a function to automatically adjust the optimal lighting settings according to the season and weather. For example, it can suggest warmer lighting in winter. The night mode lighting control unit also uses a generative AI to analyze the season and weather and automatically adjust the optimal lighting settings. For example, it can suggest brighter lighting on rainy days. The night mode lighting control unit can also add a function to automatically adjust the optimal lighting settings according to the season and weather. For example, it can suggest lighting with a cooler color temperature in summer. This makes it possible to automatically adjust the optimal lighting settings according to the season and weather.

[0037] The sleep requirement calculation unit incorporates the user's genetic information and can provide the optimal sleep time based on genetic factors. The sleep requirement calculation unit, for example, collects the user's genetic information, and the generation AI calculates the optimal sleep time based on genetic factors. For example, a shorter sleep time can be suggested for a user who is genetically suited to shorter sleep. The sleep requirement calculation unit also analyzes the user's genetic information, and the generation AI suggests the optimal sleep time. For example, a longer sleep time can be suggested for a user who is genetically suited to longer sleep. The sleep requirement calculation unit also calculates the optimal sleep time based on the user's genetic information, and performs individually customized sleep management. For example, the bedtime can be adjusted based on genetic factors. This makes it possible to provide the optimal sleep time based on the user's genetic information.

[0038] The sleep requirement calculation unit can also collect pet sleep data and add a function to synchronize the sleep rhythms of the pet and the user. The sleep requirement calculation unit, for example, collects pet sleep data and provides a function for the generation AI to synchronize the sleep rhythms of the user and the pet. For example, the user's bedtime can be adjusted to match the pet's sleep time. The sleep requirement calculation unit also analyzes the pet's sleep data and the generation AI makes suggestions to synchronize the sleep rhythms of the user and the pet. For example, the user's wake-up time can be set to match the pet's wake-up time. The sleep requirement calculation unit also provides a function for the generation AI to synchronize the sleep rhythms of the user and the pet based on the pet's sleep data. For example, the user's bedtime can be adjusted to match the pet's relaxing time. This makes it possible to provide a function for synchronizing the sleep rhythms of the pet and the user.

[0039] The calendar linkage unit can automatically implement jet lag countermeasures that match the user's travel plans by linking with the calendar. The calendar linkage unit, for example, analyzes the travel plans on the calendar, and the generation AI automatically implements jet lag countermeasures. For example, it can adjust bedtime before traveling to match local time. The calendar linkage unit also allows the generation AI to suggest jet lag countermeasures based on the user's travel plans. For example, it can adjust the sleep schedule during traveling to reduce jet lag. The calendar linkage unit also allows the generation AI to automatically implement jet lag countermeasures based on the travel plans on the calendar. For example, it can suggest light therapy before traveling to adjust the body clock. This allows jet lag countermeasures to be automatically implemented that match the user's travel plans.

[0040] The calendar linkage unit also links with the calendars of family members and housemates, and can suggest the optimal bedtime taking everyone's schedules into consideration. The calendar linkage unit, for example, links with the calendars of family members and housemates, and suggests the optimal bedtime taking everyone's schedules into consideration. For example, it can adjust so that everyone goes to bed at the same time. The calendar linkage unit also analyzes the calendars of family members and housemates, and the generation AI suggests the optimal bedtime taking everyone's schedules into consideration. For example, it can suggest going to bed at a time when everyone can relax. The calendar linkage unit also links with the calendars of family members and housemates, and suggests the optimal bedtime taking everyone's schedules into consideration. For example, it can adjust so that everyone wakes up at the same time. This makes it possible to suggest the optimal bedtime taking family members and housemates' schedules into consideration.

[0041] The personal data utilization unit allows the user to input their dietary and exercise habits and dynamically adjust the required sleep time based on that data. For example, the user inputs their daily dietary and exercise habits into the app, and the personal data utilization unit dynamically adjusts the required sleep time based on that data. For example, on days when they consume a lot of caffeine, they can delay their bedtime. The personal data utilization unit also analyzes the user's dietary and exercise habits and has the generation AI dynamically adjust the required sleep time. For example, on days when they exercise a lot, they can increase the required sleep time. The personal data utilization unit also allows the generation AI to suggest the optimal sleep time based on the user's dietary and exercise habits. For example, they can adjust their bedtime depending on the timing and content of meals. This allows the required sleep time to be dynamically adjusted based on the user's dietary and exercise habits.

[0042] The personal data utilization unit incorporates the user's genetic information and can provide the optimal sleep time based on genetic factors. The personal data utilization unit, for example, collects the user's genetic information, and the generation AI calculates the optimal sleep time based on genetic factors. For example, a shorter sleep time can be suggested for a user who is genetically suited to shorter sleep. The personal data utilization unit also analyzes the user's genetic information, and the generation AI suggests the optimal sleep time. For example, a longer sleep time can be suggested for a user who is genetically suited to longer sleep. The personal data utilization unit also calculates the optimal sleep time based on the user's genetic information, and performs individually customized sleep management. For example, bedtime can be adjusted based on genetic factors. This makes it possible to provide the optimal sleep time based on the user's genetic information.

[0043] The personal data utilization unit can also collect pet sleep data and add a function to synchronize the sleep rhythms of the pet and the user. The personal data utilization unit, for example, collects pet sleep data and provides a function in which the generation AI synchronizes the sleep rhythms of the user and the pet. For example, the user's bedtime can be adjusted to match the time the pet sleeps. The personal data utilization unit also analyzes the pet's sleep data and the generation AI makes suggestions to synchronize the sleep rhythms of the user and the pet. For example, the user's wake-up time can be set to match the time the pet wakes up. The personal data utilization unit also provides a function in which the generation AI synchronizes the sleep rhythms of the user and the pet based on the pet's sleep data. For example, the user's bedtime can be adjusted to match the time the pet is relaxing. This makes it possible to provide a function to synchronize the sleep rhythms of the pet and the user.

[0044] The efficient sleep management unit uses a generation AI to analyze a user's past sleep data and daytime activity data and calculate the optimal sleep duration for each individual in real time. For example, the efficient sleep management unit collects the user's past sleep data and daytime activity data and analyzes it using a generation AI. For example, it can analyze which time of day the user is most active and calculate the optimal sleep duration based on that data. The efficient sleep management unit also uses a generation AI to analyze the user's sleep patterns and daytime activity level in real time and dynamically adjust the required sleep duration. For example, on days when the user exercises a lot, the required sleep duration can be increased. The efficient sleep management unit also uses a generation AI to predict the optimal sleep duration based on the user's past sleep data and daytime activity data and recommend it in real time. For example, on days when the user is feeling stressed, the required sleep duration can be increased. This allows the optimal sleep duration to be calculated in real time based on the user's past sleep data and daytime activity data.

[0045] The efficient sleep management unit allows users to input their dietary and exercise habits and dynamically adjusts the required sleep time based on that data. For example, the efficient sleep management unit allows users to input their daily dietary and exercise habits into the app and dynamically adjusts the required sleep time based on that data. For example, on days when caffeine intake is high, bedtime can be delayed. The efficient sleep management unit also analyzes the user's dietary and exercise habits and uses the generation AI to dynamically adjust the required sleep time. For example, on days when the user exercises a lot, the required sleep time can be increased. The efficient sleep management unit also allows the generation AI to suggest the optimal sleep time based on the user's dietary and exercise habits. For example, bedtime can be adjusted depending on the timing and content of meals. This allows the required sleep time to be dynamically adjusted based on the user's dietary and exercise habits.

[0046] The efficient sleep management unit incorporates the user's genetic information and can provide the optimal sleep time based on genetic factors. For example, the efficient sleep management unit collects the user's genetic information and the generation AI calculates the optimal sleep time based on genetic factors. For example, a shorter sleep time can be suggested for a user who is genetically suited to shorter sleep. The efficient sleep management unit also analyzes the user's genetic information and the generation AI suggests the optimal sleep time. For example, a longer sleep time can be suggested for a user who is genetically suited to longer sleep. The efficient sleep management unit also calculates the optimal sleep time based on the user's genetic information and performs individually customized sleep management. For example, the bedtime can be adjusted based on genetic factors. This makes it possible to provide the optimal sleep time based on the user's genetic information.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The sleep management system may further include an environmental sensor unit that monitors the user's sleep environment. The environmental sensor unit may, for example, measure the room temperature, humidity, and noise level in real time and adjust the settings to provide an optimal sleep environment. For example, if the temperature is too high, the air conditioner may be automatically adjusted, and if the humidity is too low, the humidifier may be activated. Also, if the noise level is high, white noise may be played to reduce the noise. This allows the user to always sleep in an optimal sleep environment.

[0049] The sleep management system may further include an evaluation unit that evaluates the quality of the user's sleep. The evaluation unit may, for example, monitor the user's heart rate, breathing rate, and body movements to evaluate the quality of sleep. For example, a stable heart rate may be determined to be a deep sleep state, and an irregular breathing rate may be determined to be a light sleep state. Furthermore, a high level of body movement may be determined to be low quality of sleep, and a low level of body movement may be determined to be high quality of sleep. This allows the user to understand the quality of their sleep and receive advice on how to improve it.

[0050] The sleep management system may further include a dream recording unit that records the user's dreams while sleeping. The dream recording unit may, for example, record the content of the dream by voice input immediately after the user wakes up. For example, when the user speaks the content of the dream, the content is converted into text and recorded. The dream recording unit may also allow the user to input the content of the dream by handwriting. This allows the user to record the content of their dream and look back on it later.

[0051] The sleep management system may further include a temperature sensor unit that monitors the user's body temperature while sleeping. The temperature sensor unit may, for example, measure the user's body temperature in real time and adjust the temperature to provide an optimal sleeping environment. For example, if the user's body temperature is too high, a cooling seat may be activated, and if the body temperature is too low, a heating seat may be activated. The temperature sensor unit may also record fluctuations in the user's body temperature and use the data to evaluate the quality of sleep. This allows the user to always sleep at an optimal body temperature.

[0052] The sleep management system may further include an EEG sensor unit that monitors the user's brain waves while they sleep. The EEG sensor unit can, for example, measure the user's brain waves in real time and evaluate the depth of sleep. For example, stable brain waves can be determined to be a deep sleep state, and disturbed brain waves can be determined to be a light sleep state. The EEG sensor unit can also record fluctuations in the user's brain waves and use the data to evaluate the quality of sleep. This allows the user to understand the depth of their sleep and receive advice on how to improve it.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The sleep requirement calculation unit calculates the user's required sleep time. For example, the sleep requirement calculation unit calculates the required sleep time using general average values ​​based on basic information such as the user's age, gender, and lifestyle. The sleep requirement calculation unit can also calculate the optimal sleep time for each individual user by continuously collecting and analyzing the user's personal data. Step 2: The calendar linking unit sets the wake-up time and bedtime from the calendar schedule based on the required sleep time calculated by the required sleep time calculation unit. For example, the calendar linking unit links with the user's work and personal calendars, sets the wake-up time based on the schedule, and automatically adjusts the bedtime to ensure the required sleep time. Step 3: The night mode lighting control unit switches the lighting to night mode three hours before the bedtime set by the calendar linkage unit. For example, the night mode lighting control unit works with the home's electrical system to switch to warm lighting with reduced blue light, helping to induce sleep more naturally.

[0055] (Example 2) The sleep management system according to an embodiment of the present invention automatically calculates the amount of sleep a user needs, sets wake-up and bedtimes based on a calendar schedule, and uses night mode lighting to induce natural sleep. This allows the sleep management system to automatically create a user's sleeping environment and provide efficient sleep management.

[0056] A sleep management system according to an embodiment includes a sleep requirement calculation unit, a calendar linkage unit, and a night mode lighting control unit. The sleep requirement calculation unit calculates a user's sleep requirement. For example, the sleep requirement calculation unit calculates the user's sleep requirement using a general average based on basic information such as the user's age, gender, and lifestyle. The sleep requirement calculation unit can also calculate an optimal sleep duration for each individual user by continuously collecting and analyzing personal data. The calendar linkage unit sets a wake-up time and a bedtime based on a calendar schedule based on the sleep requirement calculated by the sleep requirement calculation unit. For example, the calendar linkage unit links with the user's work and personal calendars to set a wake-up time based on the schedule and automatically adjust the bedtime to ensure the required sleep duration. The night mode lighting control unit switches the lighting to night mode three hours before the bedtime set by the calendar linkage unit. For example, the night mode lighting control unit links with the user's home electrical system to switch to warm lighting with reduced blue light to naturally induce sleep. This allows the sleep management system according to an embodiment to automatically create a user's sleep environment and provide efficient sleep management. For example, users can effortlessly create an optimal sleeping environment and ensure high-quality sleep. Night mode lighting also improves sleep quality by guiding users into a natural sleep state. Furthermore, customized sleep management using personal data can provide the optimal sleeping environment for each individual user.

[0057] The sleep requirement calculation unit uses a generation AI to analyze the user's past sleep data and daytime activity data and calculate the optimal sleep time for each individual in real time. For example, the sleep requirement calculation unit collects the user's past sleep data and daytime activity data and analyzes it using the generation AI. For example, it analyzes which time of day the user is most active and calculates the optimal sleep time based on that data. The sleep requirement calculation unit also uses the generation AI to analyze the user's sleep patterns and daytime activity level in real time and dynamically adjust the required sleep time. For example, on days when the user exercises a lot, the required sleep time can be increased. The sleep requirement calculation unit also predicts the optimal sleep time based on the user's past sleep data and daytime activity data and suggests it in real time. For example, on days when the user is feeling stressed, the required sleep time can be increased. This allows the optimal sleep time to be calculated in real time based on the user's past data.

[0058] The sleep requirement calculation unit allows the user to input their dietary and exercise habits and dynamically adjusts the required sleep time based on that data. For example, the user inputs their daily dietary and exercise habits into the app, and the sleep requirement calculation unit dynamically adjusts the required sleep time based on that data. For example, on days when they consume a lot of caffeine, they can delay their bedtime. The sleep requirement calculation unit also analyzes the user's dietary and exercise habits and allows the generation AI to dynamically adjust the required sleep time. For example, on days when they exercise a lot, they can increase the required sleep time. The sleep requirement calculation unit also allows the generation AI to suggest the optimal sleep time based on the user's dietary and exercise habits. For example, they can adjust their bedtime depending on the timing and content of their meals. This allows the required sleep time to be dynamically adjusted based on the user's dietary and exercise habits.

[0059] The required sleep time calculation unit can use the emotion estimation function to analyze the user's emotional state and suggest increasing the required sleep time if stress or anxiety is high. The required sleep time calculation unit can, for example, use the emotion estimation function to analyze the user's emotional state in real time and suggest increasing the required sleep time if stress or anxiety is high. For example, on days when the user is feeling stressed, the user can go to bed earlier. The required sleep time calculation unit also analyzes the user's emotional state and the generation AI dynamically adjusts the required sleep time. For example, on days when the user is feeling anxious, the required sleep time can be increased. The required sleep time calculation unit can also use the emotion estimation function to analyze the user's emotional state and suggest increasing the required sleep time if stress or anxiety is high. For example, on days when the user is relaxed, the required sleep time can be reduced. This allows the required sleep time to be adjusted based on the user's emotional state.

[0060] The calendar linkage unit can use generation AI to predict a user's stress level from calendar schedules and suggest going to bed earlier the night before a high-stress day. The calendar linkage unit can, for example, use generation AI to predict a user's stress level from calendar schedules and suggest going to bed earlier the night before a high-stress day. For example, it can suggest going to bed earlier the night before an important meeting. The calendar linkage unit also analyzes calendar schedules, and the generation AI predicts a user's stress level and suggests going to bed earlier the night before a high-stress day. For example, it can suggest going to bed earlier the night before a presentation. The calendar linkage unit can also use generation AI to predict a user's stress level from calendar schedules and suggest going to bed earlier the night before a high-stress day. For example, it can suggest going to bed earlier the night before an exam. This makes it possible to predict stress levels based on calendar schedules and suggest appropriate bedtimes.

[0061] The calendar linkage unit can also automatically adjust the user's meal and exercise schedules based on the calendar schedule, allowing for overall health management. The calendar linkage unit can, for example, automatically adjust the user's meal and exercise schedules based on the calendar schedule, allowing for overall health management. For example, it can suggest a lighter meal before a busy day. The calendar linkage unit also analyzes the calendar schedule, and the generation AI automatically adjusts the user's meal and exercise schedules. For example, it can suggest a nutritious meal before a day when the user will be exercising heavily. The calendar linkage unit can also automatically adjust the user's meal and exercise schedules based on the calendar schedule, allowing for overall health management. For example, it can suggest a relaxing exercise before a day when the user is under a lot of stress. This allows for adjustment of the meal and exercise schedules based on the calendar schedule, allowing for overall health management.

[0062] The calendar linkage unit can use the emotion estimation function to suggest relaxing activities before important appointments, thereby reducing stress before going to bed. The calendar linkage unit can, for example, use the emotion estimation function to suggest relaxing activities before important appointments, thereby reducing stress before going to bed. For example, it can provide guidance on meditation or deep breathing. The calendar linkage unit can also use a generation AI to analyze calendar appointments and suggest relaxing activities before important appointments. For example, it can provide relaxing music or podcasts. The calendar linkage unit can also use the emotion estimation function to suggest relaxing activities before important appointments, thereby reducing stress before going to bed. For example, it can suggest aromatherapy or a warm bath. This allows the calendar linkage unit to suggest relaxing activities before important appointments, thereby reducing stress before going to bed.

[0063] The night mode lighting control unit can use the generation AI to analyze the user's past sleep data and lighting environment and automatically adjust the optimal night mode lighting settings. The night mode lighting control unit can, for example, use the generation AI to analyze the user's past sleep data and lighting environment and automatically adjust the optimal night mode lighting settings. For example, it can suggest lighting settings that will help the user relax. The night mode lighting control unit can also use the generation AI to suggest the optimal night mode lighting settings based on the user's past sleep data and lighting environment. For example, it can adjust the color temperature of the lighting that will help the user to relax the most. The night mode lighting control unit can also use the generation AI to analyze the user's past sleep data and lighting environment and automatically adjust the optimal night mode lighting settings. For example, it can adjust the brightness of the lighting that will help the user to relax. This makes it possible to automatically adjust the optimal night mode lighting settings based on the user's past sleep data and lighting environment.

[0064] The night mode lighting control unit can use the emotion estimation function to set lighting according to the user's emotional state and create a relaxing environment. The night mode lighting control unit can, for example, use the emotion estimation function to set lighting according to the user's emotional state and create a relaxing environment. For example, if the user is feeling stressed, it can suggest warm lighting. The night mode lighting control unit also analyzes the user's emotional state, and the generation AI sets lighting according to the emotion. For example, if the user is relaxed, it can suggest soft lighting. The night mode lighting control unit also uses the emotion estimation function to set lighting according to the user's emotional state and create a relaxing environment. For example, if the user is feeling anxious, it can suggest dimmer lighting. In this way, it is possible to set lighting according to the user's emotional state and create a relaxing environment.

[0065] The night mode lighting control unit can provide a relaxing environment by stimulating the five senses with music and fragrance in addition to the night mode lighting. For example, the night mode lighting control unit adds a function to automatically play relaxing music in addition to the night mode lighting. For example, it can play relaxing music before going to bed. The night mode lighting control unit can also add a function to automatically adjust fragrance in addition to the night mode lighting. For example, it can automatically diffuse a relaxing aroma. The night mode lighting control unit can also provide a relaxing environment by stimulating the five senses with music and fragrance in addition to the night mode lighting. For example, it can provide relaxing music and fragrance simultaneously. This allows the night mode lighting to be linked with music and fragrance in addition to the night mode lighting, providing a relaxing environment by stimulating the five senses.

[0066] The night mode lighting control unit can add a function to automatically adjust the optimal lighting settings according to the season and weather. The night mode lighting control unit can add a function to automatically adjust the optimal lighting settings according to the season and weather. For example, it can suggest warmer lighting in winter. The night mode lighting control unit also uses a generative AI to analyze the season and weather and automatically adjust the optimal lighting settings. For example, it can suggest brighter lighting on rainy days. The night mode lighting control unit can also add a function to automatically adjust the optimal lighting settings according to the season and weather. For example, it can suggest lighting with a cooler color temperature in summer. This makes it possible to automatically adjust the optimal lighting settings according to the season and weather.

[0067] The night mode lighting control unit can use the emotion estimation function to learn lighting settings that the user finds particularly relaxing and provide an individually customized night mode. The night mode lighting control unit can, for example, use the emotion estimation function to learn lighting settings that the user finds particularly relaxing and provide an individually customized night mode. For example, it can learn the color temperature that the user finds most relaxing. The night mode lighting control unit can also analyze the user's emotional state and use a generation AI to learn lighting settings that the user finds particularly relaxing and provide an individually customized night mode. For example, it can learn the brightness that the user finds most relaxing. The night mode lighting control unit can also use the emotion estimation function to learn lighting settings that the user finds particularly relaxing and provide an individually customized night mode. For example, it can learn the lighting arrangement that the user finds most relaxing. This allows it to learn lighting settings that the user finds particularly relaxing and provide an individually customized night mode.

[0068] The sleep requirement calculation unit incorporates the user's genetic information and can provide the optimal sleep time based on genetic factors. The sleep requirement calculation unit, for example, collects the user's genetic information, and the generation AI calculates the optimal sleep time based on genetic factors. For example, a shorter sleep time can be suggested for a user who is genetically suited to shorter sleep. The sleep requirement calculation unit also analyzes the user's genetic information, and the generation AI suggests the optimal sleep time. For example, a longer sleep time can be suggested for a user who is genetically suited to longer sleep. The sleep requirement calculation unit also calculates the optimal sleep time based on the user's genetic information, and performs individually customized sleep management. For example, the bedtime can be adjusted based on genetic factors. This makes it possible to provide the optimal sleep time based on the user's genetic information.

[0069] The sleep requirement calculation unit can also collect pet sleep data and add a function to synchronize the sleep rhythms of the pet and the user. The sleep requirement calculation unit, for example, collects pet sleep data and provides a function for the generation AI to synchronize the sleep rhythms of the user and the pet. For example, the user's bedtime can be adjusted to match the pet's sleep time. The sleep requirement calculation unit also analyzes the pet's sleep data and the generation AI makes suggestions to synchronize the sleep rhythms of the user and the pet. For example, the user's wake-up time can be set to match the pet's wake-up time. The sleep requirement calculation unit also provides a function for the generation AI to synchronize the sleep rhythms of the user and the pet based on the pet's sleep data. For example, the user's bedtime can be adjusted to match the pet's relaxing time. This makes it possible to provide a function for synchronizing the sleep rhythms of the pet and the user.

[0070] The required sleep time calculation unit can use the emotion estimation function to identify time periods when the user is relaxed and adjust the bedtime to match that time period. The required sleep time calculation unit can, for example, use the emotion estimation function to identify time periods when the user is relaxed and adjust the bedtime to match that time period. For example, it can suggest going to bed at a time when the user is relaxed. The required sleep time calculation unit can also analyze the user's emotional state, identify time periods when the generation AI is relaxed, and adjust the bedtime to match that time period. For example, it can suggest going to bed at a time when the user is relaxed. The required sleep time calculation unit can also use the emotion estimation function to identify time periods when the user is relaxed and adjust the bedtime to match that time period. For example, it can suggest going to bed at a time when the user is relaxed. This makes it possible to adjust the bedtime to match the time period when the user is relaxed.

[0071] The calendar linkage unit can automatically implement jet lag countermeasures that match the user's travel plans by linking with the calendar. The calendar linkage unit, for example, analyzes the travel plans on the calendar, and the generation AI automatically implements jet lag countermeasures. For example, it can adjust bedtime before traveling to match local time. The calendar linkage unit also allows the generation AI to suggest jet lag countermeasures based on the user's travel plans. For example, it can adjust the sleep schedule during traveling to reduce jet lag. The calendar linkage unit also allows the generation AI to automatically implement jet lag countermeasures based on the travel plans on the calendar. For example, it can suggest light therapy before traveling to adjust the body clock. This allows jet lag countermeasures to be automatically implemented that match the user's travel plans.

[0072] The calendar linkage unit also links with the calendars of family members and housemates, and can suggest the optimal bedtime taking everyone's schedules into consideration. The calendar linkage unit, for example, links with the calendars of family members and housemates, and suggests the optimal bedtime taking everyone's schedules into consideration. For example, it can adjust so that everyone goes to bed at the same time. The calendar linkage unit also analyzes the calendars of family members and housemates, and the generation AI suggests the optimal bedtime taking everyone's schedules into consideration. For example, it can suggest going to bed at a time when everyone can relax. The calendar linkage unit also links with the calendars of family members and housemates, and suggests the optimal bedtime taking everyone's schedules into consideration. For example, it can adjust so that everyone wakes up at the same time. This makes it possible to suggest the optimal bedtime taking family members and housemates' schedules into consideration.

[0073] The calendar linkage unit can use the emotion estimation function to suggest relaxing music or podcasts for the night before an event the user is looking forward to. For example, the calendar linkage unit can use the emotion estimation function to suggest relaxing music or podcasts for the night before an event the user is looking forward to. For example, it can provide relaxing music that matches the user's preferences. Furthermore, the calendar linkage unit uses the generation AI to analyze calendar events and suggest relaxing music or podcasts for the night before an event the user is looking forward to. For example, it can provide relaxing podcasts. Furthermore, the calendar linkage unit can use the emotion estimation function to suggest relaxing music or podcasts for the night before an event the user is looking forward to. For example, it can provide relaxing music that matches the user's preferences. This makes it possible to suggest relaxing music or podcasts for the night before an event the user is looking forward to.

[0074] The personal data utilization unit allows the user to input their dietary and exercise habits and dynamically adjust the required sleep time based on that data. For example, the user inputs their daily dietary and exercise habits into the app, and the personal data utilization unit dynamically adjusts the required sleep time based on that data. For example, on days when they consume a lot of caffeine, they can delay their bedtime. The personal data utilization unit also analyzes the user's dietary and exercise habits and has the generation AI dynamically adjust the required sleep time. For example, on days when they exercise a lot, they can increase the required sleep time. The personal data utilization unit also allows the generation AI to suggest the optimal sleep time based on the user's dietary and exercise habits. For example, they can adjust their bedtime depending on the timing and content of meals. This allows the required sleep time to be dynamically adjusted based on the user's dietary and exercise habits.

[0075] The personal data utilization unit can use the emotion estimation function to analyze the user's emotional state and make a suggestion to increase the required sleep time if stress or anxiety is high. The personal data utilization unit can, for example, use the emotion estimation function to analyze the user's emotional state in real time and make a suggestion to increase the required sleep time if stress or anxiety is high. For example, on days when the user is feeling stressed, the user can go to bed earlier. The personal data utilization unit also analyzes the user's emotional state and the generation AI dynamically adjusts the required sleep time. For example, on days when the user is feeling anxious, the required sleep time can be increased. The personal data utilization unit can also use the emotion estimation function to analyze the user's emotional state and make a suggestion to increase the required sleep time if stress or anxiety is high. For example, on days when the user is relaxed, the required sleep time can be reduced. This makes it possible to adjust the required sleep time based on the user's emotional state.

[0076] The personal data utilization unit incorporates the user's genetic information and can provide the optimal sleep time based on genetic factors. The personal data utilization unit, for example, collects the user's genetic information, and the generation AI calculates the optimal sleep time based on genetic factors. For example, a shorter sleep time can be suggested for a user who is genetically suited to shorter sleep. The personal data utilization unit also analyzes the user's genetic information, and the generation AI suggests the optimal sleep time. For example, a longer sleep time can be suggested for a user who is genetically suited to longer sleep. The personal data utilization unit also calculates the optimal sleep time based on the user's genetic information, and performs individually customized sleep management. For example, bedtime can be adjusted based on genetic factors. This makes it possible to provide the optimal sleep time based on the user's genetic information.

[0077] The personal data utilization unit can also collect pet sleep data and add a function to synchronize the sleep rhythms of the pet and the user. The personal data utilization unit, for example, collects pet sleep data and provides a function in which the generation AI synchronizes the sleep rhythms of the user and the pet. For example, the user's bedtime can be adjusted to match the time the pet sleeps. The personal data utilization unit also analyzes the pet's sleep data and the generation AI makes suggestions to synchronize the sleep rhythms of the user and the pet. For example, the user's wake-up time can be set to match the time the pet wakes up. The personal data utilization unit also provides a function in which the generation AI synchronizes the sleep rhythms of the user and the pet based on the pet's sleep data. For example, the user's bedtime can be adjusted to match the time the pet is relaxing. This makes it possible to provide a function to synchronize the sleep rhythms of the pet and the user.

[0078] The personal data utilization unit can use the emotion estimation function to identify time periods when the user is relaxed and adjust the bedtime to match that time period. The personal data utilization unit can, for example, use the emotion estimation function to identify time periods when the user is relaxed and adjust the bedtime to match that time period. For example, it can suggest going to bed at a time when the user is relaxed. The personal data utilization unit can also analyze the user's emotional state, identify time periods when the generation AI is relaxed, and adjust the bedtime to match that time period. For example, it can suggest going to bed at a time when the user is relaxed. The personal data utilization unit can also use the emotion estimation function to identify time periods when the user is relaxed and adjust the bedtime to match that time period. For example, it can suggest going to bed at a time when the user is relaxed. This makes it possible to adjust the bedtime to match the time period when the user is relaxed.

[0079] The efficient sleep management unit uses a generation AI to analyze a user's past sleep data and daytime activity data and calculate the optimal sleep duration for each individual in real time. For example, the efficient sleep management unit collects the user's past sleep data and daytime activity data and analyzes it using a generation AI. For example, it can analyze which time of day the user is most active and calculate the optimal sleep duration based on that data. The efficient sleep management unit also uses a generation AI to analyze the user's sleep patterns and daytime activity level in real time and dynamically adjust the required sleep duration. For example, on days when the user exercises a lot, the required sleep duration can be increased. The efficient sleep management unit also uses a generation AI to predict the optimal sleep duration based on the user's past sleep data and daytime activity data and recommend it in real time. For example, on days when the user is feeling stressed, the required sleep duration can be increased. This allows the optimal sleep duration to be calculated in real time based on the user's past sleep data and daytime activity data.

[0080] The efficient sleep management unit allows users to input their dietary and exercise habits and dynamically adjusts the required sleep time based on that data. For example, the efficient sleep management unit allows users to input their daily dietary and exercise habits into the app and dynamically adjusts the required sleep time based on that data. For example, on days when caffeine intake is high, bedtime can be delayed. The efficient sleep management unit also analyzes the user's dietary and exercise habits and uses the generation AI to dynamically adjust the required sleep time. For example, on days when the user exercises a lot, the required sleep time can be increased. The efficient sleep management unit also allows the generation AI to suggest the optimal sleep time based on the user's dietary and exercise habits. For example, bedtime can be adjusted depending on the timing and content of meals. This allows the required sleep time to be dynamically adjusted based on the user's dietary and exercise habits.

[0081] The efficient sleep management unit uses an emotion estimation function to analyze the user's emotional state and suggest increasing the required sleep time if stress or anxiety is high. The efficient sleep management unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and suggest increasing the required sleep time if stress or anxiety is high. For example, on days when the user is feeling stressed, the user can go to bed earlier. The efficient sleep management unit also analyzes the user's emotional state and the generation AI dynamically adjusts the required sleep time. For example, on days when the user is feeling anxious, the required sleep time can be increased. The efficient sleep management unit also uses the emotion estimation function to analyze the user's emotional state and suggest increasing the required sleep time if stress or anxiety is high. For example, on days when the user is relaxed, the required sleep time can be reduced. This makes it possible to adjust the required sleep time based on the user's emotional state.

[0082] The efficient sleep management unit incorporates the user's genetic information and can provide the optimal sleep time based on genetic factors. For example, the efficient sleep management unit collects the user's genetic information and the generation AI calculates the optimal sleep time based on genetic factors. For example, a shorter sleep time can be suggested for a user who is genetically suited to shorter sleep. The efficient sleep management unit also analyzes the user's genetic information and the generation AI suggests the optimal sleep time. For example, a longer sleep time can be suggested for a user who is genetically suited to longer sleep. The efficient sleep management unit also calculates the optimal sleep time based on the user's genetic information and performs individually customized sleep management. For example, the bedtime can be adjusted based on genetic factors. This makes it possible to provide the optimal sleep time based on the user's genetic information.

[0083] The efficient sleep management unit can use the emotion estimation function to identify time periods when the user is relaxed and adjust the bedtime to match that time period. The efficient sleep management unit can, for example, use the emotion estimation function to identify time periods when the user is relaxed and adjust the bedtime to match that time period. For example, it can suggest going to bed at a time when the user is relaxed. The efficient sleep management unit can also analyze the user's emotional state, identify time periods when the generation AI is relaxed, and adjust the bedtime to match that time period. For example, it can suggest going to bed at a time when the user is relaxed. The efficient sleep management unit can also use the emotion estimation function to identify time periods when the user is relaxed and adjust the bedtime to match that time period. For example, it can suggest going to bed at a time when the user is relaxed. This makes it possible to adjust the bedtime to match the time period when the user is relaxed.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The sleep management system may further include an environmental sensor unit that monitors the user's sleep environment. The environmental sensor unit may, for example, measure the room temperature, humidity, and noise level in real time and adjust the settings to provide an optimal sleep environment. For example, if the temperature is too high, the air conditioner may be automatically adjusted, and if the humidity is too low, the humidifier may be activated. Also, if the noise level is high, white noise may be played to reduce the noise. This allows the user to always sleep in an optimal sleep environment.

[0086] The sleep management system may further include an evaluation unit that evaluates the quality of the user's sleep. The evaluation unit may, for example, monitor the user's heart rate, breathing rate, and body movements to evaluate the quality of sleep. For example, a stable heart rate may be determined to be a deep sleep state, and an irregular breathing rate may be determined to be a light sleep state. Furthermore, a high level of body movement may be determined to be low quality of sleep, and a low level of body movement may be determined to be high quality of sleep. This allows the user to understand the quality of their sleep and receive advice on how to improve it.

[0087] The sleep management system may further include a dream recording unit that records the user's dreams while sleeping. The dream recording unit may, for example, record the content of the dream by voice input immediately after the user wakes up. For example, when the user speaks the content of the dream, the content is converted into text and recorded. The dream recording unit may also allow the user to input the content of the dream by handwriting. This allows the user to record the content of their dream and look back on it later.

[0088] The sleep management system may further include a temperature sensor unit that monitors the user's body temperature while sleeping. The temperature sensor unit may, for example, measure the user's body temperature in real time and adjust the temperature to provide an optimal sleeping environment. For example, if the user's body temperature is too high, a cooling seat may be activated, and if the body temperature is too low, a heating seat may be activated. The temperature sensor unit may also record fluctuations in the user's body temperature and use the data to evaluate the quality of sleep. This allows the user to always sleep at an optimal body temperature.

[0089] The sleep management system may further include an EEG sensor unit that monitors the user's brain waves while they sleep. The EEG sensor unit can, for example, measure the user's brain waves in real time and evaluate the depth of sleep. For example, stable brain waves can be determined to be a deep sleep state, and disturbed brain waves can be determined to be a light sleep state. The EEG sensor unit can also record fluctuations in the user's brain waves and use the data to evaluate the quality of sleep. This allows the user to understand the depth of their sleep and receive advice on how to improve it.

[0090] The sleep management system can further estimate the user's emotional state and provide an optimal sleeping environment based on the estimated emotion. For example, if the user is feeling stressed, relaxing music can be played, and if the user is relaxed, a quiet environment can be provided. Also, if the user is feeling anxious, lighting that gives a sense of security can be provided, and if the user is relaxed, soft lighting can be provided. This allows the user to sleep in an optimal sleeping environment according to their emotional state.

[0091] The sleep management system can further estimate the user's emotional state and suggest optimal sleep duration based on the estimated emotion. For example, if the user is feeling stressed, a longer sleep duration can be suggested, and if the user is relaxed, a normal sleep duration can be suggested. Also, if the user is feeling anxious, an earlier bedtime can be suggested, and if the user is relaxed, a normal bedtime can be suggested. This allows the user to ensure optimal sleep duration according to their emotional state.

[0092] The sleep management system can further estimate the user's emotional state and suggest optimal pre-sleep activities based on the estimated emotions. For example, if the user is feeling stressed, the system can suggest relaxing meditation or deep breathing, and if the user is relaxed, the system can suggest light stretching. Also, if the user is feeling anxious, the system can suggest listening to relaxing music, and if the user is relaxed, the system can suggest quiet reading. This allows the user to choose the optimal pre-sleep activity according to their emotional state.

[0093] The sleep management system can further estimate the user's emotional state and provide an optimal sound environment for sleep based on the estimated emotion. For example, if the user is feeling stressed, relaxing white noise can be played, and if the user is relaxed, a quiet environment can be provided. Also, if the user is feeling anxious, reassuring music can be played, and if the user is relaxed, nature sounds can be played. This allows the user to sleep in the optimal sound environment according to their emotional state.

[0094] The sleep management system can further estimate the user's emotional state and provide an optimal fragrant environment for sleeping based on the estimated emotion. For example, if the user is feeling stressed, a relaxing lavender scent can be provided, and if the user is relaxed, a soft floral scent can be provided. Also, if the user is feeling anxious, a reassuring citrus scent can be provided, and if the user is relaxed, a natural scent can be provided. This allows the user to sleep in the optimal fragrant environment according to their emotional state.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The sleep requirement calculation unit calculates the user's required sleep time. For example, the sleep requirement calculation unit calculates the required sleep time using general average values ​​based on basic information such as the user's age, gender, and lifestyle. The sleep requirement calculation unit can also calculate the optimal sleep time for each individual user by continuously collecting and analyzing the user's personal data. Step 2: The calendar linking unit sets the wake-up time and bedtime from the calendar schedule based on the required sleep time calculated by the required sleep time calculation unit. For example, the calendar linking unit links with the user's work and personal calendars, sets the wake-up time based on the schedule, and automatically adjusts the bedtime to ensure the required sleep time. Step 3: The night mode lighting control unit switches the lighting to night mode three hours before the bedtime set by the calendar linkage unit. For example, the night mode lighting control unit works with the home's electrical system to switch to warm lighting with reduced blue light, helping to induce sleep more naturally.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0125] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0127] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0129] The data processing system 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.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0163] 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]

[0164] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a required sleep time calculation unit that calculates the required sleep time of a user; a calendar linking unit that sets a wake-up time and a bedtime from a calendar schedule based on the required sleep time calculated by the required sleep time calculation unit; a night mode lighting control unit that switches the lighting to night mode three hours before the bedtime set by the calendar linkage unit. A system characterized by:

2. The required sleep time calculation unit Using a generation AI, the system analyzes the user's past sleep data and daytime activity data to calculate the optimal sleep time for each individual in real time.

2. The system of claim 1.

3. The required sleep time calculation unit The user's dietary and exercise habits are input, and the required sleep time is dynamically adjusted based on that data.

2. The system of claim 1.

4. The required sleep time calculation unit Analyzing the user's emotional state and proposing to increase the required sleep time if stress or anxiety is high 2. The system of claim 1.

5. The calendar linkage unit is Using generative AI to predict the user's stress level based on the calendar events, the system suggests going to bed earlier the night before a stressful day.

2. The system of claim 1.

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