System

Abstract

An object of a system according to an embodiment is to analyze sleep data of a user and provide a reminder and a relaxation technique that are individually suitable.SOLUTION: A system according to an embodiment includes a sleep data collection unit, an analysis unit, a reminder providing unit, a relaxation providing unit, a stress analysis unit, and a mental health support unit. The sleep data collection unit collects sleep data of a user. The analysis unit analyzes the sleep data collected by the sleep data collection unit. The reminder providing unit provides a reminder suitable for the user on the basis of the data analyzed by the analysis unit. The relaxation providing unit provides a relaxation technique according to the situation of the user. The stress analysis unit analyzes a stress level of the user. The mental health support unit supports mental health of the user.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 do not adequately provide individually tailored reminders and relaxation techniques based on a user's sleep data, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze a user's sleep data and provide individually appropriate reminders and relaxation techniques. [Means for solving the problem]

[0006] The system according to the embodiment includes a sleep data collection unit, an analysis unit, a reminder provision unit, a relaxation provision unit, a stress analysis unit, and a mental health support unit. The sleep data collection unit collects sleep data of a user. The analysis unit analyzes the sleep data collected by the sleep data collection unit. The reminder provision unit provides a reminder appropriate for the user based on the data analyzed by the analysis unit. The relaxation provision unit provides relaxation techniques according to the user's situation. The stress analysis unit analyzes the user's stress level. The mental health support unit supports the user's mental health. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the user's sleep data and provide personalized reminders and relaxation techniques. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A sleep support system according to an embodiment of the present invention supports a user's sleep and promotes healthy sleep habits. The system analyzes the user's sleep patterns and conditions and provides individually tailored sleep reminders and relaxation techniques. It also reduces stress and supports mental health, helping the user achieve quality sleep with peace of mind. In this way, the sleep support system can support the user's sleep and promote healthy sleep habits.

[0029] A sleep support system according to an embodiment includes a sleep data collection unit, an analysis unit, a reminder provision unit, a relaxation provision unit, a stress analysis unit, and a mental health support unit. The sleep data collection unit collects a user's sleep data. For example, the sleep data collection unit collects data such as sleep duration, time to fall asleep, time to wake up, and sleep quality recorded by the user using a smartphone or wearable device. The analysis unit analyzes the sleep data collected by the sleep data collection unit. For example, a generation AI analyzes the user's sleep data to understand the user's sleep patterns and conditions. The reminder provision unit provides appropriate reminders to the user based on the data analyzed by the analysis unit. For example, the reminder provision unit sends a bedtime reminder so that the user can go to bed at the same time every night. The relaxation provision unit provides relaxation techniques tailored to the user's situation. For example, if the user is feeling stressed, the relaxation provision unit suggests meditation or yoga techniques. The stress analysis unit analyzes the user's stress level. For example, the generation AI analyzes the user's stress level and provides appropriate support. The mental health support unit supports the user's mental health. For example, the generating AI analyzes the user's mental health status and provides appropriate support, thereby enabling the sleep support system according to the embodiment to support the user's sleep and promote healthy sleeping habits.

[0030] The analysis unit can perform a detailed analysis of sleep stages based on the sleep data and calculate the percentage of each stage. For example, the analysis unit uses a generative AI to analyze the user's sleep data and identify each sleep stage, such as light sleep, deep sleep, and REM sleep. For example, it can identify each stage based on fluctuations in heart rate and breathing patterns and calculate their percentage. This allows for a detailed analysis of the user's sleep stages and the calculation of the percentage of each stage.

[0031] The analysis unit can collect lifestyle habits as additional data and analyze the correlation with sleep patterns. For example, the analysis unit records the user's meal contents and intake times, and the generation AI analyzes the correlation between that data and sleep patterns. For example, it can clarify the relationship between caffeine intake and the time it takes to fall asleep. This makes it possible to analyze the correlation between the user's lifestyle habits and sleep patterns.

[0032] The reminder providing unit can use the generation AI to predict the optimal bedtime from the user's past sleep data and provide an individually customized reminder. The reminder providing unit, for example, uses the generation AI to analyze the user's past sleep data and predict the optimal bedtime. For example, the reminder providing unit identifies the time the user enters the deepest sleep from the past data and sends a reminder at that time. This makes it possible to predict the optimal bedtime from the user's past sleep data and provide an individually customized reminder.

[0033] The relaxation provider can use the generation AI to analyze physiological data such as the user's stress level and heart rate, and suggest optimal relaxation techniques. For example, the relaxation provider can use the generation AI to analyze the user's heart rate and stress level, and suggest optimal relaxation techniques. For example, if the heart rate is high, deep breathing or meditation can be suggested. This allows the user's physiological data such as stress level and heart rate to be analyzed, and optimal relaxation techniques can be suggested.

[0034] The stress analysis unit can use the generation AI to analyze the user's stress level in detail and provide advice to identify the cause of stress. The stress analysis unit can, for example, use the generation AI to analyze the user's stress level in detail and provide advice to identify the cause of stress. For example, if work-related stress is the cause, the stress analysis unit can suggest ways to reduce the work burden. This allows the user's stress level to be analyzed in detail and advice to be provided to identify the cause of stress.

[0035] The mental health support unit continuously monitors the user's mental health state and can immediately send an alert if an abnormality is detected. The mental health support unit, for example, uses a generative AI to build a system that continuously monitors the user's mental health state. For example, if an abnormality is detected, an alert is immediately sent. This makes it possible to continuously monitor the user's mental health state and immediately send an alert if an abnormality is detected.

[0036] The analysis unit compares sleep patterns for user groups of different age groups, genders, occupations, etc., and can identify trends specific to specific groups. For example, the analysis unit collects sleep data from users of different age groups, and the generation AI compares the sleep patterns of each age group. For example, it can identify differences in sleep depth and duration between younger and older people. This makes it possible to compare sleep patterns for user groups of different age groups, genders, occupations, etc., and identify trends specific to specific groups.

[0037] The analysis unit anonymizes the user's sleep data, shares it on the cloud, and performs comparative analysis with other users, thereby enabling an understanding of trends in general sleep patterns. The analysis unit, for example, builds a system that anonymizes the user's sleep data and shares it on the cloud. The generation AI performs comparative analysis with other users' data and enables an understanding of trends in general sleep patterns. As a result, the user's sleep data is anonymized, shared on the cloud, and performed comparative analysis with other users, enabling an understanding of trends in general sleep patterns.

[0038] The reminder providing unit can analyze the user's device usage patterns and provide a reminder to limit device usage. For example, the reminder providing unit analyzes the user's smartphone or tablet usage time, and the generation AI provides a reminder to limit device usage. For example, the reminder providing unit notifies the user to refrain from device use for one hour before going to bed. This allows the user's device usage patterns to be analyzed and a reminder to limit device usage to be provided.

[0039] The relaxation providing unit can analyze the user's past relaxation technique implementation history and prioritize suggesting techniques that were effective. For example, the relaxation providing unit collects the user's past relaxation technique implementation history, and the generation AI identifies techniques that were effective. For example, if meditation was effective, it will be suggested again. This allows the user's past relaxation technique implementation history to be analyzed and techniques that were effective to be prioritized.

[0040] The relaxation providing unit allows the suggested relaxation techniques to be customized according to the user's preferences, and can combine elements such as music and aromas. The relaxation providing unit uses, for example, a generation AI to build a system that customizes relaxation techniques according to the user's preferences. For example, it can suggest relaxation techniques that combine favorite music and aromas. This allows the suggested relaxation techniques to be customized according to the user's preferences, and can combine elements such as music and aromas.

[0041] The relaxation providing unit can provide interactive content to guide the user in performing relaxation techniques. For example, the relaxation providing unit uses a generative AI to build a system that provides interactive videos that guide the user in performing relaxation techniques. For example, the system provides videos on meditation or yoga. This makes it possible to provide interactive content to guide the user in performing relaxation techniques.

[0042] The mental health support unit allows the content of mental health support to be customized according to the user's preferences, and can combine elements such as music and art therapy. The mental health support unit, for example, uses generative AI to build a system that customizes mental health support according to the user's preferences. For example, it can provide support that combines favorite music and art therapy. This allows the content of mental health support to be customized according to the user's preferences, and can combine elements such as music and art therapy.

[0043] The mental health support department can provide interactive content to guide the implementation of mental health support. For example, the mental health support department uses generative AI to build a system that provides interactive videos that guide the implementation of mental health support. For example, videos of meditation or art therapy are provided. This makes it possible to provide interactive content to guide the implementation of mental health support.

[0044] The analysis unit can analyze the user's sleep environment data and propose optimal environment settings. For example, the analysis unit can use a generative AI to analyze the user's sleep environment data (temperature, humidity, lighting, etc.) and propose optimal environment settings. For example, it can provide advice on how to maintain an appropriate bedroom temperature. This allows the analysis of the user's sleep environment data and the proposal of optimal environment settings.

[0045] The analysis unit can analyze the user's past sleep environment data and prioritize suggesting environmental settings that were highly effective. For example, the analysis unit collects the user's past sleep environment data, and the generation AI identifies environmental settings that were highly effective. For example, if a specific temperature or humidity promotes good quality sleep, it will suggest it again. This allows the analysis unit to analyze the user's past sleep environment data and prioritize suggesting environmental settings that were highly effective.

[0046] The analysis unit makes the proposed environmental settings customizable according to the user's preferences, and can combine elements such as music and aromas. The analysis unit, for example, uses a generation AI to build a system that customizes environmental settings according to the user's preferences. For example, the analysis unit proposes environmental settings that combine favorite music and aromas. This makes the proposed environmental settings customizable according to the user's preferences, and can combine elements such as music and aromas.

[0047] The analysis unit can provide interactive content to guide users in setting up their environment. For example, the analysis unit uses a generative AI to build a system that provides interactive videos to guide users in setting up their environment. For example, a video explains how to adjust the temperature and humidity in a bedroom. This allows the provision of interactive content to guide users in setting up their environment.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The analysis unit can analyze the user's body temperature fluctuations during sleep based on the user's sleep data and support the selection of optimal bedding. For example, if the user's body temperature is high, it will suggest bedding with good breathability, and if the body temperature is low, it will suggest bedding with high heat retention. This can support the selection of optimal bedding according to the user's body temperature fluctuations.

[0050] The analysis unit can analyze the user's breathing patterns during sleep based on the user's sleep data and assess the risk of sleep apnea syndrome. For example, it can detect breathing cessation or abnormal patterns and recommend a medical examination if the risk is high. This allows the user's breathing patterns during sleep to be analyzed and the risk of sleep apnea syndrome to be assessed.

[0051] The analysis unit can combine and analyze a user's sleep data and exercise data to evaluate the impact of exercise habits on sleep. For example, it can analyze the quality of sleep on days when the user exercises a lot and suggest an appropriate amount of exercise. This makes it possible to clarify the relationship between the user's exercise habits and sleep and suggest an appropriate amount of exercise.

[0052] The analysis unit can combine and analyze the user's sleep data and dietary data to evaluate the impact of dietary content on sleep. For example, it can analyze the impact of specific foods on sleep quality and suggest appropriate dietary content. This makes it possible to clarify the relationship between the user's dietary content and sleep and suggest appropriate dietary content.

[0053] The analysis unit can combine and analyze the user's sleep data and environmental data to evaluate the impact of environmental factors on sleep. For example, it can analyze the impact of room temperature and humidity on sleep quality and suggest optimal environmental settings. This makes it possible to clarify the relationship between the user's environmental factors and sleep and suggest optimal environmental settings.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The sleep data collection unit collects the user's sleep data, such as the sleep duration, time to fall asleep, time to wake up, and sleep quality recorded by the user using a smartphone or wearable device. Step 2: The analysis unit analyzes the sleep data collected by the sleep data collection unit. For example, the generation AI analyzes the user's sleep data and understands their sleep patterns and conditions. Step 3: The reminder provider provides appropriate reminders to the user based on the data analyzed by the analyzer, such as sending a bedtime reminder to help the user go to bed at the same time every night. Step 4: The relaxation provider provides relaxation techniques tailored to the user's situation. For example, if the user is feeling stressed, it may suggest meditation or yoga. Step 5: The stress analysis unit analyzes the user's stress level. For example, the generation AI analyzes the user's stress level and provides appropriate support. Step 6: The mental health support unit supports the user's mental health. For example, the generation AI analyzes the user's mental health status and provides appropriate support.

[0056] (Example 2) A sleep support system according to an embodiment of the present invention supports a user's sleep and promotes healthy sleep habits. The system analyzes the user's sleep patterns and conditions and provides individually tailored sleep reminders and relaxation techniques. It also reduces stress and supports mental health, helping the user achieve quality sleep with peace of mind. In this way, the sleep support system can support the user's sleep and promote healthy sleep habits.

[0057] A sleep support system according to an embodiment includes a sleep data collection unit, an analysis unit, a reminder provision unit, a relaxation provision unit, a stress analysis unit, and a mental health support unit. The sleep data collection unit collects a user's sleep data. For example, the sleep data collection unit collects data such as sleep duration, time to fall asleep, time to wake up, and sleep quality recorded by the user using a smartphone or wearable device. The analysis unit analyzes the sleep data collected by the sleep data collection unit. For example, a generation AI analyzes the user's sleep data to understand the user's sleep patterns and conditions. The reminder provision unit provides appropriate reminders to the user based on the data analyzed by the analysis unit. For example, the reminder provision unit sends a bedtime reminder so that the user can go to bed at the same time every night. The relaxation provision unit provides relaxation techniques tailored to the user's situation. For example, if the user is feeling stressed, the relaxation provision unit suggests meditation or yoga techniques. The stress analysis unit analyzes the user's stress level. For example, the generation AI analyzes the user's stress level and provides appropriate support. The mental health support unit supports the user's mental health. For example, the generating AI analyzes the user's mental health status and provides appropriate support, thereby enabling the sleep support system according to the embodiment to support the user's sleep and promote healthy sleeping habits.

[0058] The analysis unit can perform a detailed analysis of sleep stages based on the sleep data and calculate the percentage of each stage. For example, the analysis unit uses a generative AI to analyze the user's sleep data and identify each sleep stage, such as light sleep, deep sleep, and REM sleep. For example, it can identify each stage based on fluctuations in heart rate and breathing patterns and calculate their percentage. This allows for a detailed analysis of the user's sleep stages and the calculation of the percentage of each stage.

[0059] The analysis unit can collect lifestyle habits as additional data and analyze the correlation with sleep patterns. For example, the analysis unit records the user's meal contents and intake times, and the generation AI analyzes the correlation between that data and sleep patterns. For example, it can clarify the relationship between caffeine intake and the time it takes to fall asleep. This makes it possible to analyze the correlation between the user's lifestyle habits and sleep patterns.

[0060] The analysis unit uses the emotion estimation function to analyze the relationship between the user's emotional state and sleep patterns, and can clarify the impact of emotional fluctuations on sleep. For example, the analysis unit uses the emotion estimation function to record the user's emotional state daily, and the generation AI analyzes the relationship between that data and sleep patterns. For example, it identifies improved sleep quality on days when the user has positive emotions. This makes it possible to analyze the relationship between the user's emotional state and sleep patterns and clarify the impact of emotional fluctuations on sleep.

[0061] The reminder providing unit can use the generation AI to predict the optimal bedtime from the user's past sleep data and provide an individually customized reminder. The reminder providing unit, for example, uses the generation AI to analyze the user's past sleep data and predict the optimal bedtime. For example, the reminder providing unit identifies the time the user enters the deepest sleep from the past data and sends a reminder at that time. This makes it possible to predict the optimal bedtime from the user's past sleep data and provide an individually customized reminder.

[0062] The relaxation provider can use the generation AI to analyze physiological data such as the user's stress level and heart rate, and suggest optimal relaxation techniques. For example, the relaxation provider can use the generation AI to analyze the user's heart rate and stress level, and suggest optimal relaxation techniques. For example, if the heart rate is high, deep breathing or meditation can be suggested. This allows the user's physiological data such as stress level and heart rate to be analyzed, and optimal relaxation techniques can be suggested.

[0063] The stress analysis unit can use the generation AI to analyze the user's stress level in detail and provide advice to identify the cause of stress. The stress analysis unit can, for example, use the generation AI to analyze the user's stress level in detail and provide advice to identify the cause of stress. For example, if work-related stress is the cause, the stress analysis unit can suggest ways to reduce the work burden. This allows the user's stress level to be analyzed in detail and advice to be provided to identify the cause of stress.

[0064] The mental health support unit continuously monitors the user's mental health state and can immediately send an alert if an abnormality is detected. The mental health support unit, for example, uses a generative AI to build a system that continuously monitors the user's mental health state. For example, if an abnormality is detected, an alert is immediately sent. This makes it possible to continuously monitor the user's mental health state and immediately send an alert if an abnormality is detected.

[0065] The mental health support unit uses the emotion estimation function to provide mental health support based on the user's emotional state, thereby stabilizing emotions. For example, the mental health support unit uses the emotion estimation function to analyze the user's emotional state in real time, and the generation AI provides mental health support according to the emotion. For example, when stress is high, relaxation methods are suggested. This allows for mental health support based on the user's emotional state to be provided, thereby stabilizing emotions.

[0066] The analysis unit compares sleep patterns for user groups of different age groups, genders, occupations, etc., and can identify trends specific to specific groups. For example, the analysis unit collects sleep data from users of different age groups, and the generation AI compares the sleep patterns of each age group. For example, it can identify differences in sleep depth and duration between younger and older people. This makes it possible to compare sleep patterns for user groups of different age groups, genders, occupations, etc., and identify trends specific to specific groups.

[0067] The analysis unit anonymizes the user's sleep data, shares it on the cloud, and performs comparative analysis with other users, thereby enabling an understanding of trends in general sleep patterns. The analysis unit, for example, builds a system that anonymizes the user's sleep data and shares it on the cloud. The generation AI performs comparative analysis with other users' data and enables an understanding of trends in general sleep patterns. As a result, the user's sleep data is anonymized, shared on the cloud, and performed comparative analysis with other users, enabling an understanding of trends in general sleep patterns.

[0068] The analysis unit uses the emotion estimation function to analyze the emotion of the user when entering sleep data in real time, thereby improving the accuracy of emotion-based data collection. The analysis unit, for example, uses the emotion estimation function to analyze the emotion of the user when entering sleep data in real time. For example, the analysis unit analyzes facial expressions and tone of voice at the time of entry and calculates an emotion score. This allows the emotion of the user when entering sleep data to be analyzed in real time, thereby improving the accuracy of emotion-based data collection.

[0069] The reminder providing unit can analyze the user's device usage patterns and provide a reminder to limit device usage. For example, the reminder providing unit analyzes the user's smartphone or tablet usage time, and the generation AI provides a reminder to limit device usage. For example, the reminder providing unit notifies the user to refrain from device use for one hour before going to bed. This allows the user's device usage patterns to be analyzed and a reminder to limit device usage to be provided.

[0070] The reminder providing unit can use the emotion estimation function to adjust the content of the reminder according to the user's emotional state and provide a reminder that elicits positive emotions. For example, the reminder providing unit can use the emotion estimation function to analyze the user's emotional state in real time, and the generation AI can adjust the content of the reminder according to the emotion. For example, the reminder for a stressful day can be changed to content that encourages relaxation. This allows the content of the reminder to be adjusted according to the user's emotional state, and a reminder that elicits positive emotions can be provided.

[0071] The relaxation providing unit can analyze the user's past relaxation technique implementation history and prioritize suggesting techniques that were effective. For example, the relaxation providing unit collects the user's past relaxation technique implementation history, and the generation AI identifies techniques that were effective. For example, if meditation was effective, it will be suggested again. This allows the user's past relaxation technique implementation history to be analyzed and techniques that were effective to be prioritized.

[0072] The relaxation providing unit uses the emotion estimation function to provide relaxation techniques according to the user's emotional state, thereby stabilizing emotions. For example, the relaxation providing unit uses the emotion estimation function to analyze the user's emotional state in real time, and the generation AI provides relaxation techniques according to the emotions. For example, meditation may be suggested when stress is high. This allows the unit to provide relaxation techniques according to the user's emotional state, thereby stabilizing emotions.

[0073] The relaxation providing unit allows the suggested relaxation techniques to be customized according to the user's preferences, and can combine elements such as music and aromas. The relaxation providing unit uses, for example, a generation AI to build a system that customizes relaxation techniques according to the user's preferences. For example, it can suggest relaxation techniques that combine favorite music and aromas. This allows the suggested relaxation techniques to be customized according to the user's preferences, and can combine elements such as music and aromas.

[0074] The relaxation providing unit can provide interactive content to guide the user in performing relaxation techniques. For example, the relaxation providing unit uses a generative AI to build a system that provides interactive videos that guide the user in performing relaxation techniques. For example, the system provides videos on meditation or yoga. This makes it possible to provide interactive content to guide the user in performing relaxation techniques.

[0075] The relaxation providing unit can use the emotion estimation function to monitor the effect of relaxation techniques in real time and adjust the techniques based on the user's emotional response. The relaxation providing unit, for example, uses the emotion estimation function to build a system that monitors the effect of relaxation techniques in real time. For example, it analyzes the user's emotional response and adjusts the techniques. This allows the effect of relaxation techniques to be monitored in real time and the techniques to be adjusted based on the user's emotional response.

[0076] The mental health support unit allows the content of mental health support to be customized according to the user's preferences, and can combine elements such as music and art therapy. The mental health support unit, for example, uses generative AI to build a system that customizes mental health support according to the user's preferences. For example, it can provide support that combines favorite music and art therapy. This allows the content of mental health support to be customized according to the user's preferences, and can combine elements such as music and art therapy.

[0077] The mental health support department can provide interactive content to guide the implementation of mental health support. For example, the mental health support department uses generative AI to build a system that provides interactive videos that guide the implementation of mental health support. For example, videos of meditation or art therapy are provided. This makes it possible to provide interactive content to guide the implementation of mental health support.

[0078] The mental health support unit can use the emotion estimation function to monitor the effectiveness of mental health support in real time and adjust the support content based on the user's emotional response. The mental health support unit, for example, uses the emotion estimation function to build a system that monitors the effectiveness of mental health support in real time. For example, it analyzes the user's emotional response and adjusts the support content. This makes it possible to monitor the effectiveness of mental health support in real time and adjust the support content based on the user's emotional response.

[0079] The analysis unit can analyze the user's sleep environment data and propose optimal environment settings. For example, the analysis unit can use a generative AI to analyze the user's sleep environment data (temperature, humidity, lighting, etc.) and propose optimal environment settings. For example, it can provide advice on how to maintain an appropriate bedroom temperature. This allows the analysis of the user's sleep environment data and the proposal of optimal environment settings.

[0080] The analysis unit can analyze the user's past sleep environment data and prioritize suggesting environmental settings that were highly effective. For example, the analysis unit collects the user's past sleep environment data, and the generation AI identifies environmental settings that were highly effective. For example, if a specific temperature or humidity promotes good quality sleep, it will suggest it again. This allows the analysis unit to analyze the user's past sleep environment data and prioritize suggesting environmental settings that were highly effective.

[0081] The analysis unit uses the emotion estimation function to provide environmental settings that correspond to the user's emotional state, thereby stabilizing emotions. For example, the analysis unit uses the emotion estimation function to analyze the user's emotional state in real time, and the generation AI provides environmental settings that correspond to the emotions. For example, when stress is high, the analysis unit suggests environmental settings that help users relax. This allows the analysis unit to provide environmental settings that correspond to the user's emotional state, thereby stabilizing emotions.

[0082] The analysis unit makes the proposed environmental settings customizable according to the user's preferences, and can combine elements such as music and aromas. The analysis unit, for example, uses a generation AI to build a system that customizes environmental settings according to the user's preferences. For example, the analysis unit proposes environmental settings that combine favorite music and aromas. This makes the proposed environmental settings customizable according to the user's preferences, and can combine elements such as music and aromas.

[0083] The analysis unit can provide interactive content to guide users in setting up their environment. For example, the analysis unit uses a generative AI to build a system that provides interactive videos to guide users in setting up their environment. For example, a video explains how to adjust the temperature and humidity in a bedroom. This allows the provision of interactive content to guide users in setting up their environment.

[0084] The analysis unit can use the emotion estimation function to monitor the effect of the environmental settings in real time and adjust the environmental settings based on the user's emotional response. The analysis unit, for example, uses the emotion estimation function to build a system that monitors the effect of the environmental settings in real time. For example, the analysis unit analyzes the user's emotional response and adjusts the environmental settings. This makes it possible to monitor the effect of the environmental settings in real time and adjust the environmental settings based on the user's emotional response.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The analysis unit can analyze the user's body temperature fluctuations during sleep based on the user's sleep data and support the selection of optimal bedding. For example, if the user's body temperature is high, it will suggest bedding with good breathability, and if the body temperature is low, it will suggest bedding with high heat retention. This can support the selection of optimal bedding according to the user's body temperature fluctuations.

[0087] The analysis unit can analyze the user's breathing patterns during sleep based on the user's sleep data and assess the risk of sleep apnea syndrome. For example, it can detect breathing cessation or abnormal patterns and recommend a medical examination if the risk is high. This allows the user's breathing patterns during sleep to be analyzed and the risk of sleep apnea syndrome to be assessed.

[0088] The analysis unit can combine and analyze a user's sleep data and exercise data to evaluate the impact of exercise habits on sleep. For example, it can analyze the quality of sleep on days when the user exercises a lot and suggest an appropriate amount of exercise. This makes it possible to clarify the relationship between the user's exercise habits and sleep and suggest an appropriate amount of exercise.

[0089] The analysis unit can combine and analyze the user's sleep data and dietary data to evaluate the impact of dietary content on sleep. For example, it can analyze the impact of specific foods on sleep quality and suggest appropriate dietary content. This makes it possible to clarify the relationship between the user's dietary content and sleep and suggest appropriate dietary content.

[0090] The analysis unit can combine and analyze the user's sleep data and environmental data to evaluate the impact of environmental factors on sleep. For example, it can analyze the impact of room temperature and humidity on sleep quality and suggest optimal environmental settings. This makes it possible to clarify the relationship between the user's environmental factors and sleep and suggest optimal environmental settings.

[0091] The analysis unit uses the emotion estimation function to analyze the user's emotional state and the content of dreams during sleep, and can clarify the impact of emotional fluctuations on dreams. For example, it can analyze the content of dreams on days when the user has positive emotions and provide advice on stabilizing emotions. This can clarify the relationship between the user's emotional state and the content of dreams, and can help stabilize emotions.

[0092] The analysis unit uses the emotion estimation function to analyze the user's emotional state and body movements during sleep, and can clarify the impact of emotional fluctuations on body movements. For example, it can analyze body movements on a stressful day and suggest relaxation methods. This can clarify the relationship between the user's emotional state and body movements, and help stabilize emotions.

[0093] The analysis unit uses the emotion estimation function to analyze the user's emotional state and heart rate during sleep, and can clarify the impact of emotional fluctuations on the heart rate. For example, it can analyze the heart rate on days when the user has positive emotions and provide advice on stabilizing emotions. This can clarify the relationship between the user's emotional state and heart rate and help stabilize emotions.

[0094] The analysis unit uses the emotion estimation function to analyze the user's emotional state and breathing patterns during sleep, and can clarify the impact of emotional fluctuations on breathing. For example, it can analyze breathing patterns on a stressful day and suggest relaxation methods. This can clarify the relationship between the user's emotional state and breathing patterns and help stabilize emotions.

[0095] The analysis unit uses the emotion estimation function to analyze the user's emotional state and brain waves during sleep, and can clarify the impact of emotional fluctuations on brain waves. For example, it can analyze brain waves on days when the user is feeling positive and provide advice on stabilizing emotions. This can clarify the relationship between the user's emotional state and brain waves, and can help stabilize emotions.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The sleep data collection unit collects the user's sleep data, such as the sleep duration, time to fall asleep, time to wake up, and sleep quality recorded by the user using a smartphone or wearable device. Step 2: The analysis unit analyzes the sleep data collected by the sleep data collection unit. For example, the generation AI analyzes the user's sleep data and understands their sleep patterns and conditions. Step 3: The reminder provider provides appropriate reminders to the user based on the data analyzed by the analyzer, such as sending a bedtime reminder to help the user go to bed at the same time every night. Step 4: The relaxation provider provides relaxation techniques tailored to the user's situation. For example, if the user is feeling stressed, it may suggest meditation or yoga. Step 5: The stress analysis unit analyzes the user's stress level. For example, the generation AI analyzes the user's stress level and provides appropriate support. Step 6: The mental health support unit supports the user's mental health. For example, the generation AI analyzes the user's mental health status and provides appropriate support.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0142] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] 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]

[0165] 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 sleep data collection unit that collects sleep data of a user; an analysis unit that analyzes the sleep data collected by the sleep data collection unit; a reminder providing unit that provides a reminder appropriate for the user based on the data analyzed by the analysis unit; a relaxation providing unit that provides a relaxation technique according to the user's situation; a stress analysis unit that analyzes the stress level of the user; a mental health support unit that supports the mental health of the user. A system characterized by:

2. The analysis unit A detailed sleep stage analysis is performed based on the sleep data, and the proportion of each stage is calculated.

2. The system of claim 1.

3. The reminder providing unit Using a generative AI, the optimal bedtime is predicted from the user's past sleep data, and the individually customized reminder is provided.

2. The system of claim 1.

4. The relaxation providing unit Using generative AI, the system analyzes physiological data such as the user's stress level and heart rate, and suggests optimal relaxation techniques.

2. The system of claim 1.

5. The stress analysis unit Using generative AI to analyze the user's stress level in detail and provide advice to identify the cause of stress.

2. The system of claim 1.

6. The analysis unit Analyze the user's sleep environment data (temperature, humidity, lighting, etc.) and suggest optimal environment settings 2. The system of claim 1.

7. The analysis unit Using the emotion estimation function, the relationship between the user's emotional state and sleep patterns is analyzed to clarify the impact of emotional fluctuations on sleep.

2. The system of claim 1.

8. The mental health support department: Using the emotion estimation function, mental health support based on the user's emotional state is provided to stabilize emotions.

2. The system of claim 1.

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