system

Abstract

An object of a system according to an embodiment is to generate a sound in accordance with a user's interest and taste and reproduce an appropriate sound in accordance with a sleep state.SOLUTION: A system according to an embodiment includes a sound generation unit, a feedback collection unit, a learning information update unit, a sleep state determination unit, a falling asleep sound reproduction unit, and a rising sound reproduction unit. The sound generation unit generates a sound in accordance with the user's interest and taste using the generation AI. The feedback collection unit collects feedback from the user for the generated sound. The learning information update unit updates the learning information of the generative AI based on the feedback. The sleep state determiner determines a sleep state of the user based on biological data acquired from the smartwatch. The falling asleep sound reproduction unit reproduces a sound suitable for falling asleep based on the sleep state, and stops the reproduction of the sound after reaching a deep sleep stage. The wake-up sound reproduction unit reproduces a sound suitable for waking up based on the REM sleep state.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 generated sounds that match the user's tastes and preferences, and have not adequately reproduced appropriate sounds depending on the sleep state, so there is room for improvement.

[0005] The system according to the embodiment aims to generate sounds that match the tastes and preferences of the user and to play appropriate sounds depending on the sleeping state. [Means for solving the problem]

[0006] The system according to the embodiment includes a sound generation unit, a feedback collection unit, a learning information update unit, a sleep state determination unit, a sleep onset sound playback unit, and a wake-up sound playback unit. The sound generation unit uses a generation AI to generate sounds tailored to the user's preferences. The feedback collection unit collects feedback from the user regarding the generated sounds. The learning information update unit updates the learning information of the generation AI based on the feedback. The sleep state determination unit determines the user's sleep state based on biometric data acquired from the smartwatch. The sleep onset sound playback unit plays sounds suitable for falling asleep based on the sleep state and stops playing the sounds after the user reaches a deep sleep stage. The wake-up sound playback unit plays sounds suitable for waking up based on the REM sleep state. [Effects of the Invention]

[0007] The system according to the embodiment can generate sounds that match the tastes and preferences of the user and can play appropriate sounds depending on the sleep state. [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 tech system according to an embodiment of the present invention uses a generation AI to generate sounds tailored to the user's preferences and optimizes sleep by linking a smartwatch and smartphone. By generating sounds tailored to the user's preferences and optimizing sleep, the sleep tech system can provide an inspiring sleep experience to people with sleep disorders.

[0029] The sleep tech system according to the embodiment includes a sound generation unit, a feedback collection unit, a learning information update unit, a sleep state determination unit, a sleep onset sound playback unit, and a wake-up sound playback unit. The sound generation unit uses a generation AI to generate sounds tailored to the user's preferences. For example, the generation AI generates optimal sleep onset sounds by combining natural sounds and synthetic sounds preferred by the user. The generation AI can also analyze the user's music playback history to generate more accurate music. The feedback collection unit collects user feedback on the generated sounds. For example, the user may provide feedback such as "This sound was relaxing." The learning information update unit updates the learning information of the generation AI based on the feedback. For example, the generation AI reflects the user's feedback in the next sound generation. The sleep state determination unit determines the user's sleep state based on biometric data acquired from the smartwatch. For example, it analyzes heart rate and body movement data to determine whether the user has entered deep sleep. The sleep onset sound playback unit plays sounds appropriate for falling asleep based on the sleep state and stops playing the sounds once the user reaches a deep sleep stage. For example, the system may play relaxing sounds until the user enters deep sleep, and automatically stop playing the sounds once the user has reached deep sleep. The wake-up sound playback unit plays sounds suitable for waking up based on the REM sleep state. For example, the system may play the wake-up sound in the REM sleep state closest to the wake-up time set by the user. In this way, the sleep tech system according to the embodiment can provide an inspiring sleep experience to people with sleep disorders by generating sounds tailored to the user's preferences and optimizing the sleep state.

[0030] The sound generation unit analyzes the user's past music playback history and streaming service data to generate music with higher accuracy. For example, the sound generation unit analyzes the user's past music playback history to understand trends in specific genres and artists. For example, the generation AI generates similar music based on the artists and genres the user has played most frequently in the past. The sound generation unit also uses streaming service data to extract characteristics of the user's favorite playlists and songs. For example, it analyzes tempo, key, frequency of instrument use, etc., and the generation AI generates music based on that. The sound generation unit also integrates the user's playback history and streaming service data to generate music with higher accuracy. For example, it analyzes the user's musical preferences during a specific time period and generates music that is optimal for that time period. This enables more accurate music generation by analyzing the user's past music playback history and streaming service data.

[0031] The sound generation unit can generate sounds optimal for the user's condition on that day based on the user's daily activity data. For example, the sound generation unit analyzes exercise data obtained from the user's smartwatch and generates sounds optimal for post-exercise recovery. For example, it generates music that promotes relaxation after exercise. The sound generation unit also analyzes the user's stress level and generates sounds effective for stress reduction. For example, it generates natural sounds that have a relaxing effect when stress is high. The sound generation unit also comprehensively analyzes the user's daily activity data and generates sounds optimal for the user's condition on that day. For example, it generates music with a high relaxing effect on days when fatigue is building up, and generates music that energizes the user on days when energy is high. This provides a more personalized music experience by generating sounds optimal for the user's condition on that day based on the user's daily activity data.

[0032] The sound generation unit can analyze sound patterns preferred by the user and generate new sounds by combining different genres of music and environmental sounds. The sound generation unit, for example, analyzes the user's favorite music genres and environmental sounds and generates new sounds by combining them. For example, it generates a relaxing sound by combining classical music and natural sounds. The sound generation unit also combines music of different genres to provide a new musical experience. For example, it generates music that combines jazz and electronica. The sound generation unit also analyzes the user's favorite sound patterns and generates new sounds by combining different environmental sounds. For example, it generates a relaxing sound by combining the sound of waves and birds chirping. In this way, a more diverse musical experience is provided by analyzing the user's favorite sound patterns and generating new sounds by combining different genres of music and environmental sounds.

[0033] The sound generation unit can provide not only sound but also relaxing images based on the user's preferences. The sound generation unit provides relaxing images, for example, in sync with the user's favorite music or environmental sounds. For example, an image of the ocean can be played along with the sound of waves. The sound generation unit can also combine music with visual elements to provide a more relaxing experience. For example, an image of a forest can be played along with the sound of birds chirping. The sound generation unit can also provide content that combines sound and images based on the user's preferences. For example, an image of an art museum can be played along with classical music. In this way, a more relaxing experience can be provided by providing not only sound but also relaxing images based on the user's preferences.

[0034] The feedback collection unit can analyze user feedback and individually customize the generation algorithm. The feedback collection unit, for example, analyzes user feedback and individually customizes the sound generation algorithm. For example, it reflects the sound characteristics preferred by a particular user. The feedback collection unit also adjusts the generation algorithm based on the feedback data to generate a sound that is more suited to the user's preferences. For example, it learns the characteristics of sounds that the user finds relaxing. The feedback collection unit also analyzes user feedback and generates individually customized sounds. For example, it reflects the user's preferred tempo and frequency of instrument use. In this way, by analyzing user feedback and individually customizing the sound generation algorithm, a more personalized musical experience is provided.

[0035] The feedback collection unit can analyze the content of the feedback using natural language processing technology and extract specific areas for improvement. The feedback collection unit, for example, analyzes user feedback using natural language processing technology and extracts specific areas for improvement. For example, it analyzes feedback such as "The sound is a little loud" and adjusts the volume. The feedback collection unit also analyzes the content of the feedback and identifies areas for improvement in the generation algorithm. For example, based on feedback such as "I want a more relaxing sound," it generates a sound with a high relaxing effect. The feedback collection unit also uses natural language processing technology to analyze the content of the feedback and extracts specific areas for improvement. For example, based on feedback such as "This sound was relaxing," it generates a similar sound. In this way, by analyzing the content of the feedback using natural language processing technology and extracting specific areas for improvement, more effective music generation is possible.

[0036] The feedback collection unit can share the feedback with other users and collect community-based improvement suggestions. The feedback collection unit, for example, shares the user's feedback with other users and collects community-based improvement suggestions. For example, the feedback collection unit publishes the feedback and collects opinions from other users. The feedback collection unit also collects community-based feedback and uses it to improve the generation algorithm. For example, based on feedback from multiple users, it identifies common areas for improvement. The feedback collection unit also builds a system for sharing feedback and collecting community-based improvement suggestions. For example, it holds discussions based on the feedback and extracts areas for improvement. In this way, by sharing the feedback with other users and collecting community-based improvement suggestions, more effective music generation is possible.

[0037] The feedback collection unit can improve not only sound generation but also the UI / UX of the app based on the feedback. The feedback collection unit improves not only sound generation but also the UI / UX of the app based on user feedback, for example. For example, the UI is improved based on feedback such as "The operation is difficult to understand." The feedback collection unit also analyzes the feedback and identifies areas for improvement in the UI / UX of the app. For example, the UX is improved based on feedback such as "I wish it were easier to operate." The feedback collection unit also makes comprehensive improvements to sound generation and the UI / UX of the app based on the feedback. For example, the UI is improved based on feedback such as "It's become easier to select sounds." In this way, more effective music generation is possible by improving not only sound generation but also the UI / UX of the app based on feedback.

[0038] The sleep state determination unit can automatically adjust the user's sleep environment based on data acquired from the smartwatch. The sleep state determination unit automatically adjusts the user's sleep environment based on, for example, data acquired from the smartwatch. For example, it maintains the temperature and humidity at optimal levels. The sleep state determination unit also analyzes the data from the smartwatch and builds a system that automatically adjusts the user's sleep environment. For example, it adjusts the amount of light to provide a comfortable sleep environment. The sleep state determination unit also automatically adjusts the sleep environment based on the user's sleep data. For example, it controls an air conditioner or humidifier to maintain the temperature and humidity at optimal levels. In this way, the user's sleep environment is automatically adjusted based on the data acquired from the smartwatch, thereby providing a more comfortable sleep environment.

[0039] The sleep state determination unit can analyze smartwatch data and track the user's sleep patterns over the long term. The sleep state determination unit, for example, analyzes data obtained from the smartwatch and tracks the user's sleep patterns over the long term. For example, it records the daily sleep time and the percentage of deep sleep. The sleep state determination unit also analyzes the user's sleep patterns based on the long-term sleep data and identifies areas for improvement. For example, it provides advice for improving sleep quality. The sleep state determination unit also builds a system that tracks smartwatch data over the long term and analyzes the user's sleep patterns. For example, it evaluates sleep quality and suggests areas for improvement. In this way, analyzing smartwatch data and tracking the user's sleep patterns over the long term enables more effective sleep improvement.

[0040] The sleep state determination unit can work in conjunction with the smartwatch to analyze the user's exercise data and generate sounds that are optimal for post-exercise recovery. The sleep state determination unit, for example, analyzes exercise data acquired from the smartwatch and generates sounds that are optimal for post-exercise recovery. For example, it plays music that has a relaxing effect. The sleep state determination unit also builds a system that generates sounds that are optimal for post-exercise recovery based on the user's exercise data. For example, it provides music that is suitable for stretching and relaxation. The sleep state determination unit also analyzes the exercise data from the smartwatch and generates sounds that are optimal for post-exercise recovery. For example, it provides recovery sounds that correspond to the heart rate and exercise intensity. This allows for more effective recovery by analyzing the user's exercise data in conjunction with the smartwatch and generating sounds that are optimal for post-exercise recovery.

[0041] The sleep state determination unit can comprehensively evaluate the user's health state based on the data from the smartwatch and provide health advice. The sleep state determination unit, for example, comprehensively evaluates the user's health state based on data acquired from the smartwatch and provides health advice. For example, it evaluates the quality of sleep and the amount of exercise and suggests areas for improvement. The sleep state determination unit also analyzes the user's health data and builds a system that performs a comprehensive health evaluation. For example, it evaluates the health state based on heart rate, body temperature, and amount of exercise. The sleep state determination unit also comprehensively evaluates the user's health state based on the data from the smartwatch and provides individual health advice. For example, it provides advice on diet and exercise. This enables more effective health management by comprehensively evaluating the user's health state based on the data from the smartwatch and providing health advice.

[0042] The wake-up sound playback unit can be added with a function to analyze brainwave data to more accurately detect REM sleep states. The wake-up sound playback unit, for example, adds a function to analyze brainwave data acquired from a smartwatch to more accurately detect REM sleep states. For example, it detects specific brainwave patterns and determines REM sleep states. The wake-up sound playback unit also analyzes brainwave data to build a system that accurately detects REM sleep states. For example, it monitors brainwave fluctuations in real time and identifies REM sleep states. The wake-up sound playback unit also accurately detects REM sleep states based on the brainwave data acquired from the smartwatch. For example, it analyzes specific frequency bands of brainwaves and determines REM sleep states. By adding a function to analyze brainwave data to more accurately detect REM sleep states, more effective wake-ups are possible.

[0043] The wake-up sound playback unit can provide a multimodal wake-up method that combines light and vibration in addition to wake-up sound. The wake-up sound playback unit provides a multimodal wake-up method that combines light and vibration in addition to wake-up sound, for example. For example, it uses light that gradually brightens along with music. The wake-up sound playback unit also builds a system that provides a multimodal wake-up method. For example, it wakes up the user using vibration from a smart watch along with music. The wake-up sound playback unit also provides a multimodal wake-up method that combines sound, light, and vibration in addition to wake-up sound. For example, it uses a smart light that gradually brightens along with music. This provides a multimodal wake-up method that combines light and vibration in addition to wake-up sound, enabling a more effective wake-up.

[0044] The wake-up sound reproduction unit can suggest an optimal sleep time based on the user's daily schedule in addition to waking up in REM sleep. The wake-up sound reproduction unit, for example, analyzes the user's daily schedule and suggests an optimal sleep time. For example, it suggests an optimal bedtime based on an important appointment for the next day. The wake-up sound reproduction unit also builds a system that suggests an optimal sleep time for the user based on schedule data. For example, it calculates an optimal sleep time based on the appointment for the next day. The wake-up sound reproduction unit also takes the user's daily schedule into consideration and suggests an optimal sleep time. For example, it suggests going to bed early in preparation for an important meeting the next day. This enables more effective sleep management by suggesting an optimal sleep time based on the user's daily schedule in addition to waking up in REM sleep.

[0045] The wake-up sound playback unit generates sounds and music that are optimal for the refreshing time after waking up, thereby supporting the user in starting their day. The wake-up sound playback unit generates sounds and music that are optimal for the refreshing time after waking up, for example, to support the user in starting their day. For example, it plays invigorating music. The wake-up sound playback unit also builds a system that generates sounds that are optimal for the refreshing time after waking up. For example, it plays music that has a relaxing effect to support a comfortable start to the day. The wake-up sound playback unit also generates sounds that are optimal for the refreshing time after waking up to support the user in starting their day. For example, it plays invigorating music to start the day in a positive mood. In this way, it generates sounds and music that are optimal for the refreshing time after waking up, supporting the user in starting their day, thereby providing a more comfortable start to the day.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The Sleep Tech System can further include an environmental adjustment unit that automatically adjusts the user's sleep environment. The environmental adjustment unit automatically adjusts the user's sleep environment based on data acquired from, for example, a smartwatch. For example, it controls an air conditioner or humidifier to maintain optimal temperature and humidity. The environmental adjustment unit can also adjust the amount of light to provide a comfortable sleep environment. For example, it can gradually dim the light until the user enters deep sleep and gradually brighten it when the user wakes up. This automatically adjusts the user's sleep environment to provide a more comfortable sleep experience.

[0048] The sleep tech system can further include a health evaluation unit that comprehensively evaluates the user's health condition. The health evaluation unit, for example, evaluates the user's health condition based on data obtained from a smartwatch and provides health advice. For example, it evaluates the quality of sleep and the amount of exercise and suggests areas for improvement. The health evaluation unit also analyzes the user's health data and builds a system that performs a comprehensive health evaluation. For example, it evaluates the health condition based on heart rate, body temperature, and amount of exercise. This allows for a comprehensive evaluation of the user's health condition and provides health advice, enabling more effective health management.

[0049] The sleep tech system can further include a schedule suggestion unit that suggests an optimal sleep time based on the user's daily schedule. The schedule suggestion unit, for example, analyzes the user's daily schedule and suggests an optimal sleep time. For example, it suggests an optimal bedtime based on important plans for the next day. The schedule suggestion unit also builds a system that suggests the user's optimal sleep time based on schedule data. For example, it calculates the optimal sleep time based on the next day's plans. This allows for more effective sleep management by suggesting an optimal sleep time based on the user's daily schedule.

[0050] The Sleep Tech System may further include a feedback sharing unit that shares user feedback with other users and collects community-based improvement suggestions. The feedback sharing unit, for example, shares user feedback with other users and collects community-based improvement suggestions. For example, the feedback sharing unit publishes the feedback and collects opinions from other users. The feedback sharing unit also collects community-based feedback and uses it to improve the generation algorithm. For example, the feedback sharing unit identifies common areas for improvement based on feedback from multiple users. This allows for more effective music generation by sharing feedback with other users and collecting community-based improvement suggestions.

[0051] The Sleep Tech System can further include a UI / UX improvement unit that improves not only sound generation but also the UI / UX of the app based on feedback. The UI / UX improvement unit improves not only sound generation but also the UI / UX of the app based on user feedback. For example, the UI is improved based on feedback such as "The operation is difficult to understand." The UI / UX improvement unit also analyzes the feedback and identifies areas for improvement in the app's UI / UX. For example, the UX is improved based on feedback such as "I wish it were easier to operate." In this way, more effective music generation becomes possible by improving not only sound generation but also the app's UI / UX based on feedback.

[0052] The Sleep Tech System may further include a wake-up support unit that provides a multimodal wake-up method that combines light and vibration in addition to wake-up sound. The wake-up support unit provides a multimodal wake-up method that combines light and vibration in addition to wake-up sound, for example, by using gradually brightening light along with music. The wake-up support unit also constructs a system that provides a multimodal wake-up method. For example, it wakes up the user using music along with vibrations from a smartwatch. This provides a multimodal wake-up method that combines light and vibration in addition to wake-up sound, enabling a more effective wake-up.

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

[0054] Step 1: The sound generation unit uses AI to generate sounds tailored to the user's preferences. For example, AI can generate optimal sleep sounds by combining natural sounds and synthetic sounds that the user likes. AI can also analyze the user's music playback history to generate more accurate music. Step 2: The feedback collection unit collects feedback from the user about the generated sound. For example, the user provides feedback such as "This sound is relaxing." Step 3: The learning information update unit updates the learning information of the generation AI based on the feedback. For example, the generation AI reflects the user's feedback in the next sound generation. Step 4: The sleep state determination unit determines the user's sleep state based on the biological data acquired from the smartwatch. For example, it analyzes heart rate and body movement data to determine whether the user is in deep sleep. Step 5: The sleep sound player plays sounds suitable for falling asleep based on the sleep state, and stops playing the sounds once the deep sleep stage is reached. For example, it plays relaxing sounds until the user enters deep sleep, and automatically stops playing the sounds once deep sleep is reached. Step 6: The wake-up sound playback unit plays a sound suitable for waking up based on the REM sleep state, for example, the wake-up sound is played in the REM sleep state closest to the wake-up time set by the user.

[0055] (Example 2) The sleep tech system according to an embodiment of the present invention uses a generation AI to generate sounds tailored to the user's preferences and optimizes sleep by linking a smartwatch and smartphone. By generating sounds tailored to the user's preferences and optimizing sleep, the sleep tech system can provide an inspiring sleep experience to people with sleep disorders.

[0056] The sleep tech system according to the embodiment includes a sound generation unit, a feedback collection unit, a learning information update unit, a sleep state determination unit, a sleep onset sound playback unit, and a wake-up sound playback unit. The sound generation unit uses a generation AI to generate sounds tailored to the user's preferences. For example, the generation AI generates optimal sleep onset sounds by combining natural sounds and synthetic sounds preferred by the user. The generation AI can also analyze the user's music playback history to generate more accurate music. The feedback collection unit collects user feedback on the generated sounds. For example, the user may provide feedback such as "This sound was relaxing." The learning information update unit updates the learning information of the generation AI based on the feedback. For example, the generation AI reflects the user's feedback in the next sound generation. The sleep state determination unit determines the user's sleep state based on biometric data acquired from the smartwatch. For example, it analyzes heart rate and body movement data to determine whether the user has entered deep sleep. The sleep onset sound playback unit plays sounds appropriate for falling asleep based on the sleep state and stops playing the sounds once the user reaches a deep sleep stage. For example, the system may play relaxing sounds until the user enters deep sleep, and automatically stop playing the sounds once the user has reached deep sleep. The wake-up sound playback unit plays sounds suitable for waking up based on the REM sleep state. For example, the system may play the wake-up sound in the REM sleep state closest to the wake-up time set by the user. In this way, the sleep tech system according to the embodiment can provide an inspiring sleep experience to people with sleep disorders by generating sounds tailored to the user's preferences and optimizing the sleep state.

[0057] The sound generation unit analyzes the user's past music playback history and streaming service data to generate music with higher accuracy. For example, the sound generation unit analyzes the user's past music playback history to understand trends in specific genres and artists. For example, the generation AI generates similar music based on the artists and genres the user has played most frequently in the past. The sound generation unit also uses streaming service data to extract characteristics of the user's favorite playlists and songs. For example, it analyzes tempo, key, frequency of instrument use, etc., and the generation AI generates music based on that. The sound generation unit also integrates the user's playback history and streaming service data to generate music with higher accuracy. For example, it analyzes the user's musical preferences during a specific time period and generates music that is optimal for that time period. This enables more accurate music generation by analyzing the user's past music playback history and streaming service data.

[0058] The sound generation unit can generate sounds optimal for the user's condition on that day based on the user's daily activity data. For example, the sound generation unit analyzes exercise data obtained from the user's smartwatch and generates sounds optimal for post-exercise recovery. For example, it generates music that promotes relaxation after exercise. The sound generation unit also analyzes the user's stress level and generates sounds effective for stress reduction. For example, it generates natural sounds that have a relaxing effect when stress is high. The sound generation unit also comprehensively analyzes the user's daily activity data and generates sounds optimal for the user's condition on that day. For example, it generates music with a high relaxing effect on days when fatigue is building up, and generates music that energizes the user on days when energy is high. This provides a more personalized music experience by generating sounds optimal for the user's condition on that day based on the user's daily activity data.

[0059] The sound generation unit can use the emotion estimation function to analyze the user's emotional state in real time and generate a sound that is optimal for that emotion. The sound generation unit, for example, analyzes the user's facial expressions and voice and estimates the emotional state in real time. For example, when the user is relaxed, music with a relaxing effect is generated. The sound generation unit also uses the emotion estimation function to generate music according to the user's emotional state. For example, when the user is feeling stressed, music with a stress-reducing effect is generated. The sound generation unit also monitors the user's emotional state in real time and generates a sound that is optimal for the emotion at that time. For example, when the user is tired, music with a relaxing effect is generated, and when the user is energetic, music that energizes is generated. In this way, by using the emotion estimation function to analyze the user's emotional state in real time and generating a sound that is optimal for that emotion, a more personalized music experience is provided.

[0060] The sound generation unit can analyze sound patterns preferred by the user and generate new sounds by combining different genres of music and environmental sounds. The sound generation unit, for example, analyzes the user's favorite music genres and environmental sounds and generates new sounds by combining them. For example, it generates a relaxing sound by combining classical music and natural sounds. The sound generation unit also combines music of different genres to provide a new musical experience. For example, it generates music that combines jazz and electronica. The sound generation unit also analyzes the user's favorite sound patterns and generates new sounds by combining different environmental sounds. For example, it generates a relaxing sound by combining the sound of waves and birds chirping. In this way, a more diverse musical experience is provided by analyzing the user's favorite sound patterns and generating new sounds by combining different genres of music and environmental sounds.

[0061] The sound generation unit can provide not only sound but also relaxing images based on the user's preferences. The sound generation unit provides relaxing images, for example, in sync with the user's favorite music or environmental sounds. For example, an image of the ocean can be played along with the sound of waves. The sound generation unit can also combine music with visual elements to provide a more relaxing experience. For example, an image of a forest can be played along with the sound of birds chirping. The sound generation unit can also provide content that combines sound and images based on the user's preferences. For example, an image of an art museum can be played along with classical music. In this way, a more relaxing experience can be provided by providing not only sound but also relaxing images based on the user's preferences.

[0062] The sound generation unit can use the emotion estimation function to monitor changes in a user's emotions while they are listening to the sound and switch sounds at optimal timing. For example, the sound generation unit monitors changes in a user's emotions in real time while they are listening to the sound and switches sounds according to their emotional state. For example, it plays a new relaxing sound when the relaxing effect starts to fade. The sound generation unit also uses the emotion estimation function to analyze changes in a user's emotions and switch sounds at optimal timing. For example, it plays a sound that has a stress-reducing effect when the user begins to feel stressed. The sound generation unit also monitors changes in a user's emotions and provides sounds that are optimal for the user's emotional state in real time. For example, it plays a relaxing sound when the user is tired, and an energizing sound when the user is energized. In this way, a more personalized music experience is provided by using the emotion estimation function to monitor changes in a user's emotions while they are listening to the sound and switching sounds at optimal timing.

[0063] The feedback collection unit can analyze user feedback and individually customize the generation algorithm. The feedback collection unit, for example, analyzes user feedback and individually customizes the sound generation algorithm. For example, it reflects the sound characteristics preferred by a particular user. The feedback collection unit also adjusts the generation algorithm based on the feedback data to generate a sound that is more suited to the user's preferences. For example, it learns the characteristics of sounds that the user finds relaxing. The feedback collection unit also analyzes user feedback and generates individually customized sounds. For example, it reflects the user's preferred tempo and frequency of instrument use. In this way, by analyzing user feedback and individually customizing the sound generation algorithm, a more personalized musical experience is provided.

[0064] The feedback collection unit can analyze the content of the feedback using natural language processing technology and extract specific areas for improvement. The feedback collection unit, for example, analyzes user feedback using natural language processing technology and extracts specific areas for improvement. For example, it analyzes feedback such as "The sound is a little loud" and adjusts the volume. The feedback collection unit also analyzes the content of the feedback and identifies areas for improvement in the generation algorithm. For example, based on feedback such as "I want a more relaxing sound," it generates a sound with a high relaxing effect. The feedback collection unit also uses natural language processing technology to analyze the content of the feedback and extracts specific areas for improvement. For example, based on feedback such as "This sound was relaxing," it generates a similar sound. In this way, by analyzing the content of the feedback using natural language processing technology and extracting specific areas for improvement, more effective music generation is possible.

[0065] The feedback collection unit can use the emotion estimation function to analyze the user's emotion at the time of feedback and make improvements based on the emotion. The feedback collection unit, for example, analyzes the user's emotion at the time of feedback and makes improvements based on the emotion. For example, a similar sound is generated based on feedback with a strong positive emotion. The feedback collection unit also uses the emotion estimation function to analyze the user's emotion at the time of feedback and improves the generation algorithm. For example, the sound characteristics are adjusted based on feedback with a strong negative emotion. The feedback collection unit also analyzes the user's emotion and makes improvements based on the emotion. For example, in order to generate a sound with a high relaxing effect, feedback with a strong positive emotion is reflected. In this way, more effective music generation is possible by using the emotion estimation function to analyze the user's emotion at the time of feedback and making improvements based on the emotion.

[0066] The feedback collection unit can share the feedback with other users and collect community-based improvement suggestions. The feedback collection unit, for example, shares the user's feedback with other users and collects community-based improvement suggestions. For example, the feedback collection unit publishes the feedback and collects opinions from other users. The feedback collection unit also collects community-based feedback and uses it to improve the generation algorithm. For example, based on feedback from multiple users, it identifies common areas for improvement. The feedback collection unit also builds a system for sharing feedback and collecting community-based improvement suggestions. For example, it holds discussions based on the feedback and extracts areas for improvement. In this way, by sharing the feedback with other users and collecting community-based improvement suggestions, more effective music generation is possible.

[0067] The feedback collection unit can improve not only sound generation but also the UI / UX of the app based on the feedback. The feedback collection unit improves not only sound generation but also the UI / UX of the app based on user feedback, for example. For example, the UI is improved based on feedback such as "The operation is difficult to understand." The feedback collection unit also analyzes the feedback and identifies areas for improvement in the UI / UX of the app. For example, the UX is improved based on feedback such as "I wish it were easier to operate." The feedback collection unit also makes comprehensive improvements to sound generation and the UI / UX of the app based on the feedback. For example, the UI is improved based on feedback such as "It's become easier to select sounds." In this way, more effective music generation is possible by improving not only sound generation but also the UI / UX of the app based on feedback.

[0068] The feedback collection unit can use the emotion estimation function to analyze the emotional tone of the feedback and reinforce positive feedback. For example, the feedback collection unit analyzes the emotional tone of the feedback and reinforces positive feedback. For example, feedback with a strong positive emotion is preferentially reflected. The feedback collection unit also uses the emotion estimation function to analyze the emotional tone of the feedback and improve the generation algorithm. For example, a similar sound is generated based on positive feedback. The feedback collection unit also analyzes the emotional tone of the feedback and builds a system that reinforces positive feedback. For example, feedback with a strong positive emotion is preferentially reflected. In this way, more effective music generation is possible by using the emotion estimation function to analyze the emotional tone of the feedback and reinforce positive feedback.

[0069] The sleep state determination unit can automatically adjust the user's sleep environment based on data acquired from the smartwatch. The sleep state determination unit automatically adjusts the user's sleep environment based on, for example, data acquired from the smartwatch. For example, it maintains the temperature and humidity at optimal levels. The sleep state determination unit also analyzes the data from the smartwatch and builds a system that automatically adjusts the user's sleep environment. For example, it adjusts the amount of light to provide a comfortable sleep environment. The sleep state determination unit also automatically adjusts the sleep environment based on the user's sleep data. For example, it controls an air conditioner or humidifier to maintain the temperature and humidity at optimal levels. In this way, the user's sleep environment is automatically adjusted based on the data acquired from the smartwatch, thereby providing a more comfortable sleep environment.

[0070] The sleep state determination unit can analyze smartwatch data and track the user's sleep patterns over the long term. The sleep state determination unit, for example, analyzes data obtained from the smartwatch and tracks the user's sleep patterns over the long term. For example, it records the daily sleep time and the percentage of deep sleep. The sleep state determination unit also analyzes the user's sleep patterns based on the long-term sleep data and identifies areas for improvement. For example, it provides advice for improving sleep quality. The sleep state determination unit also builds a system that tracks smartwatch data over the long term and analyzes the user's sleep patterns. For example, it evaluates sleep quality and suggests areas for improvement. In this way, analyzing smartwatch data and tracking the user's sleep patterns over the long term enables more effective sleep improvement.

[0071] The sleep state determination unit can use the emotion estimation function to analyze the emotional state of the user while sleeping and adjust the sound based on the emotion. The sleep state determination unit, for example, uses the emotion estimation function to analyze the emotional state of the user while sleeping and adjust the sound based on the emotion. For example, it plays sounds that have a high relaxing effect. The sleep state determination unit also builds a system that analyzes the emotional state of the user while sleeping in real time and adjusts the sound based on the emotion. For example, it plays sounds that have a stress-reducing effect. The sleep state determination unit also uses the emotion estimation function to analyze the emotional state of the user while sleeping and provides optimal sounds. For example, it plays sounds that have a high relaxing effect to improve the quality of sleep. As a result, by using the emotion estimation function to analyze the emotional state of the user while sleeping and adjusting the sound based on the emotion, more effective sleep improvement is possible.

[0072] The sleep state determination unit can work in conjunction with the smartwatch to analyze the user's exercise data and generate sounds that are optimal for post-exercise recovery. The sleep state determination unit, for example, analyzes exercise data acquired from the smartwatch and generates sounds that are optimal for post-exercise recovery. For example, it plays music that has a relaxing effect. The sleep state determination unit also builds a system that generates sounds that are optimal for post-exercise recovery based on the user's exercise data. For example, it provides music that is suitable for stretching and relaxation. The sleep state determination unit also analyzes the exercise data from the smartwatch and generates sounds that are optimal for post-exercise recovery. For example, it provides recovery sounds that correspond to the heart rate and exercise intensity. This allows for more effective recovery by analyzing the user's exercise data in conjunction with the smartwatch and generating sounds that are optimal for post-exercise recovery.

[0073] The sleep state determination unit can comprehensively evaluate the user's health state based on the data from the smartwatch and provide health advice. The sleep state determination unit, for example, comprehensively evaluates the user's health state based on data acquired from the smartwatch and provides health advice. For example, it evaluates the quality of sleep and the amount of exercise and suggests areas for improvement. The sleep state determination unit also analyzes the user's health data and builds a system that performs a comprehensive health evaluation. For example, it evaluates the health state based on heart rate, body temperature, and amount of exercise. The sleep state determination unit also comprehensively evaluates the user's health state based on the data from the smartwatch and provides individual health advice. For example, it provides advice on diet and exercise. This enables more effective health management by comprehensively evaluating the user's health state based on the data from the smartwatch and providing health advice.

[0074] The sleep state determination unit can use the emotion estimation function to track the user's emotional changes throughout the day and generate sounds based on the emotions. The sleep state determination unit, for example, uses the emotion estimation function to track the user's emotional changes throughout the day and generate sounds based on the emotions. For example, when stress is high, a sound with a relaxing effect is generated. The sleep state determination unit also builds a system that tracks the user's emotional changes in real time and generates sounds based on the emotions. For example, when positive emotions are strong, a sound that energizes the user is generated. The sleep state determination unit also uses the emotion estimation function to track the user's emotional changes throughout the day and generate sounds that are optimal for the user's emotions at that time. For example, when the user is tired, a sound with a relaxing effect is generated, and when the user is energized, a sound that energizes the user is generated. In this way, by using the emotion estimation function to track the user's emotional changes throughout the day and generate sounds based on the emotions, a more effective music experience is provided.

[0075] The wake-up sound playback unit can be added with a function to analyze brainwave data to more accurately detect REM sleep states. The wake-up sound playback unit, for example, adds a function to analyze brainwave data acquired from a smartwatch to more accurately detect REM sleep states. For example, it detects specific brainwave patterns and determines REM sleep states. The wake-up sound playback unit also analyzes brainwave data to build a system that accurately detects REM sleep states. For example, it monitors brainwave fluctuations in real time and identifies REM sleep states. The wake-up sound playback unit also accurately detects REM sleep states based on the brainwave data acquired from the smartwatch. For example, it analyzes specific frequency bands of brainwaves and determines REM sleep states. By adding a function to analyze brainwave data to more accurately detect REM sleep states, more effective wake-ups are possible.

[0076] The wake-up sound playback unit can provide a multimodal wake-up method that combines light and vibration in addition to wake-up sound. The wake-up sound playback unit provides a multimodal wake-up method that combines light and vibration in addition to wake-up sound, for example. For example, it uses light that gradually brightens along with music. The wake-up sound playback unit also builds a system that provides a multimodal wake-up method. For example, it wakes up the user using vibration from a smart watch along with music. The wake-up sound playback unit also provides a multimodal wake-up method that combines sound, light, and vibration in addition to wake-up sound. For example, it uses a smart light that gradually brightens along with music. This provides a multimodal wake-up method that combines light and vibration in addition to wake-up sound, enabling a more effective wake-up.

[0077] The wake-up sound playback unit can use the emotion estimation function to analyze the emotional state of the user when waking up and generate a sound that elicits positive emotions. The wake-up sound playback unit, for example, uses the emotion estimation function to analyze the emotional state of the user when waking up and generate a sound that elicits positive emotions. For example, music with a relaxing effect is played. The wake-up sound playback unit also builds a system that analyzes the emotional state of the user when waking up in real time and generates a sound that elicits positive emotions. For example, music that energizes the user is played. The wake-up sound playback unit also uses the emotion estimation function to analyze the emotional state of the user when waking up and provides an optimal sound. For example, music that elicits positive emotions is played to support a comfortable wake-up. In this way, a more comfortable wake-up is possible by using the emotion estimation function to analyze the emotional state of the user when waking up and generating a sound that elicits positive emotions.

[0078] The wake-up sound reproduction unit can suggest an optimal sleep time based on the user's daily schedule in addition to waking up in REM sleep. The wake-up sound reproduction unit, for example, analyzes the user's daily schedule and suggests an optimal sleep time. For example, it suggests an optimal bedtime based on an important appointment for the next day. The wake-up sound reproduction unit also builds a system that suggests an optimal sleep time for the user based on schedule data. For example, it calculates an optimal sleep time based on the appointment for the next day. The wake-up sound reproduction unit also takes the user's daily schedule into consideration and suggests an optimal sleep time. For example, it suggests going to bed early in preparation for an important meeting the next day. This enables more effective sleep management by suggesting an optimal sleep time based on the user's daily schedule in addition to waking up in REM sleep.

[0079] The wake-up sound playback unit generates sounds and music that are optimal for the refreshing time after waking up, thereby supporting the user in starting their day. The wake-up sound playback unit generates sounds and music that are optimal for the refreshing time after waking up, for example, to support the user in starting their day. For example, it plays invigorating music. The wake-up sound playback unit also builds a system that generates sounds that are optimal for the refreshing time after waking up. For example, it plays music that has a relaxing effect to support a comfortable start to the day. The wake-up sound playback unit also generates sounds that are optimal for the refreshing time after waking up to support the user in starting their day. For example, it plays invigorating music to start the day in a positive mood. In this way, it generates sounds and music that are optimal for the refreshing time after waking up, supporting the user in starting their day, thereby providing a more comfortable start to the day.

[0080] The wake-up sound playback unit can use the emotion estimation function to monitor the user's emotional state after waking up and suggest daily activities based on the emotion. The wake-up sound playback unit, for example, uses the emotion estimation function to monitor the user's emotional state after waking up and suggest daily activities based on the emotion. For example, it suggests activities that have a relaxing effect. The wake-up sound playback unit also builds a system that monitors the user's emotional state after waking up in real time and suggests activities based on the emotion. For example, it suggests activities that elicit positive emotions. The wake-up sound playback unit also uses the emotion estimation function to monitor the user's emotional state after waking up and suggest optimal activities. For example, it suggests activities that have a relaxing effect, supporting a comfortable start to the day. In this way, by using the emotion estimation function to monitor the user's emotional state after waking up and suggesting daily activities based on the emotion, a more effective start to the day is supported.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The Sleep Tech System can further include an environmental adjustment unit that automatically adjusts the user's sleep environment. The environmental adjustment unit automatically adjusts the user's sleep environment based on data acquired from, for example, a smartwatch. For example, it controls an air conditioner or humidifier to maintain optimal temperature and humidity. The environmental adjustment unit can also adjust the amount of light to provide a comfortable sleep environment. For example, it can gradually dim the light until the user enters deep sleep and gradually brighten it when the user wakes up. This automatically adjusts the user's sleep environment to provide a more comfortable sleep experience.

[0083] The sleep tech system can further include a health evaluation unit that comprehensively evaluates the user's health condition. The health evaluation unit, for example, evaluates the user's health condition based on data obtained from a smartwatch and provides health advice. For example, it evaluates the quality of sleep and the amount of exercise and suggests areas for improvement. The health evaluation unit also analyzes the user's health data and builds a system that performs a comprehensive health evaluation. For example, it evaluates the health condition based on heart rate, body temperature, and amount of exercise. This allows for a comprehensive evaluation of the user's health condition and provides health advice, enabling more effective health management.

[0084] The sleep tech system can further include a schedule suggestion unit that suggests an optimal sleep time based on the user's daily schedule. The schedule suggestion unit, for example, analyzes the user's daily schedule and suggests an optimal sleep time. For example, it suggests an optimal bedtime based on important plans for the next day. The schedule suggestion unit also builds a system that suggests the user's optimal sleep time based on schedule data. For example, it calculates the optimal sleep time based on the next day's plans. This allows for more effective sleep management by suggesting an optimal sleep time based on the user's daily schedule.

[0085] The sleep tech system may further include an emotion analysis unit that analyzes the user's emotional state in real time and generates sound that is optimal for that emotion. The emotion analysis unit, for example, analyzes the user's facial expressions and voice to estimate the user's emotional state in real time. For example, when the user is relaxed, music that has a relaxing effect is generated. The emotion analysis unit also uses an emotion estimation function to generate music that corresponds to the user's emotional state. For example, when the user is feeling stressed, music that has a stress-reducing effect is generated. In this way, by using the emotion estimation function to analyze the user's emotional state in real time and generating sound that is optimal for that emotion, a more personalized music experience can be provided.

[0086] The sleep tech system may further include an emotion monitoring unit that monitors the user's emotional changes and switches sounds at optimal timing. The emotion monitoring unit, for example, monitors the user's emotional changes in real time while listening to sounds and switches sounds according to the user's emotional state. For example, it plays a new relaxing sound when the relaxing effect begins to fade. The emotion monitoring unit also uses an emotion estimation function to analyze the user's emotional changes and switches sounds at optimal timing. For example, it plays a sound that has a stress-reducing effect when the user begins to feel stressed. This allows the emotion estimation function to monitor the user's emotional changes and switch sounds at optimal timing, providing a more personalized music experience.

[0087] The Sleep Tech System may further include a feedback sharing unit that shares user feedback with other users and collects community-based improvement suggestions. The feedback sharing unit, for example, shares user feedback with other users and collects community-based improvement suggestions. For example, the feedback sharing unit publishes the feedback and collects opinions from other users. The feedback sharing unit also collects community-based feedback and uses it to improve the generation algorithm. For example, the feedback sharing unit identifies common areas for improvement based on feedback from multiple users. This allows for more effective music generation by sharing feedback with other users and collecting community-based improvement suggestions.

[0088] The Sleep Tech System can further include a UI / UX improvement unit that improves not only sound generation but also the UI / UX of the app based on feedback. The UI / UX improvement unit improves not only sound generation but also the UI / UX of the app based on user feedback. For example, the UI is improved based on feedback such as "The operation is difficult to understand." The UI / UX improvement unit also analyzes the feedback and identifies areas for improvement in the app's UI / UX. For example, the UX is improved based on feedback such as "I wish it were easier to operate." In this way, more effective music generation becomes possible by improving not only sound generation but also the app's UI / UX based on feedback.

[0089] The sleep tech system may further include a feedback analysis unit that analyzes the emotional tone of the feedback and reinforces positive feedback. The feedback analysis unit, for example, analyzes the emotional tone of the feedback and reinforces positive feedback. For example, feedback with a strong positive emotion is preferentially reflected. The feedback analysis unit also uses an emotion estimation function to analyze the emotional tone of the feedback and improve the generation algorithm. For example, based on positive feedback, a similar sound is generated. This enables more effective music generation by analyzing the emotional tone of the feedback using the emotion estimation function and reinforcing positive feedback.

[0090] The sleep tech system may further include an emotion tracking unit that tracks the user's emotional changes throughout the day and generates sounds based on the emotions. The emotion tracking unit, for example, uses an emotion estimation function to track the user's emotional changes throughout the day and generates sounds based on the emotions. For example, when stress is high, a sound with a relaxing effect is generated. The emotion tracking unit also builds a system that tracks the user's emotional changes in real time and generates sounds based on the emotions. For example, when positive emotions are strong, an energizing sound is generated. In this way, by using the emotion estimation function to track the user's emotional changes throughout the day and generating sounds based on the emotions, a more effective music experience can be provided.

[0091] The Sleep Tech System may further include a wake-up support unit that provides a multimodal wake-up method that combines light and vibration in addition to wake-up sound. The wake-up support unit provides a multimodal wake-up method that combines light and vibration in addition to wake-up sound, for example, by using gradually brightening light along with music. The wake-up support unit also constructs a system that provides a multimodal wake-up method. For example, it wakes up the user using music along with vibrations from a smartwatch. This provides a multimodal wake-up method that combines light and vibration in addition to wake-up sound, enabling a more effective wake-up.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The sound generation unit uses AI to generate sounds tailored to the user's preferences. For example, AI can generate optimal sleep sounds by combining natural sounds and synthetic sounds that the user likes. AI can also analyze the user's music playback history to generate more accurate music. Step 2: The feedback collection unit collects feedback from the user about the generated sound. For example, the user provides feedback such as "This sound is relaxing." Step 3: The learning information update unit updates the learning information of the generation AI based on the feedback. For example, the generation AI reflects the user's feedback in the next sound generation. Step 4: The sleep state determination unit determines the user's sleep state based on the biological data acquired from the smartwatch. For example, it analyzes heart rate and body movement data to determine whether the user is in deep sleep. Step 5: The sleep sound player plays sounds suitable for falling asleep based on the sleep state, and stops playing the sounds once the deep sleep stage is reached. For example, it plays relaxing sounds until the user enters deep sleep, and automatically stops playing the sounds once deep sleep is reached. Step 6: The wake-up sound playback unit plays a sound suitable for waking up based on the REM sleep state, for example, the wake-up sound is played in the REM sleep state closest to the wake-up time set by the user.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 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 sound generation unit that uses generation AI to generate sounds tailored to the user's tastes and preferences; a feedback collection unit that collects feedback from users regarding the generated sound; a learning information update unit that updates the learning information of the generation AI based on the feedback; a sleep state determination unit that determines the sleep state of the user based on biological data acquired from the smart watch; a sleep sound playback unit that plays a sound suitable for falling asleep based on the sleep state and stops playing the sound when the deep sleep stage is reached; and a wake-up sound playback unit that plays a sound suitable for waking up based on the REM sleep state. A system characterized by:

2. The sound generation unit Analyze the user's past music playback history and streaming service data to generate more accurate music.

2. The system of claim 1.

3. The sound generation unit Generates a sound that is optimal for the user's condition on that day based on the user's daily activity data.

2. The system of claim 1.

4. The sound generation unit Analyzing the emotional state of the user in real time and generating the sound that best suits that emotion 2. The system of claim 1.

5. The sound generation unit Analyze the sound patterns preferred by the user and generate new sounds by combining different genres of music and environmental sounds.

2. The system of claim 1.

Images