System

Abstract

To provide a system that reproduces appropriate music according to a user's sleeping state.SOLUTION: A system according to the embodiment comprises a reception unit, a playback unit, a monitoring unit, and an awakening unit. The reception unit allows a user to select music. The playback unit plays the music selected by the reception unit. The monitoring unit monitors the user's sleep state. The awakening unit plays music suitable for waking up when in a specific 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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques do not adequately reproduce music according to the user's sleep state, and there is room for improvement.

[0005] The system according to the embodiment aims to play appropriate music according to the user's sleep state. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a playback unit, a monitoring unit, and a wake-up unit. The reception unit allows a user to select music. The playback unit plays the music selected by the reception unit. The monitoring unit monitors the user's sleep state. The wake-up unit plays music suitable for waking up when the user is in a specific sleep state. Effect of the Invention

[0007] The system according to the embodiment can play appropriate music depending on the user's sleep state. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The system according to the embodiment of the present invention is a system in which AI controls an invitation to a peaceful sleep through meditation or music played by a fabric speaker. In this system, the user selects meditation or relaxing music before going to bed. Next, the AI ​​plays the optimal music based on the user's selection to relax the user. Furthermore, the AI ​​monitors the user's sleep state and plays music that is good for waking up when the user is in a light sleep. This mechanism allows the user to have a peaceful sleep and wake up smoothly. For example, the user selects meditation or relaxing music before going to bed. In this case, the user selects music through an application using a device such as a smartphone or tablet. For example, when the user selects "relaxing music", the information is sent to the AI. Next, the AI ​​plays the optimal music based on the user's selection. The AI ​​analyzes the characteristics of the music selected by the user and selects music with a high relaxing effect. For example, the AI ​​analyzes the tempo, volume, frequency, etc. of the music and plays the optimal relaxing music for the user. Furthermore, the AI ​​monitors the user's sleep state. The AI ​​collects data such as the user's heart rate, breathing rate, and movement to determine the depth of sleep. For example, when a user enters light sleep, the AI ​​analyzes the data and plays music that is good for waking up. This mechanism allows the user to get a peaceful sleep. By having the AI ​​play relaxing music, the user can relax and fall asleep more easily. In addition, by having the AI ​​play music that is good for waking up when the user is in light sleep, the user can wake up naturally. For example, the AI ​​can play birds chirping in the morning or calm piano music, allowing the user to wake up feeling refreshed. In this way, the system can effectively support the user's sleep and provide high-quality sleep and a smooth awakening.

[0029] The sleep support system according to the embodiment includes a reception unit, a playback unit, a monitoring unit, and a wake-up unit. The reception unit allows a user to select music. When selecting music, the user selects music through an application using a device such as a smartphone or tablet. For example, when the user selects "relaxing music," the information is transmitted to the AI. The playback unit plays the music selected by the reception unit. The playback unit uses AI to analyze the characteristics of the music selected by the user and selects music with a high relaxing effect. For example, AI analyzes the tempo, volume, frequency, etc. of the music and plays the most suitable relaxing music for the user. The monitoring unit monitors the user's sleep state. The monitoring unit uses AI to collect data such as the user's heart rate, breathing rate, and movement, and determines the depth of sleep. For example, when the user enters a light sleep, the monitoring unit analyzes the data and plays music that is good for waking up. The wake-up unit plays music suitable for waking up when in a specific sleep state. The wake-up unit uses AI to analyze the user's sleep state and selects music suitable for waking up. For example, the AI ​​can play morning birdsong or calm piano music to wake the user up in a good mood. This allows the sleep support system according to the embodiment to consistently select music for the user, play music, monitor sleep, and play music to wake the user up.

[0030] The reception unit allows the user to select music. When selecting music, the user selects music through an application using a device such as a smartphone or tablet. Specifically, the user can launch the application and select their favorite music from categories such as relaxing music, natural sounds, and classical music. The application also has a function to recommend the most suitable music taking into consideration the user's past selection history, current mood, time of day, etc. For example, it recommends music with a high relaxing effect at night and music suitable for waking up in the morning. When the user selects music, the information is sent to the AI. The AI ​​analyzes the user's selection history and current state and collects data to select the most suitable music. This allows the reception unit to provide music according to the user's preferences and state. In addition, the reception unit can collect user feedback and continuously improve the music selection algorithm. For example, by evaluating the music selected by the user, such as "it was relaxing" or "it woke me up well," the AI ​​learns the evaluation and reflects it in the next and subsequent music selections. This allows the reception unit to provide a personalized music experience according to the user's needs.

[0031] The playback unit plays the music selected by the reception unit. The playback unit uses AI to analyze the characteristics of the music selected by the user and selects music that has a high relaxing effect. Specifically, the AI ​​analyzes the tempo, volume, frequency, melody pattern, etc. of the music and selects music that maximizes the user's relaxation. For example, music with a slow tempo, constant volume, and many low-frequency sounds is said to have a high relaxing effect. The AI ​​takes these characteristics into account and plays the most suitable relaxing music for the user. The playback unit can also monitor the user's real-time reaction and adjust the music as necessary. For example, if the user's heart rate or breathing rate changes suddenly, the playback unit adjusts the tempo and volume of the music to maintain the relaxing effect. Furthermore, the playback unit can also capture the user's environmental sounds and play music corresponding to them. For example, if the external noise is loud, the playback unit selects music that cancels it out and provides an environment in which the user can relax. As a result, the playback unit can provide the most suitable music according to the user's condition and environment, maximizing the relaxing effect.

[0032] The monitoring unit monitors the user's sleep state. The monitoring unit uses AI to collect data such as the user's heart rate, breathing rate, and movement, and determines the depth of sleep. Specifically, biometric data such as heart rate, breathing rate, body temperature, and movement are collected in real time through a wearable device worn by the user and sensors installed in the bed. These data are analyzed by AI to determine the user's sleep stage (light sleep, deep sleep, REM sleep, etc.). For example, a low heart rate and little movement are determined to be deep sleep. The monitoring unit evaluates the user's sleep quality based on these data and adjusts the music playback as necessary. For example, when the user enters light sleep, the monitoring unit analyzes the data and plays music that is good for waking up. The monitoring unit can also record the user's sleep patterns over the long term and provide advice to improve sleep quality. For example, it analyzes the user's sleep data and suggests optimal bedtimes, wake-up times, and relaxation methods. This allows the monitoring unit to grasp the user's sleep state in detail and provide support to improve sleep quality.

[0033] The awakening unit plays music suitable for waking up in a specific sleep state. The awakening unit uses AI to analyze the user's sleep state and select music suitable for waking up. Specifically, based on the sleep data provided by the monitoring unit, it plays music for waking up when the user is in a light sleep state. For example, the AI ​​can play morning bird chirps or gentle piano music to wake up the user comfortably. The awakening unit gradually increases the tempo and volume of the music to allow the user to wake up naturally. For example, by playing gentle music at a low volume at first and gradually increasing the volume, the user can wake up without difficulty. In addition, the awakening unit selects the optimal awakening music taking into account the user's preferences and past awakening data. For example, by playing music that the user used to wake up comfortably in the past, it provides a more comfortable awakening. In addition, the awakening unit can collect user feedback and continuously improve the selection algorithm for awakening music. As a result, the awakening unit can provide the optimal awakening music according to the user's sleep state and preferences, and support a comfortable awakening.

[0034] The sleep support system includes an option expansion unit that increases the types of music that the user can select. The option expansion unit increases the types of music that the user can select. The types of music include, but are not limited to, genres, artists, and albums. The option expansion unit can use AI to analyze the user's preferences and past selection history and suggest optimal music options. For example, based on relaxing music selected by the user in the past, similar music is suggested. The option expansion unit can also analyze the genre of music that the user liked to listen to in the past and suggest related music. Furthermore, the option expansion unit can also suggest optimal music based on the tempo and volume of music selected by the user in the past. This increases the types of music that the user can select, enabling a more diverse music experience.

[0035] The sleep support system includes a data collection unit that indicates a specific data collection method. The data collection unit indicates a specific data collection method. The data collection method includes, for example, a type of sensor, a frequency of data collection, a type of data to be collected, and the like, but is not limited to such examples. The data collection unit can use AI to collect data such as the user's heart rate, breathing rate, and movement, and determine the depth of sleep. For example, the data collection unit uses a heart rate sensor to monitor the user's heart rate in real time. The data collection unit can also use a breathing sensor to measure the user's breathing rate. Furthermore, the data collection unit can use a motion sensor to detect the user's movement and determine the depth of sleep. As a result, more accurate data collection is possible by specifically indicating the data collection method.

[0036] The sleep support system includes a data analysis unit that indicates the type of data and the analysis method that the AI ​​analyzes. The data analysis unit indicates the type of data and the analysis method that the AI ​​analyzes. The type of data to be analyzed includes, but is not limited to, heart rate, breathing rate, movement, and music characteristics, for example. The data analysis unit can use the AI ​​to analyze collected data and determine the user's sleep state. For example, the data analysis unit can use a machine learning algorithm to analyze heart rate and breathing rate patterns and determine the depth of sleep. The data analysis unit can also use a statistical analysis method to analyze movement data and evaluate the quality of sleep. Furthermore, the data analysis unit can analyze music characteristics and select music with a high relaxing effect. This clarifies the type and method of data analysis, thereby improving the analysis accuracy.

[0037] The playback unit can analyze the tempo, volume, frequency, etc. of the music and select music that is expected to have a relaxing effect. The playback unit uses AI to analyze the tempo, volume, frequency, etc. of the music and selects music that is expected to have a relaxing effect. Music that is expected to have a relaxing effect includes, for example, music with a slow tempo, low volume, and gentle frequency, but is not limited to such examples. For example, the playback unit uses AI to analyze the tempo of the music and selects music with a tempo that has a high relaxing effect. The playback unit can also use AI to analyze the volume of the music and select music with a volume that has a high relaxing effect. Furthermore, the playback unit can also use AI to analyze the frequency of the music and select music with a frequency that has a high relaxing effect. In this way, music with a high relaxing effect can be selected by analyzing the characteristics of the music.

[0038] The monitoring unit can collect data such as the user's heart rate, breathing rate, and movement, and determine the state of sleep. The monitoring unit uses AI to collect data such as the user's heart rate, breathing rate, and movement, and determine the state of sleep. The state of sleep includes, for example, REM sleep, non-REM sleep, light sleep, deep sleep, and the like, but is not limited to such examples. The monitoring unit monitors the user's heart rate in real time, for example, using a heart rate sensor. The monitoring unit can also measure the user's breathing rate using a breathing sensor. Furthermore, the monitoring unit can detect the user's movement using a motion sensor and determine the depth of sleep. This allows the user's biological data to be collected and the depth of sleep to be accurately determined.

[0039] The reception unit can analyze the user's past music selection history and automatically suggest the optimal music. The reception unit uses AI to analyze the user's past music selection history and automatically suggest the optimal music. The optimal music includes, for example, relaxing music selected in the past, the genre of music that the user liked to listen to, the tempo and volume, etc., but is not limited to such examples. For example, the reception unit suggests similar music based on relaxing music selected by the user in the past. The reception unit can also analyze the genre of music that the user liked to listen to in the past and suggest related music. Furthermore, the reception unit can also suggest the optimal music based on the tempo and volume of music selected by the user in the past. This makes it possible to suggest the optimal music based on the past selection history.

[0040] The reception unit can perform filtering based on the user's current mood and activity status when selecting music. The reception unit uses AI to perform filtering based on the user's current mood and activity status when selecting music. The current mood and activity status include, but are not limited to, for example, wanting to relax, wanting to concentrate, exercising, etc. For example, when the user feels like relaxing, the reception unit can preferentially display music that has a high relaxing effect. In addition, when the user feels like concentrating, the reception unit can also preferentially display music that enhances concentration. Furthermore, when the user is exercising, the reception unit can also preferentially display energetic music. This allows the optimal music to be selected based on the current mood and activity status.

[0041] The reception unit can suggest highly relevant music in consideration of the geographical location information of the user when selecting music. The reception unit uses AI to suggest highly relevant music in consideration of the geographical location information of the user when selecting music. The geographical location information includes, but is not limited to, GPS data, location information services, and the like. For example, when the user is in nature, the reception unit can suggest relaxing music including natural sounds. In addition, when the user is in an urban area, the reception unit can also suggest music that eases the hustle and bustle of the city. Furthermore, when the user is traveling, the reception unit can also suggest music related to the culture of the travel destination. This makes it possible to suggest optimal music based on the geographical location information.

[0042] The reception unit can analyze the user's social media activity at the time of music selection and suggest related music. The reception unit uses AI to analyze the user's social media activity at the time of music selection and suggest related music. Social media activity includes, for example, the content of posts, the number of likes, and reactions of followers, but is not limited to such examples. The reception unit can suggest related music, for example, based on music shared by the user on social media. The reception unit can also suggest music of artists the user follows on social media. Furthermore, the reception unit can also suggest related music based on music that the user has "liked" on social media. This makes it possible to suggest related music based on social media activity.

[0043] The playback unit can select optimal music when playing music by analyzing the user's past relaxing effect. The playback unit uses AI to select optimal music when playing music. Optimal music includes, for example, music that has helped the user relax in the past, and the tempo, volume, and frequency of music that the user likes to listen to, but is not limited to such examples. The playback unit plays similar music based on, for example, music that has helped the user relax in the past. The playback unit can also analyze the tempo and volume of music that the user likes to listen to in the past and play optimal music. Furthermore, the playback unit can play optimal music based on the frequency of music that has helped the user relax in the past. This makes it possible to select optimal music based on the past relaxing effect.

[0044] The playback unit can adjust the tempo and volume of music based on the user's current activity status when playing music. The playback unit uses AI to adjust the tempo and volume of music based on the user's current activity status when playing music. The current activity status includes, but is not limited to, relaxing, concentrating, exercising, and the like. For example, when the user is relaxing, the playback unit adjusts the tempo of the music to be slow. In addition, when the user is concentrating, the playback unit can also adjust the volume of the music to be low. Furthermore, when the user is exercising, the playback unit can also adjust the tempo of the music to be fast. This makes it possible to adjust the music playback method based on the current activity status.

[0045] The playback unit can select optimal music in consideration of the geographical location information of the user when playing music. The playback unit uses AI to select optimal music in consideration of the geographical location information of the user when playing music. The geographical location information includes, but is not limited to, GPS data, location information services, and the like. For example, when the user is in nature, the playback unit plays relaxing music including natural sounds. In addition, when the user is in an urban area, the playback unit can also play music that alleviates the hustle and bustle of the city. Furthermore, when the user is traveling, the playback unit can also play music related to the culture of the travel destination. This allows optimal music to be selected based on the geographical location information.

[0046] The playback unit can analyze the user's social media activity and play related music when playing music. The playback unit uses AI to analyze the user's social media activity and play related music when playing music. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and reactions of followers. The playback unit plays related music, for example, based on music shared by the user on social media. The playback unit can also play music of artists the user follows on social media. Furthermore, the playback unit can also play related music based on music that the user has "liked" on social media. This makes it possible to play related music based on social media activity.

[0047] The monitoring unit can select the optimal monitoring method by referring to the user's past sleep data during monitoring. The monitoring unit uses AI to select the optimal monitoring method by referring to the user's past sleep data during monitoring. The optimal monitoring method includes, for example, data on the user having had a good sleep in the past, data on the user having experienced a sleep disorder, data on the user being able to relax, and the like, but is not limited to such examples. For example, the monitoring unit selects a similar monitoring method based on data on the user having had a good sleep in the past. The monitoring unit can also select a monitoring method including an improvement measure based on data on the user having experienced a sleep disorder in the past. Furthermore, the monitoring unit can also select the optimal monitoring method based on data on the user having been able to relax in the past. This makes it possible to select the optimal monitoring method based on the past sleep data.

[0048] The monitoring unit can adjust the monitoring parameters based on the user's current health condition during monitoring. The monitoring unit adjusts the monitoring parameters based on the user's current health condition during monitoring using AI. The current health condition includes, for example, heart rate, blood pressure, activity level, etc., but is not limited to such examples. For example, when the user is healthy, the monitoring unit sets the monitoring parameters to standard. In addition, when the user is tired, the monitoring unit can also set the monitoring parameters to be gentle. Furthermore, when the user is in poor health, the monitoring unit can also set the monitoring parameters to be strict. This makes it possible to adjust the monitoring parameters based on the current health condition.

[0049] The monitoring unit can select the optimal monitoring method in consideration of the geographical location information of the user during monitoring. The monitoring unit uses AI to select the optimal monitoring method in consideration of the geographical location information of the user during monitoring. The geographical location information includes, for example, GPS data, location information services, etc., but is not limited to such examples. For example, when the user is in nature, the monitoring unit selects a monitoring method including natural sounds. In addition, when the user is in an urban area, the monitoring unit can also select a monitoring method that reduces the hustle and bustle of the city. Furthermore, when the user is traveling, the monitoring unit can also select a monitoring method related to the culture of the travel destination. This allows the optimal monitoring method to be selected based on the geographical location information.

[0050] The monitoring unit can analyze the user's social media activity during monitoring to collect related data. The monitoring unit uses AI to analyze the user's social media activity during monitoring to collect related data. Social media activity includes, for example, the content of posts, the number of likes, and reactions of followers, but is not limited to such examples. The monitoring unit collects related data, for example, based on health information shared by the user on social media. The monitoring unit can also collect related data based on information on health accounts that the user follows on social media. Furthermore, the monitoring unit can also collect related data based on health information that the user "liked" on social media. This makes it possible to collect related data based on social media activity.

[0051] The awakening unit can select optimal music by referring to the user's past awakening data when waking up. The awakening unit uses AI to select optimal music by referring to the user's past awakening data when waking up. Optimal music includes, for example, music that woke up the user well in the past, music that woke up poorly, and music that refreshed the user, but is not limited to such examples. For example, the awakening unit plays similar music based on music that woke up the user well in the past. In addition, the awakening unit can select optimal music by avoiding music that woke up the user poorly in the past. Furthermore, the awakening unit can select optimal music based on music that refreshed the user in the past. In this way, optimal music can be selected based on past awakening data.

[0052] The awakening unit can adjust the tempo and volume of the music based on the user's current activity plan when waking up. The awakening unit uses AI to adjust the tempo and volume of the music based on the user's current activity plan when waking up. The current activity plan includes, for example, calendar information, the type of plan, and the like, but is not limited to such examples. For example, the awakening unit plays music with a fast tempo on a day when the user is busy. The awakening unit can also play music with a slow tempo on a day when the user can relax. Furthermore, the awakening unit can play energetic music if the user plans to exercise. This allows the music playback method to be adjusted based on the current activity plan.

[0053] The awakening unit can select optimal music when waking up, taking into consideration the geographical location information of the user. The awakening unit uses AI to select optimal music when waking up, taking into consideration the geographical location information of the user. The geographical location information includes, but is not limited to, GPS data, location information services, and the like. For example, when the user is in nature, the awakening unit plays awakening music including natural sounds. In addition, when the user is in an urban area, the awakening unit can also play music that alleviates the hustle and bustle of the city. Furthermore, when the user is traveling, the awakening unit can also play music related to the culture of the travel destination. This allows optimal music to be selected based on the geographical location information.

[0054] The awakening unit can analyze the user's social media activity and play related music when the user wakes up. The awakening unit uses AI to analyze the user's social media activity and play related music when the user wakes up. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the reactions of followers. The awakening unit can play related music, for example, based on music shared by the user on social media. The awakening unit can also play music of artists the user follows on social media. Furthermore, the awakening unit can play related music based on music that the user "liked" on social media. This allows related music to be played based on social media activity.

[0055] The option expansion unit can suggest the optimal option by referring to the user's past music selection history when expanding the options. The option expansion unit uses AI to suggest the optimal option by referring to the user's past music selection history when expanding the options. The optimal option includes, for example, relaxing music selected in the past, the genre of music that the user liked to listen to, the tempo and volume, etc., but is not limited to such examples. For example, the option expansion unit suggests similar music based on relaxing music selected by the user in the past. In addition, the option expansion unit can also analyze the genre of music that the user liked to listen to in the past and suggest related music. Furthermore, the option expansion unit can also suggest the optimal music based on the tempo and volume of music selected by the user in the past. This makes it possible to suggest the optimal option based on the past selection history.

[0056] The option expansion unit can suggest highly relevant music in consideration of the geographical location information of the user when expanding the options. The option expansion unit uses AI to suggest highly relevant music in consideration of the geographical location information of the user when expanding the options. The geographical location information includes, for example, GPS data, location information services, and the like, but is not limited to such examples. For example, when the user is in nature, the option expansion unit can suggest relaxing music including natural sounds. In addition, when the user is in an urban area, the option expansion unit can also suggest music that alleviates the hustle and bustle of the city. Furthermore, when the user is traveling, the option expansion unit can also suggest music related to the culture of the travel destination. This makes it possible to suggest optimal music based on the geographical location information.

[0057] The data collection unit can select the optimal collection method by referring to the user's past data collection history when collecting data. The data collection unit uses AI to select the optimal collection method by referring to the user's past data collection history when collecting data. The optimal collection method includes, for example, a method that provided good data in the past, a method that caused dissatisfaction with data collection, a data collection method that allowed the user to relax, and the like, but is not limited to such examples. The data collection unit selects a similar method based on, for example, a method that provided good data in the past for the user. In addition, the data collection unit can also select the optimal method by avoiding a method that caused dissatisfaction with data collection in the past for the user. Furthermore, the data collection unit can also select the optimal method based on a data collection method that allowed the user to relax in the past. This allows the optimal collection method to be selected based on the past data collection history.

[0058] The data collection unit can select the optimal collection method in consideration of the geographical location information of the user when collecting data. The data collection unit uses AI to select the optimal collection method in consideration of the geographical location information of the user when collecting data. The geographical location information includes, but is not limited to, GPS data, location information services, and the like. For example, when the user is in nature, the data collection unit selects a data collection method that includes natural sounds. In addition, when the user is in an urban area, the data collection unit can also select a data collection method that reduces the hustle and bustle of the city. Furthermore, when the user is traveling, the data collection unit can also select a data collection method related to the culture of the travel destination. This allows the optimal collection method to be selected based on the geographical location information.

[0059] The data analysis unit can select the optimal analysis method by referring to the user's past data analysis history when analyzing data. The data analysis unit uses AI to select the optimal analysis method by referring to the user's past data analysis history when analyzing data. The optimal analysis method includes, for example, a method that provided good data in the past, a method that caused dissatisfaction with data analysis, a data analysis method that allowed the user to relax, and the like, but is not limited to such examples. For example, the data analysis unit selects a similar method based on a method that provided good data in the past for the user. In addition, the data analysis unit can select the optimal method by avoiding a method that caused dissatisfaction with data analysis in the past for the user. Furthermore, the data analysis unit can select the optimal method based on a data analysis method that allowed the user to relax in the past. This allows the optimal analysis method to be selected based on the past data analysis history.

[0060] The data analysis unit can select the optimal analysis method in consideration of the geographical location information of the user when analyzing data. The data analysis unit uses AI to select the optimal analysis method in consideration of the geographical location information of the user when analyzing data. The geographical location information includes, for example, GPS data, location information services, etc., but is not limited to such examples. For example, when the user is in nature, the data analysis unit selects a data analysis method including natural sounds. In addition, when the user is in an urban area, the data analysis unit can also select a data analysis method that reduces the hustle and bustle of the city. Furthermore, when the user is traveling, the data analysis unit can also select a data analysis method related to the culture of the travel destination. This allows the optimal analysis method to be selected based on the geographical location information.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The reception unit can also analyze the user's past sleep data and suggest optimal music. For example, similar music can be suggested based on music that the user found relaxing in the past. The reception unit can also analyze the genre of music that the user liked to listen to in the past and suggest related music. Furthermore, the reception unit can suggest optimal music based on the tempo and volume of music that the user selected in the past. In this way, optimal music can be suggested based on past data.

[0063] The option expansion unit can also suggest music options based on the user's current activity status. For example, if the user feels like relaxing, music with a high relaxing effect can be suggested. Also, if the user feels like concentrating, music that enhances concentration can be suggested. Furthermore, if the user is exercising, energetic music can be suggested. In this way, optimal music can be suggested based on the user's current activity status.

[0064] The data collection unit can also adjust the data collection method taking into account the user's geographic location information. For example, if the user is in nature, a data collection method that includes natural sounds can be selected. If the user is in an urban area, a data collection method that reduces the hustle and bustle of the city can be selected. Furthermore, if the user is traveling, a data collection method related to the culture of the travel destination can be selected. In this way, the optimal data collection method can be selected based on the geographic location information.

[0065] The data analysis unit can also analyze the user's social media activities and analyze related data. For example, the data analysis unit can analyze related data based on health information shared by the user on social media. The data analysis unit can also analyze related data based on information on health accounts the user follows on social media. The data analysis unit can also analyze related data based on health information that the user "likes" on social media. This makes it possible to analyze related data based on social media activities.

[0066] The process flow of the first embodiment will be briefly described below.

[0067] Step 1: The reception unit receives the user's selection of music. The user selects music through an application using a device such as a smartphone or tablet. For example, when the user selects "relaxing music," that information is sent to the AI. Step 2: The playback unit plays the music selected by the reception unit. The playback unit uses AI to analyze the characteristics of the music selected by the user and selects music with a high relaxing effect. For example, the AI ​​analyzes the tempo, volume, frequency, etc. of the music and plays the most relaxing music for the user. Step 3: The monitoring unit monitors the user's sleep state. Using AI, the monitoring unit collects data such as the user's heart rate, breathing rate, and movement, and determines the depth of sleep. For example, when the user enters a light sleep, the monitoring unit analyzes the data and plays music that helps wake the user up. Step 4: The wake-up unit plays music suitable for waking up when the user is in a specific sleep state. The wake-up unit uses AI to analyze the user's sleep state and select music suitable for waking up. For example, the AI ​​can play the chirping of birds in the morning or calm piano music to wake the user up comfortably.

[0068] (Example 2) The system according to the embodiment of the present invention is a system in which AI controls an invitation to a peaceful sleep through meditation or music played by a fabric speaker. In this system, the user selects meditation or relaxing music before going to bed. Next, the AI ​​plays the optimal music based on the user's selection to relax the user. Furthermore, the AI ​​monitors the user's sleep state and plays music that is good for waking up when the user is in a light sleep. This mechanism allows the user to have a peaceful sleep and wake up smoothly. For example, the user selects meditation or relaxing music before going to bed. In this case, the user selects music through an application using a device such as a smartphone or tablet. For example, when the user selects "relaxing music", the information is sent to the AI. Next, the AI ​​plays the optimal music based on the user's selection. The AI ​​analyzes the characteristics of the music selected by the user and selects music with a high relaxing effect. For example, the AI ​​analyzes the tempo, volume, frequency, etc. of the music and plays the optimal relaxing music for the user. Furthermore, the AI ​​monitors the user's sleep state. The AI ​​collects data such as the user's heart rate, breathing rate, and movement to determine the depth of sleep. For example, when a user enters light sleep, the AI ​​analyzes the data and plays music that is good for waking up. This mechanism allows the user to get a peaceful sleep. By having the AI ​​play relaxing music, the user can relax and fall asleep more easily. In addition, by having the AI ​​play music that is good for waking up when the user is in light sleep, the user can wake up naturally. For example, the AI ​​can play birds chirping in the morning or calm piano music, allowing the user to wake up feeling refreshed. In this way, the system can effectively support the user's sleep and provide high-quality sleep and a smooth awakening.

[0069] The sleep support system according to the embodiment includes a reception unit, a playback unit, a monitoring unit, and a wake-up unit. The reception unit allows a user to select music. When selecting music, the user selects music through an application using a device such as a smartphone or tablet. For example, when the user selects "relaxing music," the information is transmitted to the AI. The playback unit plays the music selected by the reception unit. The playback unit uses AI to analyze the characteristics of the music selected by the user and selects music with a high relaxing effect. For example, AI analyzes the tempo, volume, frequency, etc. of the music and plays the most suitable relaxing music for the user. The monitoring unit monitors the user's sleep state. The monitoring unit uses AI to collect data such as the user's heart rate, breathing rate, and movement, and determines the depth of sleep. For example, when the user enters a light sleep, the monitoring unit analyzes the data and plays music that is good for waking up. The wake-up unit plays music suitable for waking up when in a specific sleep state. The wake-up unit uses AI to analyze the user's sleep state and selects music suitable for waking up. For example, the AI ​​can play morning birdsong or calm piano music to wake the user up in a good mood. This allows the sleep support system according to the embodiment to consistently select music for the user, play music, monitor sleep, and play music to wake the user up.

[0070] The reception unit allows the user to select music. When selecting music, the user selects music through an application using a device such as a smartphone or tablet. Specifically, the user can launch the application and select their favorite music from categories such as relaxing music, natural sounds, and classical music. The application also has a function to recommend the most suitable music taking into consideration the user's past selection history, current mood, time of day, etc. For example, it recommends music with a high relaxing effect at night and music suitable for waking up in the morning. When the user selects music, the information is sent to the AI. The AI ​​analyzes the user's selection history and current state and collects data to select the most suitable music. This allows the reception unit to provide music according to the user's preferences and state. In addition, the reception unit can collect user feedback and continuously improve the music selection algorithm. For example, by evaluating the music selected by the user, such as "it was relaxing" or "it woke me up well," the AI ​​learns the evaluation and reflects it in the next and subsequent music selections. This allows the reception unit to provide a personalized music experience according to the user's needs.

[0071] The playback unit plays the music selected by the reception unit. The playback unit uses AI to analyze the characteristics of the music selected by the user and selects music that has a high relaxing effect. Specifically, the AI ​​analyzes the tempo, volume, frequency, melody pattern, etc. of the music and selects music that maximizes the user's relaxation. For example, music with a slow tempo, constant volume, and many low-frequency sounds is said to have a high relaxing effect. The AI ​​takes these characteristics into account and plays the most suitable relaxing music for the user. The playback unit can also monitor the user's real-time reaction and adjust the music as necessary. For example, if the user's heart rate or breathing rate changes suddenly, the playback unit adjusts the tempo and volume of the music to maintain the relaxing effect. Furthermore, the playback unit can also capture the user's environmental sounds and play music corresponding to them. For example, if the external noise is loud, the playback unit selects music that cancels it out and provides an environment in which the user can relax. As a result, the playback unit can provide the most suitable music according to the user's condition and environment, maximizing the relaxing effect.

[0072] The monitoring unit monitors the user's sleep state. The monitoring unit uses AI to collect data such as the user's heart rate, breathing rate, and movement, and determines the depth of sleep. Specifically, biometric data such as heart rate, breathing rate, body temperature, and movement are collected in real time through a wearable device worn by the user and sensors installed in the bed. These data are analyzed by AI to determine the user's sleep stage (light sleep, deep sleep, REM sleep, etc.). For example, a low heart rate and little movement are determined to be deep sleep. The monitoring unit evaluates the user's sleep quality based on these data and adjusts the music playback as necessary. For example, when the user enters light sleep, the monitoring unit analyzes the data and plays music that is good for waking up. The monitoring unit can also record the user's sleep patterns over the long term and provide advice to improve sleep quality. For example, it analyzes the user's sleep data and suggests optimal bedtimes, wake-up times, and relaxation methods. This allows the monitoring unit to grasp the user's sleep state in detail and provide support to improve sleep quality.

[0073] The awakening unit plays music suitable for waking up in a specific sleep state. The awakening unit uses AI to analyze the user's sleep state and select music suitable for waking up. Specifically, based on the sleep data provided by the monitoring unit, it plays music for waking up when the user is in a light sleep state. For example, the AI ​​can play morning bird chirps or gentle piano music to wake up the user comfortably. The awakening unit gradually increases the tempo and volume of the music to allow the user to wake up naturally. For example, by playing gentle music at a low volume at first and gradually increasing the volume, the user can wake up without difficulty. In addition, the awakening unit selects the optimal awakening music taking into account the user's preferences and past awakening data. For example, by playing music that the user used to wake up comfortably in the past, it provides a more comfortable awakening. In addition, the awakening unit can collect user feedback and continuously improve the selection algorithm for awakening music. As a result, the awakening unit can provide the optimal awakening music according to the user's sleep state and preferences, and support a comfortable awakening.

[0074] The sleep support system includes an option expansion unit that increases the types of music that the user can select. The option expansion unit increases the types of music that the user can select. The types of music include, but are not limited to, genres, artists, and albums. The option expansion unit can use AI to analyze the user's preferences and past selection history and suggest optimal music options. For example, based on relaxing music selected by the user in the past, similar music is suggested. The option expansion unit can also analyze the genre of music that the user liked to listen to in the past and suggest related music. Furthermore, the option expansion unit can also suggest optimal music based on the tempo and volume of music selected by the user in the past. This increases the types of music that the user can select, enabling a more diverse music experience.

[0075] The sleep support system includes a data collection unit that indicates a specific data collection method. The data collection unit indicates a specific data collection method. The data collection method includes, for example, a type of sensor, a frequency of data collection, a type of data to be collected, and the like, but is not limited to such examples. The data collection unit can use AI to collect data such as the user's heart rate, breathing rate, and movement, and determine the depth of sleep. For example, the data collection unit uses a heart rate sensor to monitor the user's heart rate in real time. The data collection unit can also use a breathing sensor to measure the user's breathing rate. Furthermore, the data collection unit can use a motion sensor to detect the user's movement and determine the depth of sleep. As a result, more accurate data collection is possible by specifically indicating the data collection method.

[0076] The sleep support system includes a data analysis unit that indicates the type of data and the analysis method that the AI ​​analyzes. The data analysis unit indicates the type of data and the analysis method that the AI ​​analyzes. The type of data to be analyzed includes, but is not limited to, heart rate, breathing rate, movement, and music characteristics, for example. The data analysis unit can use the AI ​​to analyze collected data and determine the user's sleep state. For example, the data analysis unit can use a machine learning algorithm to analyze heart rate and breathing rate patterns and determine the depth of sleep. The data analysis unit can also use a statistical analysis method to analyze movement data and evaluate the quality of sleep. Furthermore, the data analysis unit can analyze music characteristics and select music with a high relaxing effect. This clarifies the type and method of data analysis, thereby improving the analysis accuracy.

[0077] The playback unit can analyze the tempo, volume, frequency, etc. of the music and select music that is expected to have a relaxing effect. The playback unit uses AI to analyze the tempo, volume, frequency, etc. of the music and selects music that is expected to have a relaxing effect. Music that is expected to have a relaxing effect includes, for example, music with a slow tempo, low volume, and gentle frequency, but is not limited to such examples. For example, the playback unit uses AI to analyze the tempo of the music and selects music with a tempo that has a high relaxing effect. The playback unit can also use AI to analyze the volume of the music and select music with a volume that has a high relaxing effect. Furthermore, the playback unit can also use AI to analyze the frequency of the music and select music with a frequency that has a high relaxing effect. In this way, music with a high relaxing effect can be selected by analyzing the characteristics of the music.

[0078] The monitoring unit can collect data such as the user's heart rate, breathing rate, and movement, and determine the state of sleep. The monitoring unit uses AI to collect data such as the user's heart rate, breathing rate, and movement, and determine the state of sleep. The state of sleep includes, for example, REM sleep, non-REM sleep, light sleep, deep sleep, and the like, but is not limited to such examples. The monitoring unit monitors the user's heart rate in real time, for example, using a heart rate sensor. The monitoring unit can also measure the user's breathing rate using a breathing sensor. Furthermore, the monitoring unit can detect the user's movement using a motion sensor and determine the depth of sleep. This allows the user's biological data to be collected and the depth of sleep to be accurately determined.

[0079] The reception unit can estimate the user's emotion and suggest music options based on the estimated user's emotion. The reception unit uses AI to estimate the user's emotion and suggests music options based on the estimated user's emotion. The user's emotion includes, for example, stress, relaxation, fatigue, etc., but is not limited to such examples. The reception unit estimates the emotion from the user's facial expression, for example, using facial expression recognition technology. The reception unit can also estimate the emotion from the tone and speed of the user's voice using voice analysis technology. Furthermore, the reception unit can estimate the emotion by the user reporting his / her emotion using self-report. This makes it possible to suggest optimal music based on the user's emotion. For example, if the user is feeling stressed, the reception unit suggests music with a high relaxing effect. Furthermore, if the user is relaxed, the reception unit can also suggest music that is even more relaxing. Furthermore, if the user is tired, the reception unit can also suggest music with a healing effect. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows optimal music to be suggested based on the user's emotions.

[0080] The reception unit can analyze the user's past music selection history and automatically suggest the optimal music. The reception unit uses AI to analyze the user's past music selection history and automatically suggest the optimal music. The optimal music includes, for example, relaxing music selected in the past, the genre of music that the user liked to listen to, the tempo and volume, etc., but is not limited to such examples. For example, the reception unit suggests similar music based on relaxing music selected by the user in the past. The reception unit can also analyze the genre of music that the user liked to listen to in the past and suggest related music. Furthermore, the reception unit can also suggest the optimal music based on the tempo and volume of music selected by the user in the past. This makes it possible to suggest the optimal music based on the past selection history.

[0081] The reception unit can perform filtering based on the user's current mood and activity status when selecting music. The reception unit uses AI to perform filtering based on the user's current mood and activity status when selecting music. The current mood and activity status include, but are not limited to, for example, wanting to relax, wanting to concentrate, exercising, etc. For example, when the user feels like relaxing, the reception unit can preferentially display music that has a high relaxing effect. In addition, when the user feels like concentrating, the reception unit can also preferentially display music that enhances concentration. Furthermore, when the user is exercising, the reception unit can also preferentially display energetic music. This allows the optimal music to be selected based on the current mood and activity status.

[0082] The reception unit can estimate the user's emotion and prioritize the music options based on the estimated user's emotion. The reception unit uses AI to estimate the user's emotion and prioritize the music options based on the estimated user's emotion. Prioritization of the music options includes, for example, intensity of emotion, past selection history, user preferences, etc., but is not limited to such examples. For example, when the user is stressed, the reception unit preferentially displays music with a high relaxing effect. Furthermore, when the user is relaxed, the reception unit can also preferentially display music that is even more relaxing. Furthermore, when the user is tired, the reception unit can also preferentially display music that has a healing effect. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the music options to be prioritized based on emotion.

[0083] The reception unit can suggest highly relevant music in consideration of the geographical location information of the user when selecting music. The reception unit uses AI to suggest highly relevant music in consideration of the geographical location information of the user when selecting music. The geographical location information includes, but is not limited to, GPS data, location information services, and the like. For example, when the user is in nature, the reception unit can suggest relaxing music including natural sounds. In addition, when the user is in an urban area, the reception unit can also suggest music that eases the hustle and bustle of the city. Furthermore, when the user is traveling, the reception unit can also suggest music related to the culture of the travel destination. This makes it possible to suggest optimal music based on the geographical location information.

[0084] The reception unit can analyze the user's social media activity at the time of music selection and suggest related music. The reception unit uses AI to analyze the user's social media activity at the time of music selection and suggest related music. Social media activity includes, for example, the content of posts, the number of likes, and reactions of followers, but is not limited to such examples. The reception unit can suggest related music, for example, based on music shared by the user on social media. The reception unit can also suggest music of artists the user follows on social media. Furthermore, the reception unit can also suggest related music based on music that the user has "liked" on social media. This makes it possible to suggest related music based on social media activity.

[0085] The playback unit can estimate the user's emotion and adjust the music playback method based on the estimated user's emotion. The playback unit uses AI to estimate the user's emotion and adjust the music playback method based on the estimated user's emotion. The adjustment of the music playback method includes, for example, tempo, volume, playback order, etc., but is not limited to such examples. For example, when the user is relaxed, the playback unit adjusts the tempo of the music to be slow. In addition, when the user is stressed, the playback unit can also adjust the volume of the music to be low. Furthermore, when the user is tired, the playback unit can adjust the frequency of the music to have a healing effect. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to adjust the music playback method based on emotions.

[0086] The playback unit can select optimal music when playing music by analyzing the user's past relaxing effect. The playback unit uses AI to select optimal music when playing music. Optimal music includes, for example, music that has helped the user relax in the past, and the tempo, volume, and frequency of music that the user likes to listen to, but is not limited to such examples. The playback unit plays similar music based on, for example, music that has helped the user relax in the past. The playback unit can also analyze the tempo and volume of music that the user likes to listen to in the past and play optimal music. Furthermore, the playback unit can play optimal music based on the frequency of music that has helped the user relax in the past. This makes it possible to select optimal music based on the past relaxing effect.

[0087] The playback unit can adjust the tempo and volume of music based on the user's current activity status when playing music. The playback unit uses AI to adjust the tempo and volume of music based on the user's current activity status when playing music. The current activity status includes, but is not limited to, relaxing, concentrating, exercising, and the like. For example, when the user is relaxing, the playback unit adjusts the tempo of the music to be slow. In addition, when the user is concentrating, the playback unit can also adjust the volume of the music to be low. Furthermore, when the user is exercising, the playback unit can also adjust the tempo of the music to be fast. This makes it possible to adjust the music playback method based on the current activity status.

[0088] The playback unit can estimate the user's emotion and adjust the playback order of music based on the estimated user's emotion. The playback unit uses AI to estimate the user's emotion and adjust the playback order of music based on the estimated user's emotion. The adjustment of the playback order of music includes, for example, an order according to changes in emotion, user preferences, and the like, but is not limited to such examples. For example, when the user is relaxed, the playback unit first plays music with a high relaxing effect. In addition, when the user is stressed, the playback unit can also first play music with a healing effect. Furthermore, when the user is tired, the playback unit can also first play music with a refreshing effect. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to adjust the playback order of music based on emotion.

[0089] The playback unit can select optimal music in consideration of the geographical location information of the user when playing music. The playback unit uses AI to select optimal music in consideration of the geographical location information of the user when playing music. The geographical location information includes, but is not limited to, GPS data, location information services, and the like. For example, when the user is in nature, the playback unit plays relaxing music including natural sounds. In addition, when the user is in an urban area, the playback unit can also play music that alleviates the hustle and bustle of the city. Furthermore, when the user is traveling, the playback unit can also play music related to the culture of the travel destination. This allows optimal music to be selected based on the geographical location information.

[0090] The playback unit can analyze the user's social media activity and play related music when playing music. The playback unit uses AI to analyze the user's social media activity and play related music when playing music. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and reactions of followers. The playback unit plays related music, for example, based on music shared by the user on social media. The playback unit can also play music of artists the user follows on social media. Furthermore, the playback unit can also play related music based on music that the user has "liked" on social media. This makes it possible to play related music based on social media activity.

[0091] The monitoring unit can estimate the user's emotion and adjust the monitoring accuracy based on the estimated user's emotion. The monitoring unit uses AI to estimate the user's emotion and adjust the monitoring accuracy based on the estimated user's emotion. The adjustment of the monitoring accuracy includes, for example, the intensity of the emotion, past data, real-time feedback, etc., but is not limited to such examples. For example, the monitoring unit increases the monitoring accuracy when the user is relaxed. Also, the monitoring unit can lower the monitoring accuracy when the user is stressed. Furthermore, the monitoring unit can adjust the monitoring accuracy to a medium level when the user is tired. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the monitoring accuracy to be adjusted based on the emotion.

[0092] The monitoring unit can select the optimal monitoring method by referring to the user's past sleep data during monitoring. The monitoring unit uses AI to select the optimal monitoring method by referring to the user's past sleep data during monitoring. The optimal monitoring method includes, for example, data on the user having had a good sleep in the past, data on the user having experienced a sleep disorder, data on the user being able to relax, and the like, but is not limited to such examples. For example, the monitoring unit selects a similar monitoring method based on data on the user having had a good sleep in the past. The monitoring unit can also select a monitoring method including an improvement measure based on data on the user having experienced a sleep disorder in the past. Furthermore, the monitoring unit can also select the optimal monitoring method based on data on the user having been able to relax in the past. This makes it possible to select the optimal monitoring method based on the past sleep data.

[0093] The monitoring unit can adjust the monitoring parameters based on the user's current health condition during monitoring. The monitoring unit adjusts the monitoring parameters based on the user's current health condition during monitoring using AI. The current health condition includes, for example, heart rate, blood pressure, activity level, etc., but is not limited to such examples. For example, when the user is healthy, the monitoring unit sets the monitoring parameters to standard. In addition, when the user is tired, the monitoring unit can also set the monitoring parameters to be gentle. Furthermore, when the user is in poor health, the monitoring unit can also set the monitoring parameters to be strict. This makes it possible to adjust the monitoring parameters based on the current health condition.

[0094] The monitoring unit can estimate the user's emotion during monitoring and adjust the monitoring frequency based on the estimated user's emotion. The monitoring unit uses AI to estimate the user's emotion during monitoring and adjust the monitoring frequency based on the estimated user's emotion. The adjustment of the monitoring frequency includes, for example, changes in emotion, activity status, health status, etc., but is not limited to such examples. For example, the monitoring unit reduces the monitoring frequency when the user is relaxed. In addition, the monitoring unit can also increase the monitoring frequency when the user is stressed. Furthermore, the monitoring unit can adjust the monitoring frequency to a moderate level when the user is tired. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to adjust the monitoring frequency based on emotion.

[0095] The monitoring unit can select the optimal monitoring method in consideration of the geographical location information of the user during monitoring. The monitoring unit uses AI to select the optimal monitoring method in consideration of the geographical location information of the user during monitoring. The geographical location information includes, for example, GPS data, location information services, etc., but is not limited to such examples. For example, when the user is in nature, the monitoring unit selects a monitoring method including natural sounds. In addition, when the user is in an urban area, the monitoring unit can also select a monitoring method that reduces the hustle and bustle of the city. Furthermore, when the user is traveling, the monitoring unit can also select a monitoring method related to the culture of the travel destination. This allows the optimal monitoring method to be selected based on the geographical location information.

[0096] The monitoring unit can analyze the user's social media activity during monitoring to collect related data. The monitoring unit uses AI to analyze the user's social media activity during monitoring to collect related data. Social media activity includes, for example, the content of posts, the number of likes, and reactions of followers, but is not limited to such examples. The monitoring unit collects related data, for example, based on health information shared by the user on social media. The monitoring unit can also collect related data based on information on health accounts that the user follows on social media. Furthermore, the monitoring unit can also collect related data based on health information that the user "liked" on social media. This makes it possible to collect related data based on social media activity.

[0097] The awakening unit can estimate the user's emotion and adjust the awakening music based on the estimated user's emotion. The awakening unit uses AI to estimate the user's emotion and adjust the awakening music based on the estimated user's emotion. The adjustment of the awakening music includes, for example, tempo, volume, genre, etc., but is not limited to such examples. For example, the awakening unit plays calm music when the user is relaxed. In addition, the awakening unit can also play music with a healing effect when the user is stressed. Furthermore, the awakening unit can also play music with a refreshing effect when the user is tired. The estimation of emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the awakening music to be adjusted based on emotion.

[0098] The awakening unit can select optimal music by referring to the user's past awakening data when waking up. The awakening unit uses AI to select optimal music by referring to the user's past awakening data when waking up. Optimal music includes, for example, music that woke up the user well in the past, music that woke up poorly, and music that refreshed the user, but is not limited to such examples. For example, the awakening unit plays similar music based on music that woke up the user well in the past. In addition, the awakening unit can select optimal music by avoiding music that woke up the user poorly in the past. Furthermore, the awakening unit can select optimal music based on music that refreshed the user in the past. In this way, optimal music can be selected based on past awakening data.

[0099] The awakening unit can adjust the tempo and volume of the music based on the user's current activity plan when waking up. The awakening unit uses AI to adjust the tempo and volume of the music based on the user's current activity plan when waking up. The current activity plan includes, for example, calendar information, the type of plan, and the like, but is not limited to such examples. For example, the awakening unit plays music with a fast tempo on a day when the user is busy. The awakening unit can also play music with a slow tempo on a day when the user can relax. Furthermore, the awakening unit can play energetic music if the user plans to exercise. This allows the music playback method to be adjusted based on the current activity plan.

[0100] The awakening unit can estimate the user's emotion and adjust the order of the awakening music based on the estimated user's emotion. The awakening unit uses AI to estimate the user's emotion and adjust the order of the awakening music based on the estimated user's emotion. Adjusting the order of the awakening music includes, for example, an order according to changes in emotion, user preferences, and the like, but is not limited to such examples. For example, the awakening unit plays calm music first when the user is relaxed. Also, the awakening unit can play music with a healing effect first when the user is stressed. Furthermore, the awakening unit can play music with a refreshing effect first when the user is tired. The estimation of emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the order of the awakening music to be adjusted based on emotion.

[0101] The awakening unit can select optimal music when waking up, taking into consideration the geographical location information of the user. The awakening unit uses AI to select optimal music when waking up, taking into consideration the geographical location information of the user. The geographical location information includes, but is not limited to, GPS data, location information services, and the like. For example, when the user is in nature, the awakening unit plays awakening music including natural sounds. In addition, when the user is in an urban area, the awakening unit can also play music that alleviates the hustle and bustle of the city. Furthermore, when the user is traveling, the awakening unit can also play music related to the culture of the travel destination. This allows optimal music to be selected based on the geographical location information.

[0102] The awakening unit can analyze the user's social media activity and play related music when the user wakes up. The awakening unit uses AI to analyze the user's social media activity and play related music when the user wakes up. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the reactions of followers. The awakening unit can play related music, for example, based on music shared by the user on social media. The awakening unit can also play music of artists the user follows on social media. Furthermore, the awakening unit can play related music based on music that the user "liked" on social media. This allows related music to be played based on social media activity.

[0103] The option expansion unit can estimate the user's emotion and expand the music options based on the estimated user's emotion. The option expansion unit uses AI to estimate the user's emotion and expand the music options based on the estimated user's emotion. The expansion of the music options includes, for example, changes in emotion, past selection history, user preferences, etc., but is not limited to such examples. For example, when the user is relaxed, the option expansion unit expands the music options with a high relaxing effect. In addition, when the user is stressed, the option expansion unit can also expand the music options with a healing effect. Furthermore, when the user is tired, the option expansion unit can also expand the music options with a refreshing effect. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the music options to be expanded based on emotions.

[0104] The option expansion unit can suggest the optimal option by referring to the user's past music selection history when expanding the options. The option expansion unit uses AI to suggest the optimal option by referring to the user's past music selection history when expanding the options. The optimal option includes, for example, relaxing music selected in the past, the genre of music that the user liked to listen to, the tempo and volume, etc., but is not limited to such examples. For example, the option expansion unit suggests similar music based on relaxing music selected by the user in the past. In addition, the option expansion unit can also analyze the genre of music that the user liked to listen to in the past and suggest related music. Furthermore, the option expansion unit can also suggest the optimal music based on the tempo and volume of music selected by the user in the past. This makes it possible to suggest the optimal option based on the past selection history.

[0105] The option expansion unit can estimate the user's emotion and prioritize the music options based on the estimated user's emotion. The option expansion unit uses AI to estimate the user's emotion and prioritize the music options based on the estimated user's emotion. Prioritization of the music options includes, for example, the intensity of emotion, past selection history, user preferences, etc., but is not limited to such examples. For example, when the user is relaxed, the option expansion unit can preferentially display music with a high relaxing effect. In addition, when the user is stressed, the option expansion unit can also preferentially display music with a healing effect. Furthermore, when the user is tired, the option expansion unit can also preferentially display music with a refreshing effect. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the music options to be prioritized based on emotion.

[0106] The option expansion unit can suggest highly relevant music in consideration of the geographical location information of the user when expanding the options. The option expansion unit uses AI to suggest highly relevant music in consideration of the geographical location information of the user when expanding the options. The geographical location information includes, for example, GPS data, location information services, and the like, but is not limited to such examples. For example, when the user is in nature, the option expansion unit can suggest relaxing music including natural sounds. In addition, when the user is in an urban area, the option expansion unit can also suggest music that alleviates the hustle and bustle of the city. Furthermore, when the user is traveling, the option expansion unit can also suggest music related to the culture of the travel destination. This makes it possible to suggest optimal music based on the geographical location information.

[0107] The data collection unit can estimate the user's emotion and adjust the data collection method based on the estimated user's emotion. The data collection unit uses AI to estimate the user's emotion and adjust the data collection method based on the estimated user's emotion. The adjustment of the data collection method includes, for example, changes in emotion, past data, real-time feedback, etc., but is not limited to such examples. For example, the data collection unit reduces the frequency of data collection when the user is relaxed. In addition, the data collection unit can also increase the frequency of data collection when the user is stressed. Furthermore, the data collection unit can adjust the frequency of data collection to a moderate level when the user is tired. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the data collection method to be adjusted based on emotion.

[0108] The data collection unit can select the optimal collection method by referring to the user's past data collection history when collecting data. The data collection unit uses AI to select the optimal collection method by referring to the user's past data collection history when collecting data. The optimal collection method includes, for example, a method that provided good data in the past, a method that caused dissatisfaction with data collection, a data collection method that allowed the user to relax, and the like, but is not limited to such examples. The data collection unit selects a similar method based on, for example, a method that provided good data in the past for the user. In addition, the data collection unit can also select the optimal method by avoiding a method that caused dissatisfaction with data collection in the past for the user. Furthermore, the data collection unit can also select the optimal method based on a data collection method that allowed the user to relax in the past. This allows the optimal collection method to be selected based on the past data collection history.

[0109] The data collection unit can estimate the user's emotion and adjust the frequency of data collection based on the estimated user's emotion. The data collection unit uses AI to estimate the user's emotion and adjust the frequency of data collection based on the estimated user's emotion. The adjustment of the frequency of data collection includes, for example, changes in emotion, activity status, health status, etc., but is not limited to such examples. For example, the data collection unit reduces the frequency of data collection when the user is relaxed. In addition, the data collection unit can also increase the frequency of data collection when the user is stressed. Furthermore, the data collection unit can adjust the frequency of data collection to a moderate level when the user is tired. The estimation of emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the frequency of data collection to be adjusted based on emotion.

[0110] The data collection unit can select the optimal collection method in consideration of the geographical location information of the user when collecting data. The data collection unit uses AI to select the optimal collection method in consideration of the geographical location information of the user when collecting data. The geographical location information includes, but is not limited to, GPS data, location information services, and the like. For example, when the user is in nature, the data collection unit selects a data collection method that includes natural sounds. In addition, when the user is in an urban area, the data collection unit can also select a data collection method that reduces the hustle and bustle of the city. Furthermore, when the user is traveling, the data collection unit can also select a data collection method related to the culture of the travel destination. This allows the optimal collection method to be selected based on the geographical location information.

[0111] The data analysis unit can estimate the user's emotion and adjust the data analysis method based on the estimated user's emotion. The data analysis unit uses AI to estimate the user's emotion and adjust the data analysis method based on the estimated user's emotion. The adjustment of the data analysis method includes, for example, changes in emotion, past data, real-time feedback, etc., but is not limited to such examples. For example, the data analysis unit reduces the frequency of data analysis when the user is relaxed. In addition, the data analysis unit can also increase the frequency of data analysis when the user is stressed. Furthermore, the data analysis unit can adjust the frequency of data analysis to a moderate level when the user is tired. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the data analysis method to be adjusted based on emotion.

[0112] The data analysis unit can select the optimal analysis method by referring to the user's past data analysis history when analyzing data. The data analysis unit uses AI to select the optimal analysis method by referring to the user's past data analysis history when analyzing data. The optimal analysis method includes, for example, a method that provided good data in the past, a method that caused dissatisfaction with data analysis, a data analysis method that allowed the user to relax, and the like, but is not limited to such examples. For example, the data analysis unit selects a similar method based on a method that provided good data in the past for the user. In addition, the data analysis unit can select the optimal method by avoiding a method that caused dissatisfaction with data analysis in the past for the user. Furthermore, the data analysis unit can select the optimal method based on a data analysis method that allowed the user to relax in the past. This allows the optimal analysis method to be selected based on the past data analysis history.

[0113] The data analysis unit can estimate the user's emotion and adjust the frequency of data analysis based on the estimated user's emotion. The data analysis unit uses AI to estimate the user's emotion and adjust the frequency of data analysis based on the estimated user's emotion. The adjustment of the frequency of data analysis includes, for example, changes in emotion, activity status, health status, etc., but is not limited to such examples. For example, the data analysis unit reduces the frequency of data analysis when the user is relaxed. In addition, the data analysis unit can also increase the frequency of data analysis when the user is stressed. Furthermore, the data analysis unit can adjust the frequency of data analysis to a moderate level when the user is tired. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the frequency of data analysis to be adjusted based on emotion.

[0114] The data analysis unit can select the optimal analysis method in consideration of the geographical location information of the user when analyzing data. The data analysis unit uses AI to select the optimal analysis method in consideration of the geographical location information of the user when analyzing data. The geographical location information includes, for example, GPS data, location information services, etc., but is not limited to such examples. For example, when the user is in nature, the data analysis unit selects a data analysis method including natural sounds. In addition, when the user is in an urban area, the data analysis unit can also select a data analysis method that reduces the hustle and bustle of the city. Furthermore, when the user is traveling, the data analysis unit can also select a data analysis method related to the culture of the travel destination. This allows the optimal analysis method to be selected based on the geographical location information.

[0115] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0116] The reception unit can also analyze the user's past sleep data and suggest optimal music. For example, similar music can be suggested based on music that the user found relaxing in the past. The reception unit can also analyze the genre of music that the user liked to listen to in the past and suggest related music. Furthermore, the reception unit can suggest optimal music based on the tempo and volume of music that the user selected in the past. In this way, optimal music can be suggested based on past data.

[0117] The option expansion unit can also suggest music options based on the user's current activity status. For example, if the user feels like relaxing, music with a high relaxing effect can be suggested. Also, if the user feels like concentrating, music that enhances concentration can be suggested. Furthermore, if the user is exercising, energetic music can be suggested. In this way, optimal music can be suggested based on the user's current activity status.

[0118] The data collection unit can also adjust the data collection method taking into account the user's geographic location information. For example, if the user is in nature, a data collection method that includes natural sounds can be selected. If the user is in an urban area, a data collection method that reduces the hustle and bustle of the city can be selected. Furthermore, if the user is traveling, a data collection method related to the culture of the travel destination can be selected. In this way, the optimal data collection method can be selected based on the geographic location information.

[0119] The data analysis unit can also analyze the user's social media activities and analyze related data. For example, the data analysis unit can analyze related data based on health information shared by the user on social media. The data analysis unit can also analyze related data based on information on health accounts the user follows on social media. The data analysis unit can also analyze related data based on health information that the user "likes" on social media. This makes it possible to analyze related data based on social media activities.

[0120] The playback unit can also estimate the user's emotions and adjust the music playback method based on the estimated user's emotions. For example, if the user is relaxed, the tempo of the music can be adjusted to be slower. Also, if the user is stressed, the volume of the music can be adjusted to be lower. Furthermore, if the user is tired, the frequency of the music can be adjusted to have a soothing effect. In this way, the music playback method can be adjusted based on emotions.

[0121] The monitoring unit can also estimate the user's emotion and adjust the monitoring accuracy based on the estimated user's emotion. For example, when the user is relaxed, the monitoring accuracy can be increased. Also, when the user is stressed, the monitoring accuracy can be decreased. Furthermore, when the user is tired, the monitoring accuracy can be adjusted to a medium level. In this way, the monitoring accuracy can be adjusted based on the emotion.

[0122] The reception unit can also estimate the user's emotions and prioritize music options based on the estimated user's emotions. For example, if the user is feeling stressed, music with a high relaxing effect can be preferentially displayed. Also, if the user is relaxed, music that is even more relaxing can be preferentially displayed. Furthermore, if the user is tired, music with a soothing effect can be preferentially displayed. In this way, music options can be prioritized based on emotions.

[0123] The awakening unit can also estimate the user's emotions and adjust the awakening music based on the estimated user's emotions. For example, if the user is relaxed, calm music can be played. Also, if the user is stressed, music with a soothing effect can be played. Furthermore, if the user is tired, music with a refreshing effect can be played. In this way, the awakening music can be adjusted based on emotions.

[0124] The playback unit can also estimate the user's emotion and adjust the playback order of music based on the estimated user's emotion. For example, if the user is relaxed, music with a strong relaxing effect can be played first. Also, if the user is stressed, music with a healing effect can be played first. Furthermore, if the user is tired, music with a refreshing effect can be played first. In this way, the playback order of music can be adjusted based on emotion.

[0125] The data collection unit can also estimate the user's emotion and adjust the method of data collection based on the estimated user's emotion. For example, when the user is relaxed, the frequency of data collection can be reduced. Also, when the user is stressed, the frequency of data collection can be increased. Furthermore, when the user is tired, the frequency of data collection can be adjusted to a medium level. In this way, the method of data collection can be adjusted based on emotion.

[0126] The process flow of the second embodiment will be briefly described below.

[0127] Step 1: The reception unit receives the user's selection of music. The user selects music through an application using a device such as a smartphone or tablet. For example, when the user selects "relaxing music," that information is sent to the AI. Step 2: The playback unit plays the music selected by the reception unit. The playback unit uses AI to analyze the characteristics of the music selected by the user and selects music with a high relaxing effect. For example, the AI ​​analyzes the tempo, volume, frequency, etc. of the music and plays the most relaxing music for the user. Step 3: The monitoring unit monitors the user's sleep state. Using AI, the monitoring unit collects data such as the user's heart rate, breathing rate, and movement, and determines the depth of sleep. For example, when the user enters a light sleep, the monitoring unit analyzes the data and plays music that helps wake the user up. Step 4: The wake-up unit plays music suitable for waking up when the user is in a specific sleep state. The wake-up unit uses AI to analyze the user's sleep state and select music suitable for waking up. For example, the AI ​​can play the chirping of birds in the morning or calm piano music to wake the user up comfortably.

[0128] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0129] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0130] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0131] For example, the reception unit allows the user to select music using the reception device 38 of the smart device 14. The playback unit plays the selected music using the output device 40 of the smart device 14. The monitoring unit monitors the user's sleep state using the camera 42 and microphone 38B of the smart device 14. The awakening unit plays music suitable for waking up using the output device 40 of the smart device 14. The option expansion unit analyzes the user's preferences and past selection history using the specific processing unit 290 of the data processing device 12, and proposes optimal music options. The data collection unit collects data using the camera 42 and microphone 38B of the smart device 14. The data analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and determines the user's sleep state. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0132] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0133] 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.

[0134] 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, the RAM 30, and the 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.

[0135] 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.

[0136] 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 the voice according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0138] 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 performed in a secure state.

[0139] Fig. 4 shows an example of 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.

[0140] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0142] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0144] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0146] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0147] For example, the reception unit allows the user to select music using the microphone 238 of the smart glasses 214. The playback unit plays the selected music using the speaker 240 of the smart glasses 214. The monitoring unit monitors the user's sleep state using the camera 42 and the microphone 238 of the smart glasses 214. The awakening unit plays music suitable for waking up using the speaker 240 of the smart glasses 214. The option expansion unit analyzes the user's preferences and past selection history using the specific processing unit 290 of the data processing device 12, and suggests optimal music options. The data collection unit collects data using the camera 42 and the microphone 238 of the smart glasses 214. The data analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and determines the user's sleep state. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0148] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0149] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0150] 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, the RAM 30, and the 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.

[0151] 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.

[0152] 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 the voice according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0154] 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 performed in a secure state.

[0155] 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.

[0156] 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.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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 identification processing unit 290 using these models.

[0159] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] 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 voice indicating a user input for 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.

[0161] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0163] For example, the reception unit allows the user to select music using the microphone 238 of the headset type terminal 314. The playback unit plays the selected music using the speaker 240 of the headset type terminal 314. The monitoring unit monitors the user's sleep state using the camera 42 and microphone 238 of the headset type terminal 314. The awakening unit plays music suitable for waking up using the speaker 240 of the headset type terminal 314. The option expansion unit analyzes the user's preferences and past selection history using the specific processing unit 290 of the data processing device 12, and proposes optimal music options. The data collection unit collects data using the camera 42 and microphone 238 of the headset type terminal 314. The data analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and determines the user's sleep state. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0164] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0165] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0166] 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, the RAM 30, and the 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.

[0167] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0168] 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 the voice according to instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0170] 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 performed in a secure state.

[0171] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0172] 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.

[0173] 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.

[0174] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0175] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 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.

[0176] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0177] 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 a voice indicating a user input for 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.

[0178] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0179] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0180] For example, the reception unit allows the user to select music using the microphone 238 of the robot 414. The playback unit plays the selected music using the speaker 240 of the robot 414. The monitoring unit monitors the user's sleep state using the camera 42 and microphone 238 of the robot 414. The awakening unit plays music suitable for waking up using the speaker 240 of the robot 414. The option expansion unit analyzes the user's preferences and past selection history using the specific processing unit 290 of the data processing device 12, and proposes optimal music options. The data collection unit collects data using the camera 42 and microphone 238 of the robot 414. The data analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and determines the user's sleep state. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0181] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0182] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0183] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0184] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0185] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0186] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0187] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0188] 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.

[0189] 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 Universal Serial Bus (USB) 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.

[0190] In addition, 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 upon request from the data processing device 12.

[0191] 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.

[0192] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0193] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0194] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0195] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0196] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0197] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0198] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0199] (Appendix 1) a reception unit for allowing a user to select music; a playback unit that plays the music selected by the reception unit; A monitoring unit that monitors a sleep state of a user; A wake-up unit that plays music suitable for waking up when the user is in a specific sleep state. A system characterized by: (Appendix 2) Equipped with a selection expansion section that increases the variety of music that users can select 2. The system of claim 1, (Appendix 3) Equipped with a data collection section that shows specific data collection methods 2. The system of claim 1, (Appendix 4) Equipped with a data analysis section that indicates the type of data that the AI ​​will analyze and the analysis method 2. The system of claim 1, (Appendix 5) The reproducing unit includes: The tempo, volume, frequency, etc. of the music are analyzed to select music that is expected to have a relaxing effect. 2. The system of claim 1, (Appendix 6) The monitoring unit is It collects data such as the user's heart rate, breathing rate, and movement to determine sleep state. 2. The system of claim 1, (Appendix 7) The reception unit is Infer user emotions and suggest music options based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past music selection history and automatically suggest the best music 2. The system of claim 1, (Appendix 9) The reception unit is Filter music selection based on the user's current mood and activity 2. The system of claim 1, (Appendix 10) The reception unit is Inferring user emotions and prioritizing music choices based on the inferred user emotions 2. The system of claim 1, (Appendix 11) The reception unit is When selecting music, the app takes into account the user's geographic location to suggest relevant music. 2. The system of claim 1, (Appendix 12) The reception unit is When selecting music, the app analyzes users' social media activity to suggest related music 2. The system of claim 1, (Appendix 13) The reproducing unit includes: Estimating a user's emotions and adjusting the way music is played based on the estimated user's emotions 2. The system of claim 1, (Appendix 14) The reproducing unit includes: When playing music, the system analyzes the user's past relaxation effects and selects the most suitable music. 2. The system of claim 1, (Appendix 15) The reproducing unit includes: When playing music, adjust the tempo or volume of the music based on the user's current activity 2. The system of claim 1, (Appendix 16) The reproducing unit includes: Estimating a user's emotions and adjusting the order of music playback based on the estimated user's emotions 2. The system of claim 1, (Appendix 17) The reproducing unit includes: When playing music, the most suitable music is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 18) The reproducing unit includes: When playing music, analyze your social media activity to play relevant music 2. The system of claim 1, (Appendix 19) The monitoring unit is Estimate the user's emotions and adjust the accuracy of monitoring based on the estimated user emotions. 2. The system of claim 1, (Appendix 20) The monitoring unit is When monitoring, the optimal monitoring method is selected by referring to the user's past sleep data. 2. The system of claim 1, (Appendix 21) The monitoring unit is During monitoring, adjust the monitoring parameters based on the user's current health condition 2. The system of claim 1, (Appendix 22) The monitoring unit is During monitoring, the user's emotions are estimated, and the frequency of monitoring is adjusted based on the estimated user's emotions. 2. The system of claim 1, (Appendix 23) The monitoring unit is When monitoring, the optimal monitoring method is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 24) The monitoring unit is During monitoring, analyze users' social media activities to collect relevant data 2. The system of claim 1, (Appendix 25) The awakening unit includes: To estimate a user's emotion and adjust wake-up music based on the estimated user's emotion. 2. The system of claim 1, (Appendix 26) The awakening unit includes: When waking up, the system selects the most suitable music by referring to the user's past awakening data. 2. The system of claim 1, (Appendix 27) The awakening unit includes: Upon waking, adjust the tempo and volume of your music based on your current schedule 2. The system of claim 1, (Appendix 28) The awakening unit includes: Estimating a user's emotion and adjusting the order of wake-up music based on the estimated user's emotion 2. The system of claim 1, (Appendix 29) The awakening unit includes: When you wake up, the app selects the best music for you based on your geographic location. 2. The system of claim 1, (Appendix 30) The awakening unit includes: Analyze your social media activity and play relevant music when you wake up 2. The system of claim 1, (Appendix 31) The option expansion unit is Estimating user emotions and expanding music choices based on the estimated user emotions 3. The system of claim 2, (Appendix 32) The option expansion unit is When expanding the options, the system suggests optimal options by referring to the user's past music selection history. 3. The system of claim 2, (Appendix 33) The option expansion unit is Inferring user emotions and prioritizing music choices based on the inferred user emotions 3. The system of claim 2, (Appendix 34) The option expansion unit is When expanding the options, the system takes into account the user's geographic location to suggest more relevant music. 3. The system of claim 2, (Appendix 35) The data collection unit includes: Inferring user emotions and adjusting data collection methods based on the inferred user emotions 4. The system of claim 3. (Appendix 36) The data collection unit includes: When collecting data, select the optimal collection method by referring to the user's past data collection history. 4. The system of claim 3. (Appendix 37) The data collection unit includes: Inferring user sentiment and adjusting the frequency of data collection based on the inferred user sentiment 4. The system of claim 3. (Appendix 38) The data collection unit includes: When collecting data, choose the most appropriate collection method taking into account the user's geographic location information 4. The system of claim 3. (Appendix 39) The data analysis unit Infer user sentiment and adjust data analysis methods based on the inferred user sentiment 5. The system of claim 4. (Appendix 40) The data analysis unit When analyzing data, select the optimal analysis method by referring to the user's past data analysis history. 5. The system of claim 4. (Appendix 41) The data analysis unit Infer user sentiment and adjust the frequency of data analysis based on the estimated user sentiment. 5. The system of claim 4. (Appendix 42) The data analysis unit When analyzing data, select the optimal analysis method taking into account the user's geographic location information 5. The system of claim 4. [Explanation of symbols]

[0200] 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 reception unit for allowing a user to select music; a playback unit that plays the music selected by the reception unit; A monitoring unit that monitors a sleep state of a user; A wake-up unit that plays music suitable for waking up when the user is in a specific sleep state. A system characterized by:

2. Equipped with a selection expansion section that increases the variety of music that users can select 2. The system of claim 1 .

3. Equipped with a data collection section that shows specific data collection methods 2. The system of claim 1 .

4. Equipped with a data analysis section that indicates the type of data that the AI ​​will analyze and the analysis method 2. The system of claim 1 .

5. The reproducing unit includes: The tempo, volume, frequency, etc. of the music are analyzed to select music that is expected to have a relaxing effect.

2. The system of claim 1 .

6. The monitoring unit is It collects data such as the user's heart rate, breathing rate, and movement to determine the state of sleep.

2. The system of claim 1 .

7. The reception unit is Infer user emotions and suggest music options based on the estimated user emotions 2. The system of claim 1 .

8. The reception unit is Analyze the user's past music selection history and automatically suggest the best music 2. The system of claim 1 .

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