System

Abstract

An object of a system according to an embodiment is to automatically generate and reproduce music according to a mental state of a user and a surrounding environment.SOLUTION: A system includes a sound recognition part, a mental state estimation part, an information collection part, a music generation part, and a reproduction part. The sound recognition unit recognizes a sound obtained from a user or a surrounding environment. The mental state estimation unit estimates a mental state of the user on the basis of the sound recognized by the sound recognition unit. The information collection unit collects weather information and position information. The music generation unit generates music based on the information obtained by the mental state estimation unit and the information collection unit. The reproduction unit reproduces the music generated by the music generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have not adequately addressed the automatic generation and playback of music that responds to the user's mental state and surrounding environment, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically generate and play music that matches the user's mental state and surrounding environment. [Means for solving the problem]

[0006] The system according to the embodiment includes a sound recognition unit, a mental state estimation unit, an information collection unit, a music generation unit, and a playback unit. The sound recognition unit recognizes sounds obtained from the user or the surrounding environment. The mental state estimation unit estimates the user's mental state based on the sounds recognized by the sound recognition unit. The information collection unit collects weather information and location information. The music generation unit generates music based on information obtained by the mental state estimation unit and the information collection unit. The playback unit plays the music generated by the music generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate and play music that matches the user's mental state and surrounding environment. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A music generation system according to an embodiment of the present invention recognizes sounds and weather information obtained from the user and the surrounding environment, and automatically generates and plays music with a similar tempo and atmosphere based on the recognized sounds and weather information. This allows the music generation system to generate and play appropriate music based on the user's mental state and environmental information.

[0029] A music generation system according to an embodiment includes a sound recognition unit, a mental state estimation unit, an information collection unit, a music generation unit, and a playback unit. The sound recognition unit recognizes sounds obtained from the user and the surrounding environment. For example, the heart rate is measured using a heart rate sensor or a smartwatch, and walking sounds are collected using a smartphone microphone or a dedicated device. The sound recognition unit can also collect breathing sounds and body temperature data. The mental state estimation unit estimates the user's mental state based on the sounds recognized by the sound recognition unit. For example, a high heart rate is determined to indicate tension or stress, and a low heart rate is determined to indicate relaxation. The information collection unit collects meteorological information and location information. For example, meteorological information such as the current weather, temperature, and humidity is acquired to determine the user's location. The music generation unit generates music based on the information obtained by the mental state estimation unit and the information collection unit. For example, if the heart rate is high and walking sounds are fast, fast-tempo rhythmic music is generated. The playback unit plays the music generated by the music generation unit. For example, the music is played through a smartphone or speaker. This allows the music generation system to generate and play appropriate music based on the user's mental state and environmental information.

[0030] The sound recognition unit collects breathing sounds and body temperature data in addition to heartbeat or walking sounds, allowing it to generate music based on more detailed biometric information. For example, the sound recognition unit uses a heartbeat sensor or a smartwatch, as well as a microphone for collecting breathing sounds, to analyze the rhythm and depth of breathing. This allows it to generate music that takes into account not only heartbeat and walking sounds, but also breathing patterns. The sound recognition unit also uses a body temperature sensor to collect the user's body temperature data and adjusts the tempo and rhythm of the music based on fluctuations in body temperature. For example, if the body temperature is rising, it generates energetic music. The sound recognition unit also analyzes breathing sounds and body temperature data in real time and combines them with heartbeat and walking sounds to generate music based on more accurate biometric information. This allows it to generate music that is optimal for the user's condition based on more detailed biometric information.

[0031] The sound recognition unit can learn the user's past heart rate data or walking patterns and develop a music generation algorithm optimized for each individual user. For example, the sound recognition unit collects the user's past heart rate data and analyzes long-term heart rate fluctuation patterns. This allows the development of a music generation algorithm optimized for each individual user. The sound recognition unit also collects walking sound data over a long period of time and learns the user's walking pattern. For example, it analyzes fluctuations in walking speed and rhythm and generates music based on that. The sound recognition unit also customizes the music generation algorithm according to the user's condition based on the past heart rate data and walking patterns. For example, it provides music tailored to a specific time period or activity. This allows the provision of music optimized for each individual user.

[0032] The sound recognition unit can also collect the heartbeat or walking sounds of the user's pet and generate music that can be enjoyed together with the pet. The sound recognition unit, for example, uses a pet heartbeat sensor to collect the pet's heartbeat data and generates music that can be enjoyed together with the pet based on that data. For example, when the pet is relaxed, it provides calm music. The sound recognition unit has also developed a dedicated device for collecting the pet's walking sounds and analyzes the pet's walking pattern. This generates music that matches the pet's movements. The sound recognition unit can also simultaneously collect the heartbeats and walking sounds of the user and the pet and generate music based on both data. For example, when walking together with the pet, it provides music that matches a common rhythm. This makes it possible to provide music that can be enjoyed together with the pet.

[0033] The sound recognition unit generates music according to the exercise intensity during sports or fitness, thereby enhancing the training effect. For example, the sound recognition unit measures the heart rate in real time using a heart rate sensor during sports or fitness and generates music according to the exercise intensity. For example, if the heart rate is high, fast-paced music is provided. The sound recognition unit also works with a fitness app to automatically generate music according to the type and intensity of exercise. For example, rhythmic music is provided while running, and relaxing music is provided while yoga is being performed. The sound recognition unit also analyzes heart rate data and walking sounds during exercise to generate music to enhance the training effect. For example, music that matches changes in intensity is provided during interval training. This allows the training effect to be enhanced by providing music according to the exercise intensity.

[0034] The mental state estimation unit can also collect the mental states of the user's friends or family and generate music that matches the atmosphere of the entire group. For example, the mental state estimation unit collects heart rate data of the user's friends and family and analyzes the mental state of the entire group. For example, if everyone is relaxed, it generates calm music. The mental state estimation unit also analyzes the content of group chats and estimates the overall atmosphere. For example, if there is a lot of fun conversation, it provides upbeat music. The mental state estimation unit also integrates emotional data of the user and their friends and family to generate music that matches the atmosphere of the entire group. For example, if everyone is tense, it provides relaxing music. This makes it possible to provide music that matches the atmosphere of the entire group.

[0035] The mental state estimation unit can analyze the user's past mental state data and generate music based on long-term changes in the mental state. The mental state estimation unit, for example, collects the user's past heart rate data over a long period of time and analyzes changes in the mental state. For example, it provides relaxing music during times of stress. The mental state estimation unit also predicts changes in the user's mental state based on past emotion data and generates music accordingly. For example, it provides calm music during times when stress is likely to build up. The mental state estimation unit also analyzes the user's past mental state data and understands long-term trends. For example, it generates music based on seasonal changes in the mental state. This makes it possible to provide music based on long-term changes in the mental state.

[0036] The information collecting unit can collect air quality and pollen information in addition to weather information and generate music based on this data. For example, the information collecting unit uses an air quality sensor to measure the concentration of pollutants and pollen in the air in addition to weather information and generates music based on that data. For example, when the air is clean, refreshing music is provided. The information collecting unit also collects pollen information and generates relaxing music during times of high pollen count. For example, calm music to alleviate hay fever symptoms is provided. The information collecting unit also analyzes air quality and pollen information in real time and generates music by combining it with the weather information. For example, relaxing music is provided when the air is polluted. This makes it possible to provide music based on weather information, air quality, and pollen information.

[0037] The information collecting unit can generate music that incorporates the culture or traditional music of a region based on the user's location information. The information collecting unit, for example, collects traditional music of a region based on the user's location information and incorporates it into the music to be generated. For example, if the user is in Japan, music using Japanese instruments is provided. The information collecting unit also generates music that takes into account the cultural background of a region based on the location information. For example, if the user is in Brazil, music incorporating samba rhythms is provided. The information collecting unit also analyzes the user's location information in real time and generates music that reflects the musical style of that region. For example, if the user is in Africa, music incorporating African beats is provided. In this way, music that incorporates the culture and traditional music of a region can be provided based on the user's location information.

[0038] The information collection unit can collect the user's moving speed or traffic conditions and generate music that suits the situation during movement. The information collection unit, for example, collects the user's moving speed from GPS data and generates music that suits the situation during movement. For example, if the moving speed is fast, fast-tempo music is provided. The information collection unit also collects traffic conditions in real time and generates music that suits traffic congestion and crowding. For example, relaxing music is provided when stuck in traffic. The information collection unit also analyzes the user's movement patterns and generates music that is optimal for the situation during movement. For example, energetic music is provided during the commute and relaxing music is provided when returning home. In this way, music that suits the situation during movement can be provided.

[0039] The information collection unit can collect information about places the user plans to visit in advance and generate music that suits the places. The information collection unit, for example, works in conjunction with the user's calendar app to collect information about places the user plans to visit in advance. For example, if a meeting is scheduled, music that helps concentration is provided. The information collection unit can also collect information about travel destinations in advance and generate music that suits the places. For example, if the user is going to a beach resort, music with a resort feel is provided. The information collection unit can also collect weather information and cultural background of the places the user plans to visit in advance and generate music based on that information. For example, relaxing music is provided on rainy days. This makes it possible to provide music that suits the places the user plans to visit.

[0040] The music generation unit can learn the user's favorite artists or genres and generate music based on that. For example, the music generation unit analyzes the user's music playback history and identifies the user's favorite artists and genres. For example, for a user who likes rock, the music generation unit generates rock-style music. The music generation unit also learns music that the user has given high ratings to in the past and generates music based on that tendency. For example, for a user who likes jazz, the music generation unit provides jazz-style music. The music generation unit also analyzes the user's musical preferences in real time and generates music that matches those preferences. For example, for a user who likes pop music, the music generation unit provides pop-style music. This makes it possible to provide music based on the user's favorite artists and genres.

[0041] The music generation unit can analyze the user's past music playback history and generate music based on playback frequency and ratings. The music generation unit, for example, analyzes the user's past music playback history and learns the characteristics of songs that are frequently played. For example, it generates music based on the tempo and rhythm of songs that are frequently played. The music generation unit also analyzes the characteristics of songs that the user has given high ratings and generates music based on those characteristics. For example, it provides music that incorporates the melody and harmony of highly rated songs. The music generation unit also analyzes the user's music playback history in real time and generates music based on playback frequency and ratings. For example, it provides music in a genre that is frequently played. This makes it possible to provide music based on the user's past music playback history.

[0042] The music generation unit can collect the musical preferences of the user's friends or family and generate music based on their shared preferences. For example, the music generation unit collects the music playback history of the user's friends and family and identifies their shared musical preferences. For example, it generates music in a genre that everyone likes. The music generation unit also analyzes the musical preferences of friends and family and generates music based on their shared characteristics. For example, it provides music that incorporates the characteristics of songs that everyone has given high ratings to. The music generation unit also analyzes the musical preferences of the user and their friends and family in real time and generates music that matches their shared preferences. For example, it provides music that everyone can enjoy. This makes it possible to provide music based on the shared preferences of the user's friends and family.

[0043] The music generation unit can generate music that matches the theme of the event or party the user will be attending. For example, the music generation unit collects themes of the events the user will be attending in advance and generates music that matches the theme. For example, for a Halloween party, music that matches the Halloween atmosphere is provided. The music generation unit also generates music of a specific genre or style based on the party theme. For example, for a beach party, music with a resort feel is provided. The music generation unit also analyzes the theme of the event or party in real time and generates music that matches the theme. For example, Christmas songs are provided for a Christmas party. In this way, music that matches the theme of the event or party can be provided.

[0044] The playback unit can adjust the sound quality of music based on the user's hearing characteristics. For example, the playback unit adjusts the sound quality of music to match the range and sensitivity of hearing based on the user's hearing test data. For example, if the user has difficulty hearing high-pitched sounds, the playback unit emphasizes high-pitched sounds. The playback unit also analyzes the user's hearing characteristics in real time to provide the user with optimal sound quality. For example, it adjusts the volume and balance. The playback unit also collects the user's hearing characteristic data and customizes the sound quality of music based on that data. For example, it emphasizes a specific frequency band. This makes it possible to adjust the sound quality of music based on the user's hearing characteristics.

[0045] The playback unit can automatically adjust the volume and effects of music according to the user's activity status. For example, the playback unit analyzes the user's activity status in real time and automatically adjusts the volume and effects of music. For example, the volume can be increased during exercise and decreased during relaxation. The playback unit also builds a system that adjusts music effects according to the activity status. For example, the bass can be emphasized during exercise and echo can be added during relaxation. The playback unit also customizes the volume and effects of music based on the user's activity data. For example, the tempo and rhythm of the music can be adjusted according to the intensity of exercise. This makes it possible to automatically adjust the volume and effects of music according to the user's activity status.

[0046] The playback unit can enhance empathy by sharing music with the user's family or friends and playing it simultaneously. The playback unit, for example, builds a system for sharing music with the user's family and friends and playing it simultaneously. For example, the playback unit shares the same playlist and synchronizes playback in real time. The playback unit also uses a music sharing function to enable the user and their family and friends to enjoy music simultaneously. For example, the playback unit provides an online music session. The playback unit also develops an interface for sharing music with the user's family and friends and enhancing empathy. For example, the playback unit displays real-time impressions and comments on the shared music. This allows the user's family and friends to share music and play it simultaneously and enhance empathy.

[0047] The playback unit can link music playback with locations the user visits and provide music that suits the location. The playback unit, for example, collects information about locations the user visits in advance and builds a system that provides music that suits the location. For example, relaxing music is provided in a cafe. The playback unit also links music playback in a gym or fitness center and provides music suitable for exercise. For example, music is provided according to the type of training. The playback unit also develops a system that automatically generates music that matches the atmosphere of the location the user visits and plays it at that location. For example, music that suits the meal is provided in a restaurant. This makes it possible to provide music that suits the location the user visits.

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

[0049] The sound recognition unit can also collect the heartbeat or walking sounds of the user's pet and generate music that can be enjoyed together with the pet. For example, a pet heartbeat sensor can be used to collect the pet's heartbeat data and music that can be enjoyed together with the pet can be generated based on that data. For example, when the pet is relaxed, calm music can be provided. The sound recognition unit can also develop a dedicated device to collect the pet's walking sounds and analyze the pet's walking pattern. This generates music that matches the pet's movements. The sound recognition unit can also simultaneously collect the heartbeats and walking sounds of the user and the pet and generate music based on both sets of data. For example, when walking together with the pet, music that matches a common rhythm can be provided. This makes it possible to provide music that can be enjoyed together with the pet.

[0050] The sound recognition unit generates music according to the exercise intensity during sports or fitness, thereby enhancing the training effect. For example, a heart rate sensor is used to measure the heart rate in real time during sports or fitness, and music is generated according to the exercise intensity. For example, if the heart rate is high, fast-paced music is provided. The sound recognition unit also works with a fitness app to automatically generate music according to the type and intensity of exercise. For example, rhythmic music is provided while running, and relaxing music is provided during yoga. The sound recognition unit also analyzes heart rate data and walking sounds during exercise to generate music to enhance the training effect. For example, music that matches changes in intensity is provided during interval training. This allows the training effect to be enhanced by providing music according to the exercise intensity.

[0051] The information collection unit can collect air quality and pollen information in addition to weather information and generate music based on this data. For example, in addition to weather information, an air quality sensor is used to measure the concentration of pollutants and pollen in the air and generate music based on that data. For example, when the air is clean, refreshing music is provided. The information collection unit also collects pollen information and generates relaxing music during times of high pollen count. For example, calm music to alleviate hay fever symptoms is provided. The information collection unit also analyzes air quality and pollen information in real time and generates music by combining it with weather information. For example, relaxing music is provided when the air is polluted. This makes it possible to provide music based on weather information, air quality, and pollen information.

[0052] The information collecting unit can generate music that incorporates the culture or traditional music of a region based on the user's location information. For example, traditional music of a region is collected based on the user's location information and incorporated into the music to be generated. For example, if the user is in Japan, music using Japanese instruments is provided. The information collecting unit also generates music that takes into account the cultural background of a region based on the location information. For example, if the user is in Brazil, music incorporating samba rhythms is provided. The information collecting unit also analyzes the user's location information in real time to generate music that reflects the musical style of that region. For example, if the user is in Africa, music incorporating African beats is provided. In this way, music that incorporates the culture and traditional music of a region can be provided based on the user's location information.

[0053] The information collection unit can collect the user's moving speed or traffic conditions and generate music that suits the situation during movement. For example, the user's moving speed can be collected from GPS data and music that suits the situation during movement can be generated. For example, if the moving speed is fast, fast-paced music can be provided. The information collection unit can also collect traffic conditions in real time and generate music that suits traffic congestion or crowding. For example, relaxing music can be provided when stuck in a traffic jam. The information collection unit can also analyze the user's movement patterns and generate music that is optimal for the situation during movement. For example, energetic music can be provided during the commute and relaxing music can be provided when returning home. This makes it possible to provide music that suits the situation during movement.

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

[0055] Step 1: The sound recognition unit recognizes sounds from the user and the surrounding environment. For example, heart rate is measured using a heart rate sensor or a smartwatch, and walking sounds are collected using a smartphone microphone or a dedicated device. The sound recognition unit can also collect breathing sounds and body temperature data. Step 2: The mental state estimation unit estimates the user's mental state based on the sounds recognized by the sound recognition unit. For example, if the heart rate is high, it is determined that the user is feeling tense or stressed, and if the heart rate is low, it is determined that the user is relaxed. Step 3: The information collection unit collects meteorological information and location information. For example, it obtains meteorological information such as the current weather, temperature, and humidity, and determines the location information of the user's location. Step 4: The music generation unit generates music based on the information obtained by the mental state estimation unit and the information collection unit. For example, if the heart rate is high and the walking sound is fast, fast-paced rhythmic music is generated. Step 5: The playback unit plays the music generated by the music generation unit, for example, through a smartphone or a speaker.

[0056] (Example 2) A music generation system according to an embodiment of the present invention recognizes sounds and weather information obtained from the user and the surrounding environment, and automatically generates and plays music with a similar tempo and atmosphere based on the recognized sounds and weather information. This allows the music generation system to generate and play appropriate music based on the user's mental state and environmental information.

[0057] A music generation system according to an embodiment includes a sound recognition unit, a mental state estimation unit, an information collection unit, a music generation unit, and a playback unit. The sound recognition unit recognizes sounds obtained from the user and the surrounding environment. For example, the heart rate is measured using a heart rate sensor or a smartwatch, and walking sounds are collected using a smartphone microphone or a dedicated device. The sound recognition unit can also collect breathing sounds and body temperature data. The mental state estimation unit estimates the user's mental state based on the sounds recognized by the sound recognition unit. For example, a high heart rate is determined to indicate tension or stress, and a low heart rate is determined to indicate relaxation. The information collection unit collects meteorological information and location information. For example, meteorological information such as the current weather, temperature, and humidity is acquired to determine the user's location. The music generation unit generates music based on the information obtained by the mental state estimation unit and the information collection unit. For example, if the heart rate is high and walking sounds are fast, fast-tempo rhythmic music is generated. The playback unit plays the music generated by the music generation unit. For example, the music is played through a smartphone or speaker. This allows the music generation system to generate and play appropriate music based on the user's mental state and environmental information.

[0058] The sound recognition unit collects breathing sounds and body temperature data in addition to heartbeat or walking sounds, allowing it to generate music based on more detailed biometric information. For example, the sound recognition unit uses a heartbeat sensor or a smartwatch, as well as a microphone for collecting breathing sounds, to analyze the rhythm and depth of breathing. This allows it to generate music that takes into account not only heartbeat and walking sounds, but also breathing patterns. The sound recognition unit also uses a body temperature sensor to collect the user's body temperature data and adjusts the tempo and rhythm of the music based on fluctuations in body temperature. For example, if the body temperature is rising, it generates energetic music. The sound recognition unit also analyzes breathing sounds and body temperature data in real time and combines them with heartbeat and walking sounds to generate music based on more accurate biometric information. This allows it to generate music that is optimal for the user's condition based on more detailed biometric information.

[0059] The sound recognition unit can learn the user's past heart rate data or walking patterns and develop a music generation algorithm optimized for each individual user. For example, the sound recognition unit collects the user's past heart rate data and analyzes long-term heart rate fluctuation patterns. This allows the development of a music generation algorithm optimized for each individual user. The sound recognition unit also collects walking sound data over a long period of time and learns the user's walking pattern. For example, it analyzes fluctuations in walking speed and rhythm and generates music based on that. The sound recognition unit also customizes the music generation algorithm according to the user's condition based on the past heart rate data and walking patterns. For example, it provides music tailored to a specific time period or activity. This allows the provision of music optimized for each individual user.

[0060] The sound recognition unit can use an emotion estimation function to estimate an emotion from the user's tone of voice or speaking style, and generate music based on that emotion. The sound recognition unit, for example, uses a microphone to collect the user's voice and analyzes the tone of voice and speaking style. For example, the sound recognition unit estimates an emotion based on the pitch and speed of the voice, and generates music that matches that emotion. The sound recognition unit also uses voice recognition technology to estimate an emotion from the way the user speaks. For example, if the user speaks quickly, it determines that the user is nervous and generates relaxing music. The sound recognition unit also analyzes the user's tone of voice and speaking style in real time, and generates music based on the emotion estimation data. For example, if the voice is calm, relaxing music is provided. In this way, music can be generated based on the user's emotion.

[0061] The sound recognition unit can also collect the heartbeat or walking sounds of the user's pet and generate music that can be enjoyed together with the pet. The sound recognition unit, for example, uses a pet heartbeat sensor to collect the pet's heartbeat data and generates music that can be enjoyed together with the pet based on that data. For example, when the pet is relaxed, it provides calm music. The sound recognition unit has also developed a dedicated device for collecting the pet's walking sounds and analyzes the pet's walking pattern. This generates music that matches the pet's movements. The sound recognition unit can also simultaneously collect the heartbeats and walking sounds of the user and the pet and generate music based on both data. For example, when walking together with the pet, it provides music that matches a common rhythm. This makes it possible to provide music that can be enjoyed together with the pet.

[0062] The sound recognition unit generates music according to the exercise intensity during sports or fitness, thereby enhancing the training effect. For example, the sound recognition unit measures the heart rate in real time using a heart rate sensor during sports or fitness and generates music according to the exercise intensity. For example, if the heart rate is high, fast-paced music is provided. The sound recognition unit also works with a fitness app to automatically generate music according to the type and intensity of exercise. For example, rhythmic music is provided while running, and relaxing music is provided while yoga is being performed. The sound recognition unit also analyzes heart rate data and walking sounds during exercise to generate music to enhance the training effect. For example, music that matches changes in intensity is provided during interval training. This allows the training effect to be enhanced by providing music according to the exercise intensity.

[0063] The sound recognition unit can use the emotion estimation function to analyze the user's emotional response to the music they are listening to in real time and adjust the music to be played next. For example, the sound recognition unit collects the user's emotional response to the music they are listening to in real time and adjusts the music to be played next based on that data. For example, if there are many positive responses, it continues to play music with the same atmosphere. The sound recognition unit also uses the emotion estimation function to analyze the user's facial expressions and tone of voice and evaluates the emotional response to the music. For example, if there are many smiles, it provides cheerful music. The sound recognition unit also builds a system that automatically selects the next music to be played based on the user's emotional response data. For example, it prioritizes playing music with a high emotion score. This makes it possible to adjust the next music to be played based on the user's emotional response.

[0064] The mental state estimation unit can analyze the user's facial expression using a camera, estimate emotions from the facial expression, and generate music. The mental state estimation unit, for example, uses a camera to analyze the user's facial expression in real time and estimate emotions from the facial expression. For example, if the user smiles a lot, it generates cheerful music. The mental state estimation unit also uses facial expression recognition technology to analyze the user's facial movements and estimate emotions. For example, if the user's brow is furrowed, it provides relaxing music. The mental state estimation unit also collects the user's facial expression data and generates music using an emotion estimation algorithm. For example, it determines emotions based on eye movements and mouth shape, and provides music that matches those emotions. In this way, music can be generated based on the user's facial expressions.

[0065] The mental state estimation unit can analyze the content of a user's SNS posts or messages, infer emotions from the text, and generate music. For example, the mental state estimation unit analyzes the user's SNS posts and infers emotions from the text. For example, if there are many positive posts, it generates cheerful music. The mental state estimation unit also analyzes the content of a messaging app and infers the user's emotions. For example, if a conversation with a friend is enjoyable, it provides rhythmic music. The mental state estimation unit also uses text analysis technology to extract emotions from the user's posts and messages and generates music based on those emotions. For example, if there are many negative emotions, it provides relaxing music. In this way, music can be generated based on the content of a user's SNS posts and messages.

[0066] The mental state estimation unit can use the emotion estimation function to analyze the content of the user's dream and generate music that matches the atmosphere of the dream. For example, the mental state estimation unit records the content of the user's dream and analyzes the atmosphere of the dream using the emotion estimation function. For example, if the dream was pleasant, cheerful music is generated. The mental state estimation unit also works in conjunction with a dream diary app to analyze the content of the dream recorded by the user. For example, music is generated based on the emotions felt in the dream. The mental state estimation unit also performs text analysis of the content of the user's dream and generates music using an emotion estimation algorithm. For example, appropriate music is provided based on the events and emotions felt in the dream. This makes it possible to generate music based on the content of the user's dream.

[0067] The mental state estimation unit can also collect the mental states of the user's friends or family and generate music that matches the atmosphere of the entire group. For example, the mental state estimation unit collects heart rate data of the user's friends and family and analyzes the mental state of the entire group. For example, if everyone is relaxed, it generates calm music. The mental state estimation unit also analyzes the content of group chats and estimates the overall atmosphere. For example, if there is a lot of fun conversation, it provides upbeat music. The mental state estimation unit also integrates emotional data of the user and their friends and family to generate music that matches the atmosphere of the entire group. For example, if everyone is tense, it provides relaxing music. This makes it possible to provide music that matches the atmosphere of the entire group.

[0068] The mental state estimation unit can analyze the user's past mental state data and generate music based on long-term changes in the mental state. The mental state estimation unit, for example, collects the user's past heart rate data over a long period of time and analyzes changes in the mental state. For example, it provides relaxing music during times of stress. The mental state estimation unit also predicts changes in the user's mental state based on past emotion data and generates music accordingly. For example, it provides calm music during times when stress is likely to build up. The mental state estimation unit also analyzes the user's past mental state data and understands long-term trends. For example, it generates music based on seasonal changes in the mental state. This makes it possible to provide music based on long-term changes in the mental state.

[0069] The mental state estimation unit can use the emotion estimation function to generate music that matches emotional scenes in video content that the user is watching. The mental state estimation unit, for example, analyzes the video content that the user is watching and generates music that matches emotional scenes. For example, it provides emotional music for emotional scenes. The mental state estimation unit also analyzes audio data of the video content and identifies emotional scenes. For example, it provides music that increases tension for tense scenes. The mental state estimation unit also analyzes the user's emotional response in real time and generates music that matches the video content that the user is watching. For example, it provides fun music for funny scenes. In this way, it is possible to provide music that matches emotional scenes in the video content.

[0070] The information collecting unit can collect air quality and pollen information in addition to weather information and generate music based on this data. For example, the information collecting unit uses an air quality sensor to measure the concentration of pollutants and pollen in the air in addition to weather information and generates music based on that data. For example, when the air is clean, refreshing music is provided. The information collecting unit also collects pollen information and generates relaxing music during times of high pollen count. For example, calm music to alleviate hay fever symptoms is provided. The information collecting unit also analyzes air quality and pollen information in real time and generates music by combining it with the weather information. For example, relaxing music is provided when the air is polluted. This makes it possible to provide music based on weather information, air quality, and pollen information.

[0071] The information collecting unit can generate music that incorporates the culture or traditional music of a region based on the user's location information. The information collecting unit, for example, collects traditional music of a region based on the user's location information and incorporates it into the music to be generated. For example, if the user is in Japan, music using Japanese instruments is provided. The information collecting unit also generates music that takes into account the cultural background of a region based on the location information. For example, if the user is in Brazil, music incorporating samba rhythms is provided. The information collecting unit also analyzes the user's location information in real time and generates music that reflects the musical style of that region. For example, if the user is in Africa, music incorporating African beats is provided. In this way, music that incorporates the culture and traditional music of a region can be provided based on the user's location information.

[0072] The information collection unit can use the emotion estimation function to generate music based on the user's memories of places they have visited in the past. For example, the information collection unit collects location information of places the user has visited in the past and generates music based on the user's memories of those places. For example, the information collection unit analyzes photos and videos taken by the user at travel destinations and provides music that matches the atmosphere. The information collection unit also estimates the user's emotions at places they have visited based on past location information data and generates music based on those emotions. For example, cheerful music is provided for places with many happy memories. The information collection unit also integrates the user's past location information and emotion data to generate music based on memories. For example, music that matches a specific place is provided based on the emotion score for that place. This makes it possible to provide music based on the user's memories of places they have visited in the past.

[0073] The information collection unit can collect the user's moving speed or traffic conditions and generate music that suits the situation during movement. The information collection unit, for example, collects the user's moving speed from GPS data and generates music that suits the situation during movement. For example, if the moving speed is fast, fast-tempo music is provided. The information collection unit also collects traffic conditions in real time and generates music that suits traffic congestion and crowding. For example, relaxing music is provided when stuck in traffic. The information collection unit also analyzes the user's movement patterns and generates music that is optimal for the situation during movement. For example, energetic music is provided during the commute and relaxing music is provided when returning home. In this way, music that suits the situation during movement can be provided.

[0074] The information collection unit can collect information about places the user plans to visit in advance and generate music that suits the places. The information collection unit, for example, works in conjunction with the user's calendar app to collect information about places the user plans to visit in advance. For example, if a meeting is scheduled, music that helps concentration is provided. The information collection unit can also collect information about travel destinations in advance and generate music that suits the places. For example, if the user is going to a beach resort, music with a resort feel is provided. The information collection unit can also collect weather information and cultural background of the places the user plans to visit in advance and generate music based on that information. For example, relaxing music is provided on rainy days. This makes it possible to provide music that suits the places the user plans to visit.

[0075] The information collection unit can use the emotion estimation function to analyze the user's emotional reactions to places they have visited in the past and generate music based on those emotions. For example, the information collection unit records the user's emotional reactions to places they have visited in the past and generates music based on that data. For example, cheerful music is provided for places that have many happy memories. The information collection unit also integrates past location information and emotion data to analyze the user's emotional reactions to places they have visited. For example, music that suits a particular place is provided based on the emotion score for that place. The information collection unit also generates music that suits the user's memories of places they have visited based on the user's past emotional reaction data. For example, moving music is provided for places that have many moving memories. In this way, music can be provided based on the user's emotional reactions to places they have visited in the past.

[0076] The music generation unit can learn the user's favorite artists or genres and generate music based on that. For example, the music generation unit analyzes the user's music playback history and identifies the user's favorite artists and genres. For example, for a user who likes rock, the music generation unit generates rock-style music. The music generation unit also learns music that the user has given high ratings to in the past and generates music based on that tendency. For example, for a user who likes jazz, the music generation unit provides jazz-style music. The music generation unit also analyzes the user's musical preferences in real time and generates music that matches those preferences. For example, for a user who likes pop music, the music generation unit provides pop-style music. This makes it possible to provide music based on the user's favorite artists and genres.

[0077] The music generation unit can analyze the user's past music playback history and generate music based on playback frequency and ratings. The music generation unit, for example, analyzes the user's past music playback history and learns the characteristics of songs that are frequently played. For example, it generates music based on the tempo and rhythm of songs that are frequently played. The music generation unit also analyzes the characteristics of songs that the user has given high ratings and generates music based on those characteristics. For example, it provides music that incorporates the melody and harmony of highly rated songs. The music generation unit also analyzes the user's music playback history in real time and generates music based on playback frequency and ratings. For example, it provides music in a genre that is frequently played. This makes it possible to provide music based on the user's past music playback history.

[0078] The music generation unit can use the emotion estimation function to analyze the user's emotional response to music that the user has listened to in the past and generate music based on the emotion. For example, the music generation unit records the user's emotional response to music that the user has listened to in the past and generates music based on that data. For example, music that incorporates characteristics of songs that often evoke positive emotional responses is provided. The music generation unit also uses the emotion estimation function to analyze the user's emotional response in real time while music is being played. For example, music is generated based on characteristics of songs with high emotional scores. The music generation unit also generates music that matches the user's emotions based on the user's past emotional response data. For example, to provide relaxing music, the music generation unit incorporates characteristics of songs that often evoke relaxing emotional responses. This makes it possible to provide music based on the user's emotional responses to music that the user has listened to in the past.

[0079] The music generation unit can collect the musical preferences of the user's friends or family and generate music based on their shared preferences. For example, the music generation unit collects the music playback history of the user's friends and family and identifies their shared musical preferences. For example, it generates music in a genre that everyone likes. The music generation unit also analyzes the musical preferences of friends and family and generates music based on their shared characteristics. For example, it provides music that incorporates the characteristics of songs that everyone has given high ratings to. The music generation unit also analyzes the musical preferences of the user and their friends and family in real time and generates music that matches their shared preferences. For example, it provides music that everyone can enjoy. This makes it possible to provide music based on the shared preferences of the user's friends and family.

[0080] The music generation unit can generate music that matches the theme of the event or party the user will be attending. For example, the music generation unit collects themes of the events the user will be attending in advance and generates music that matches the theme. For example, for a Halloween party, music that matches the Halloween atmosphere is provided. The music generation unit also generates music of a specific genre or style based on the party theme. For example, for a beach party, music with a resort feel is provided. The music generation unit also analyzes the theme of the event or party in real time and generates music that matches the theme. For example, Christmas songs are provided for a Christmas party. In this way, music that matches the theme of the event or party can be provided.

[0081] The music generation unit can use the emotion estimation function to generate music that matches emotional scenes in the video content the user is watching. The music generation unit, for example, analyzes the video content the user is watching and generates music that matches emotional scenes. For example, it provides emotional music for an emotional scene. The music generation unit also analyzes audio data of the video content to identify emotional scenes. For example, it provides music that increases the tension for a tense scene. The music generation unit also analyzes the user's emotional response in real time and generates music that matches the video content being watched. For example, it provides cheerful music for a funny scene. In this way, it is possible to provide music that matches emotional scenes in the video content.

[0082] The playback unit can adjust the sound quality of music based on the user's hearing characteristics. For example, the playback unit adjusts the sound quality of music to match the range and sensitivity of hearing based on the user's hearing test data. For example, if the user has difficulty hearing high-pitched sounds, the playback unit emphasizes high-pitched sounds. The playback unit also analyzes the user's hearing characteristics in real time to provide the user with optimal sound quality. For example, it adjusts the volume and balance. The playback unit also collects the user's hearing characteristic data and customizes the sound quality of music based on that data. For example, it emphasizes a specific frequency band. This makes it possible to adjust the sound quality of music based on the user's hearing characteristics.

[0083] The playback unit can automatically adjust the volume and effects of music according to the user's activity status. For example, the playback unit analyzes the user's activity status in real time and automatically adjusts the volume and effects of music. For example, the volume can be increased during exercise and decreased during relaxation. The playback unit also builds a system that adjusts music effects according to the activity status. For example, the bass can be emphasized during exercise and echo can be added during relaxation. The playback unit also customizes the volume and effects of music based on the user's activity data. For example, the tempo and rhythm of the music can be adjusted according to the intensity of exercise. This makes it possible to automatically adjust the volume and effects of music according to the user's activity status.

[0084] The playback unit can use the emotion estimation function to adjust the playback order and content of music based on the user's real-time emotional response. The playback unit, for example, analyzes the user's emotional response in real time and adjusts the playback order of music based on the data. For example, songs with a high number of positive emotional responses are preferentially played. The playback unit also uses the emotion estimation function to adjust the content of music based on the user's emotions. For example, when the user wants to relax, calm music is provided. The playback unit also builds a system that dynamically adjusts the playback order and content of music based on the user's emotional response data. For example, songs with high emotion scores are played consecutively. This makes it possible to adjust the playback order and content of music based on the user's real-time emotional response.

[0085] The playback unit can enhance empathy by sharing music with the user's family or friends and playing it simultaneously. The playback unit, for example, builds a system for sharing music with the user's family and friends and playing it simultaneously. For example, the playback unit shares the same playlist and synchronizes playback in real time. The playback unit also uses a music sharing function to enable the user and their family and friends to enjoy music simultaneously. For example, the playback unit provides an online music session. The playback unit also develops an interface for sharing music with the user's family and friends and enhancing empathy. For example, the playback unit displays real-time impressions and comments on the shared music. This allows the user's family and friends to share music and play it simultaneously and enhance empathy.

[0086] The playback unit can link music playback with locations the user visits and provide music that suits the location. The playback unit, for example, collects information about locations the user visits in advance and builds a system that provides music that suits the location. For example, relaxing music is provided in a cafe. The playback unit also links music playback in a gym or fitness center and provides music suitable for exercise. For example, music is provided according to the type of training. The playback unit also develops a system that automatically generates music that matches the atmosphere of the location the user visits and plays it at that location. For example, music that suits the meal is provided in a restaurant. This makes it possible to provide music that suits the location the user visits.

[0087] The playback unit can use the emotion estimation function to play music that matches emotional scenes in the video content the user is watching. The playback unit, for example, analyzes the video content the user is watching and plays music that matches emotional scenes. For example, it provides emotional music for emotional scenes. The playback unit also analyzes audio data of the video content to identify emotional scenes. For example, it provides music that increases the tension for tense scenes. The playback unit also analyzes the user's emotional response in real time and plays music that matches the video content being watched. For example, it provides fun music for funny scenes. In this way, music can be played that matches emotional scenes in the video content the user is watching.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The sound recognition unit can analyze the user's tone of voice and speaking style to estimate their emotions. For example, it can estimate emotions based on the pitch and speed of the voice and generate music that matches that emotion. The sound recognition unit also uses voice recognition technology to estimate emotions from the way the user speaks. For example, if the user speaks quickly, it can determine that the user is nervous and generate relaxing music. The sound recognition unit also analyzes the user's tone of voice and speaking style in real time and generates music based on the emotion estimation data. For example, if the user's voice is calm, it can provide relaxing music. This makes it possible to generate music based on the user's emotions.

[0090] The sound recognition unit can also collect the heartbeat or walking sounds of the user's pet and generate music that can be enjoyed together with the pet. For example, a pet heartbeat sensor can be used to collect the pet's heartbeat data and music that can be enjoyed together with the pet can be generated based on that data. For example, when the pet is relaxed, calm music can be provided. The sound recognition unit can also develop a dedicated device to collect the pet's walking sounds and analyze the pet's walking pattern. This generates music that matches the pet's movements. The sound recognition unit can also simultaneously collect the heartbeats and walking sounds of the user and the pet and generate music based on both sets of data. For example, when walking together with the pet, music that matches a common rhythm can be provided. This makes it possible to provide music that can be enjoyed together with the pet.

[0091] The sound recognition unit generates music according to the exercise intensity during sports or fitness, thereby enhancing the training effect. For example, a heart rate sensor is used to measure the heart rate in real time during sports or fitness, and music is generated according to the exercise intensity. For example, if the heart rate is high, fast-paced music is provided. The sound recognition unit also works with a fitness app to automatically generate music according to the type and intensity of exercise. For example, rhythmic music is provided while running, and relaxing music is provided during yoga. The sound recognition unit also analyzes heart rate data and walking sounds during exercise to generate music to enhance the training effect. For example, music that matches changes in intensity is provided during interval training. This allows the training effect to be enhanced by providing music according to the exercise intensity.

[0092] The sound recognition unit can use the emotion estimation function to analyze the user's emotional response to the music they are listening to in real time and adjust the music to be played next. For example, the sound recognition unit can collect the user's emotional response to the music they are listening to in real time and adjust the music to be played next based on that data. For example, if there are many positive responses, it can continue to play music with the same atmosphere. The sound recognition unit can also use the emotion estimation function to analyze the user's facial expressions and tone of voice to evaluate their emotional response to the music. For example, if there are many smiles, it can provide cheerful music. The sound recognition unit can also build a system that automatically selects the next music to be played based on the user's emotional response data. For example, it can prioritize playing music with a high emotion score. This makes it possible to adjust the next music to be played based on the user's emotional response.

[0093] The mental state estimation unit can analyze the user's facial expression using a camera, estimate emotions from the facial expression, and generate music. For example, the camera can be used to analyze the user's facial expression in real time and estimate emotions from the facial expression. For example, if the user smiles a lot, cheerful music can be generated. The mental state estimation unit can also analyze the user's facial movements using facial expression recognition technology to estimate emotions. For example, if the user has wrinkles between the eyebrows, relaxing music can be provided. The mental state estimation unit can also collect the user's facial expression data and generate music using an emotion estimation algorithm. For example, emotions can be determined based on eye movements and mouth shape, and music that matches those emotions can be provided. This makes it possible to generate music based on the user's facial expression.

[0094] The mental state estimation unit can analyze the content of a user's SNS posts or messages, infer emotions from the text, and generate music. For example, the unit analyzes the user's SNS posts and infers emotions from the text. For example, if there are many positive posts, it generates upbeat music. The mental state estimation unit also analyzes the content of a messaging app and infers the user's emotions. For example, if a conversation with a friend is enjoyable, it provides rhythmic music. The mental state estimation unit also uses text analysis technology to extract emotions from the user's posts and messages and generates music based on those emotions. For example, if there are many negative emotions, it provides relaxing music. In this way, music can be generated based on the content of a user's SNS posts and messages.

[0095] The mental state estimation unit can use the emotion estimation function to analyze the content of a user's dream and generate music that matches the atmosphere of the dream. For example, the content of the user's dream is recorded and the emotion estimation function is used to analyze the atmosphere of the dream. For example, if the dream was pleasant, cheerful music is generated. The mental state estimation unit also works in conjunction with a dream diary app to analyze the content of the dream recorded by the user. For example, music is generated based on the emotions felt in the dream. The mental state estimation unit also performs text analysis of the content of the user's dream and generates music using an emotion estimation algorithm. For example, appropriate music is provided based on the events and emotions felt in the dream. This makes it possible to generate music based on the content of the user's dream.

[0096] The information collection unit can collect air quality and pollen information in addition to weather information and generate music based on this data. For example, in addition to weather information, an air quality sensor is used to measure the concentration of pollutants and pollen in the air and generate music based on that data. For example, when the air is clean, refreshing music is provided. The information collection unit also collects pollen information and generates relaxing music during times of high pollen count. For example, calm music to alleviate hay fever symptoms is provided. The information collection unit also analyzes air quality and pollen information in real time and generates music by combining it with weather information. For example, relaxing music is provided when the air is polluted. This makes it possible to provide music based on weather information, air quality, and pollen information.

[0097] The information collecting unit can generate music that incorporates the culture or traditional music of a region based on the user's location information. For example, traditional music of a region is collected based on the user's location information and incorporated into the music to be generated. For example, if the user is in Japan, music using Japanese instruments is provided. The information collecting unit also generates music that takes into account the cultural background of a region based on the location information. For example, if the user is in Brazil, music incorporating samba rhythms is provided. The information collecting unit also analyzes the user's location information in real time to generate music that reflects the musical style of that region. For example, if the user is in Africa, music incorporating African beats is provided. In this way, music that incorporates the culture and traditional music of a region can be provided based on the user's location information.

[0098] The information collection unit can collect the user's moving speed or traffic conditions and generate music that suits the situation during movement. For example, the user's moving speed can be collected from GPS data and music that suits the situation during movement can be generated. For example, if the moving speed is fast, fast-paced music can be provided. The information collection unit can also collect traffic conditions in real time and generate music that suits traffic congestion or crowding. For example, relaxing music can be provided when stuck in a traffic jam. The information collection unit can also analyze the user's movement patterns and generate music that is optimal for the situation during movement. For example, energetic music can be provided during the commute and relaxing music can be provided when returning home. This makes it possible to provide music that suits the situation during movement.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The sound recognition unit recognizes sounds from the user and the surrounding environment. For example, heart rate is measured using a heart rate sensor or a smartwatch, and walking sounds are collected using a smartphone microphone or a dedicated device. The sound recognition unit can also collect breathing sounds and body temperature data. Step 2: The mental state estimation unit estimates the user's mental state based on the sounds recognized by the sound recognition unit. For example, if the heart rate is high, it is determined that the user is feeling tense or stressed, and if the heart rate is low, it is determined that the user is relaxed. Step 3: The information collection unit collects meteorological information and location information. For example, it obtains meteorological information such as the current weather, temperature, and humidity, and determines the location information of the user's location. Step 4: The music generation unit generates music based on the information obtained by the mental state estimation unit and the information collection unit. For example, if the heart rate is high and the walking sound is fast, fast-paced rhythmic music is generated. Step 5: The playback unit plays the music generated by the music generation unit, for example, through a smartphone or a speaker.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] 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.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] 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.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] 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.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0142] 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.

[0143] 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.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0152] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0153] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0154] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0157] 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.

[0158] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0159] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0160] 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.

[0161] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0162] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0163] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0165] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0166] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0167] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a sound recognition unit that recognizes sounds obtained from the user or the surrounding environment; a mental state estimation unit that estimates a mental state of a user based on the sound recognized by the sound recognition unit; an information collection unit that collects weather information and location information; a music generation unit that generates music based on the information obtained by the mental state estimation unit and the information collection unit; a playback unit that plays the music generated by the music generation unit. A system characterized by:

2. The sound recognition unit In addition to heartbeat or walking sounds, breathing and body temperature data will also be collected to generate music based on more detailed biometric information.

2. The system of claim 1.

3. The sound recognition unit Developing a music generation algorithm optimized for each individual user by learning from the user's past heart rate data or walking patterns 2. The system of claim 1.

4. The sound recognition unit Inferring emotions from the user's tone of voice or speaking style and generating music based on those emotions 2. The system of claim 1.

5. The sound recognition unit It also collects the heartbeat or walking sounds of the user's pet and generates music that can be enjoyed together with the pet.

2. The system of claim 1.

6. The sound recognition unit Generates music according to exercise intensity during sports or fitness to enhance training effects 2. The system of claim 1.

7. The sound recognition unit Analyzes the user's emotional response to the music they're listening to in real time and adjusts the music they play next 2. The system of claim 1.

8. The mental state estimation unit The system analyzes the user's facial expressions with a camera, estimates emotions from the facial expressions, and generates music based on the emotions.

2. The system of claim 1.

Images