system

Abstract

An object of the system according to the embodiment is to generate BGM in real time based on a natural sound.SOLUTION: A system includes a natural sound collection unit, an analysis unit, and a generation unit. The natural sound collection unit collects a natural sound. The analysis unit analyzes the natural sound collected by the natural sound collection unit. The generation unit generates BGM on the basis of the natural sound analyzed by the analysis 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 rarely generate background music based on natural sounds in real time, leaving room for improving the user experience.

[0005] The system according to the embodiment aims to generate background music in real time based on natural sounds. [Means for solving the problem]

[0006] The system according to the embodiment includes a natural sound collection unit, an analysis unit, and a generation unit. The natural sound collection unit collects natural sounds. The analysis unit analyzes the natural sounds collected by the natural sound collection unit. The generation unit generates background music based on the natural sounds analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate background music in real time based on natural sounds. [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 pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example 1) The nature sound real-time background music maker according to an embodiment of the present invention is a system that collects nature sounds, analyzes them with a generation AI, and generates background music that matches the nature sounds in real time. This allows the nature sound real-time background music maker to provide users with a unique musical experience.

[0029] A natural sound real-time background music maker according to an embodiment includes a natural sound collection unit, an analysis unit, and a generation unit. The natural sound collection unit collects natural sounds. For example, the natural sound collection unit uses a microphone to collect natural sounds such as rain, wind, and waves. The natural sound collection unit can also connect to a user's smart device and collect sounds from multiple devices. For example, a wide range of natural sounds can be collected using a smart watch or smart speaker. The analysis unit analyzes the natural sounds collected by the natural sound collection unit. For example, the analysis unit performs frequency analysis of the sounds to extract characteristics of the natural sounds. The analysis unit can also analyze the rhythm and tempo of the natural sounds and provide data for generating background music based on the analysis. For example, the analysis is performed in accordance with the rhythm of the rain. The generation unit generates background music based on the natural sounds analyzed by the analysis unit. For example, the generation unit generates a quiet piano melody that harmonizes with the rain. The generation unit can also generate a flute melody that harmonizes with the wind. For example, the generation unit generates a flute melody that harmonizes with the wind. This allows the nature sound real-time background music maker according to the embodiment to generate background music in real time based on nature sounds. For example, users can enjoy music while feeling like they are in nature. This is particularly useful when studying, concentrating, meditating, or relaxing.

[0030] The generation unit can generate a quiet piano melody that harmonizes with the sound of rain. For example, the generation AI generates a piano melody that matches the rhythm of the sound of rain, harmonizing it with the sound of rain. The generation unit can also analyze the frequency components of the sound of rain and generate a piano tone that matches a specific frequency band. For example, it can generate a bass sound that matches the low-frequency components of the sound of rain, harmonizing it with the sound of rain. In this way, background music that harmonizes with the sound of rain is generated, providing a relaxing effect to the user.

[0031] The generation unit can generate a flute timbre that harmonizes with the wind sound. For example, the generation AI generates a flute melody that harmonizes with the wind sound. For example, the generation AI generates a flute melody that matches the rhythm of the wind sound and harmonizes with the wind sound. The generation unit can also analyze the frequency components of the wind sound and generate a flute timbre that matches a specific frequency band. For example, the generation unit generates a flute timbre that matches the high-frequency components of the wind sound and harmonizes with the wind sound. In this way, background music that harmonizes with the wind sound is generated, providing a relaxing effect to the user.

[0032] The generation unit can generate a light melody that harmonizes with the chirping of birds. For example, the generation AI generates a melody that matches the rhythm of the birdsong, harmonizing it with the birdsong. The generation unit can also analyze the frequency components of the birdsong and generate a melody that matches a specific frequency band. For example, a light melody can be generated that matches the high-frequency components of the birdsong, harmonizing it with the birdsong. In this way, background music that harmonizes with the birdsong is generated, providing a relaxing effect to the user.

[0033] The natural sound collection unit can use multiple microphones to identify the direction and distance of a sound and analyze it as stereophonic sound. The natural sound collection unit, for example, uses multiple microphones to identify the direction and distance of a sound and analyzes it as stereophonic sound. For example, the natural sound collection unit identifies the direction from which rain is falling and recreates a stereophonic sound field based on that information. The natural sound collection unit can also identify the direction of a sound using the difference in sound arrival time. For example, multiple microphones can be placed and the difference in sound arrival time can be measured to identify the direction of the sound. This allows for analysis as stereophonic sound to recreate a more realistic sound field.

[0034] The analysis unit learns patterns of natural sounds and can predict and analyze specific natural sounds before they occur. For example, the generation AI of the analysis unit learns past natural sound data and recognizes specific patterns. For example, the generation AI learns the sound of wind and changes in humidity before rain starts to fall, and predicts and analyzes the sound of rain before it occurs. The analysis unit can also learn patterns of natural sounds and predict and analyze specific natural sounds before they occur. For example, the generation AI learns patterns of wind sounds and predicts and analyzes the sound of wind before it gets stronger. This allows for more accurate analysis by learning and predicting patterns of natural sounds.

[0035] The natural sound collection unit can link with the user's smart devices and integrate and analyze sounds from multiple devices. The natural sound collection unit, for example, links with the user's smartwatch and smart speaker and integrates and analyzes natural sounds collected from multiple devices. For example, the microphone of the smartwatch and the microphone of the smart speaker are used to collect a wider range of natural sounds. The natural sound collection unit can also integrate and analyze sounds from multiple devices. For example, the microphones of a smartphone and a tablet are used to collect natural sounds and the data is integrated and analyzed. In this way, by integrating and analyzing sounds from multiple devices, a wider range of natural sounds can be collected and the analysis accuracy can be improved.

[0036] The natural sound collection unit can simultaneously collect meteorological data such as environmental temperature and humidity and reflect it in the music generation. For example, when collecting natural sounds, the natural sound collection unit simultaneously collects meteorological data such as environmental temperature and humidity and inputs it into the generation AI. For example, when collecting rain sounds, it collects temperature and humidity data and reflects that information in the music generation. The natural sound collection unit can also collect meteorological data and generate background music based on that. For example, when collecting wind sounds, it collects wind speed and direction data and generates background music based on that information. In this way, collecting meteorological data and reflecting it in the music generation provides a more realistic musical experience.

[0037] The generation unit can analyze the rhythm and tempo of natural sounds and generate background music that is perfectly synchronized with them. For example, the generation AI analyzes the rhythm and tempo of natural sounds and generates background music that is perfectly synchronized with them. For example, a piano melody can be generated to match the rhythm of rain sounds, harmonizing with the rain sounds. The generation unit can also analyze the rhythm and tempo of natural sounds and generate background music based on that. For example, a flute melody can be generated to match the rhythm of wind sounds, harmonizing with the wind sounds. This generates background music that is synchronized with the rhythm and tempo of natural sounds, providing a more harmonious musical experience.

[0038] The generation unit can generate background music that incorporates traditional musical elements from different cultural spheres. For example, when the generation AI generates background music that matches natural sounds, the generation unit incorporates traditional musical elements from different cultural spheres. For example, the generation unit generates the sound of a shakuhachi, a traditional Japanese instrument, to match the sound of rain. The generation unit can also generate background music that incorporates traditional musical elements from different cultural spheres. For example, the generation unit generates the rhythm of a djembe, a traditional African instrument, to match the sound of wind. This provides a global musical experience by incorporating traditional musical elements from different cultural spheres.

[0039] The generation unit can refer to the user's past music playback history and generate background music that matches the user's preferences. For example, when the generation AI generates background music that matches natural sounds, the generation unit refers to the user's past music playback history. For example, if the user has previously preferred music that has a high relaxing effect, the generation unit generates background music that has a high relaxing effect based on that history. The generation unit can also refer to the user's past music playback history and generate background music based on that history. For example, if the user has previously preferred music that has a high concentration effect, the generation unit generates background music that has a high concentration effect based on that history. In this way, by referring to the user's past music playback history, a more personalized music experience is provided.

[0040] The generation unit can generate background music according to the user's activity status. For example, the generation unit uses sensors in a smartwatch or smartphone to detect the user's activity status. For example, if the user is walking, the generation unit generates background music suitable for walking based on that data. The generation unit can also generate background music according to the user's activity status. For example, if the user is running, the generation unit generates background music suitable for running based on that data. In this way, generating background music according to the user's activity status provides a more appropriate music experience.

[0041] The generation unit can generate background music with a relaxing effect according to the user's heart rate and stress level. The generation unit uses, for example, a smartwatch or fitness tracker to monitor the user's heart rate and stress level. For example, if the user's heart rate is high, the generation unit generates background music with a relaxing effect based on that data. The generation unit can also generate background music according to the user's stress level. For example, if the user's stress level is high, the generation unit generates background music with a relaxing effect based on that data. In this way, generating background music with a relaxing effect according to the user's heart rate and stress level provides a more appropriate music experience.

[0042] When a user is in a specific location, the generation unit can collect natural sounds specific to that location and generate background music that matches them. For example, when a user is in a specific location, the generation unit uses GPS data to collect natural sounds specific to that location. For example, if the user is at the beach, the generation unit can collect ocean sounds based on that data and generate background music that matches them. The generation unit can also collect natural sounds specific to a specific location and generate background music based on that. For example, if the user is in the mountains, the generation unit can collect bird chirping and wind sounds based on that data and generate background music that matches them. In this way, by collecting natural sounds specific to a specific location and generating background music that matches them, a more realistic musical experience is provided.

[0043] The generation unit can work in conjunction with other apps to provide background music that matches the activity content. For example, if a user is using a meditation app, the generation unit inputs that data into the generation AI to provide background music that is suitable for meditation. For example, background music with a high relaxing effect is generated based on session data from the meditation app. The generation unit can also work in conjunction with other apps to provide background music that matches the activity content. For example, background music suitable for exercise is generated based on data from a fitness app. This allows for collaboration with other apps to provide background music that matches the activity content, providing a more appropriate music experience.

[0044] The generation unit can analyze the volume and sound quality of the environmental sound in real time and dynamically adjust the volume and sound quality of the background music accordingly. For example, the generation unit analyzes the volume and sound quality of the environmental sound in real time and dynamically adjusts the volume and sound quality of the background music based on that data. For example, if the environmental sound is loud, the volume of the background music is lowered based on that data. The generation unit can also analyze the sound quality of the environmental sound and adjust the sound quality of the background music based on that analysis. For example, if the environmental sound contains a lot of high-frequency components, the high-frequency components of the background music are adjusted based on that data. In this way, by dynamically adjusting the background music according to the volume and sound quality of the environmental sound, a more harmonious musical experience is provided.

[0045] The generation unit can adjust the tempo and rhythm of the background music in real time according to changes in the environmental sound. For example, the generation unit analyzes changes in the environmental sound in real time and dynamically adjusts the tempo and rhythm of the background music based on the data. For example, if the environmental sound becomes faster, the generation unit speeds up the tempo of the background music based on the data. The generation unit can also adjust the rhythm of the background music according to changes in the environmental sound. For example, if the environmental sound changes to a slower rhythm, the generation unit adjusts the rhythm of the background music based on the data. In this way, by adjusting the tempo and rhythm of the background music according to changes in the environmental sound, a more harmonious musical experience is provided.

[0046] The generation unit can generate background music that incorporates natural sounds specific to different seasons and times of day. For example, when generating background music that matches environmental sounds, the generation unit incorporates natural sounds specific to different seasons. For example, in spring, background music that incorporates birdsong and the sound of wind blowing through fresh greenery is generated. The generation unit can also generate background music that incorporates natural sounds specific to different times of day. For example, at night, background music that incorporates insects chirping and the sound of quiet wind is generated. In this way, incorporating natural sounds specific to different seasons and times of day provides a more realistic musical experience.

[0047] The generation unit can provide a function that allows the user to customize the instrumental composition and timbre of the background music. For example, the generation unit provides an interface that allows the user to customize the instrumental composition of the background music in detail. For example, the user can select instruments such as piano, guitar, and flute and adjust the timbre of each instrument. The generation unit can also provide a function that allows the user to customize the timbre of the background music. For example, the user can adjust the pitch, intensity, duration, etc. This allows the user to customize the instrumental composition and timbre of the background music, providing a more personalized musical experience.

[0048] The generation unit can provide a function that allows the user to select a background music generation algorithm. The generation unit, for example, provides an interface that allows the user to select a background music generation algorithm. For example, the user can select an algorithm that has a high relaxation effect or an algorithm that has a high concentration effect. The generation unit can also provide a function that allows the user to try out different generation AI models. For example, the user can select different generation algorithms such as rule-based generation or machine learning-based generation. This allows the user to select the background music generation algorithm, providing a more personalized music experience.

[0049] The generation unit can provide a function that allows a user to share customized settings with other users and refer to popular settings within a community. The generation unit, for example, provides an interface that allows a user to share their customized settings with other users. For example, a user can share settings that have a high relaxation effect or settings that have a high concentration effect. The generation unit can also provide a function that allows a user to refer to popular settings within a community. For example, a user can refer to settings that other users prefer and incorporate them into their own settings. This allows a user to share customized settings with other users and refer to popular settings within a community, thereby providing a more diverse music experience.

[0050] The generation unit can provide a function that allows the user to select a preset customized setting that matches a specific activity. The generation unit, for example, provides an interface that allows the user to select a preset customized setting that matches a specific activity. For example, a setting that provides a high relaxation effect suitable for yoga or a setting that provides a high concentration effect suitable for reading is provided. The generation unit can also provide a function that allows the user to select a preset customized setting that matches a specific activity. For example, a fast-paced setting that matches exercise or a quiet setting that matches meditation is provided. This allows the user to select a preset customized setting that matches a specific activity, thereby providing a more appropriate music experience.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The natural sound collection unit can link with the user's smart device and integrate and analyze sounds from multiple devices. For example, by linking with a smart watch and a smart speaker, natural sounds collected from multiple devices can be integrated and analyzed. For example, a wider range of natural sounds can be collected using the microphones of a smart watch and a smart speaker. The natural sound collection unit can also integrate and analyze sounds from multiple devices. For example, the microphones of a smartphone and a tablet can be used to collect natural sounds and the data can be integrated and analyzed. In this way, by integrating and analyzing sounds from multiple devices, a wider range of natural sounds can be collected and the analysis accuracy can be improved.

[0053] The generator can generate background music that incorporates traditional musical elements from different cultural spheres. For example, when the generator AI generates background music that matches the sounds of nature, it incorporates traditional musical elements from different cultural spheres. For example, it generates the sound of a shakuhachi, a traditional Japanese instrument, to match the sound of rain. The generator can also generate background music that incorporates traditional musical elements from different cultural spheres. For example, it generates the rhythm of a djembe, a traditional African instrument, to match the sound of wind. This incorporates traditional musical elements from different cultural spheres, providing a global musical experience.

[0054] The generation unit can refer to the user's past music playback history and generate background music that matches their preferences. For example, when the generation AI generates background music that matches natural sounds, it refers to the user's past music playback history. For example, if the user has previously preferred music that has a high relaxing effect, it will generate background music that has a high relaxing effect based on that history. The generation unit can also refer to the user's past music playback history and generate background music based on that. For example, if the user has previously preferred music that has a high concentration effect, it will generate background music that has a high concentration effect based on that history. In this way, by referring to the user's past music playback history, a more personalized music experience is provided.

[0055] The generation unit can generate background music according to the user's activity status. For example, a sensor in a smartwatch or smartphone is used to detect the user's activity status. For example, if the user is walking, the generation unit generates background music suitable for walking based on that data. The generation unit can also generate background music according to the user's activity status. For example, if the user is running, the generation unit generates background music suitable for running based on that data. In this way, generating background music according to the user's activity status provides a more appropriate music experience.

[0056] The generation unit can analyze the volume and sound quality of the environmental sound in real time and dynamically adjust the volume and sound quality of the background music accordingly. For example, the volume and sound quality of the environmental sound can be analyzed in real time and the volume and sound quality of the background music can be dynamically adjusted based on that data. For example, if the environmental sound is loud, the volume of the background music can be lowered based on that data. The generation unit can also analyze the sound quality of the environmental sound and adjust the sound quality of the background music based on that data. For example, if the environmental sound contains a lot of high-frequency components, the high-frequency components of the background music can be adjusted based on that data. In this way, the background music can be dynamically adjusted according to the volume and sound quality of the environmental sound, providing a more harmonious musical experience.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The natural sound collection unit collects natural sounds. For example, the natural sound collection unit uses a microphone to collect natural sounds such as rain, wind, and waves. The natural sound collection unit can also connect to the user's smart device and collect sounds from multiple devices in an integrated manner. For example, a wide range of natural sounds can be collected using a smart watch or smart speaker. Step 2: The analysis unit analyzes the natural sounds collected by the natural sound collection unit. For example, the analysis unit performs frequency analysis of the sounds and extracts the characteristics of the natural sounds. The analysis unit can also analyze the rhythm and tempo of the natural sounds and provide data for generating background music based on the analysis. For example, the analysis is performed in accordance with the rhythm of the sound of rain. Step 3: The generator generates background music based on the natural sounds analyzed by the analyzer. For example, the generator generates a quiet piano melody that harmonizes with the sound of rain. The generator can also generate a flute melody that harmonizes with the sound of wind. For example, the generator generates a flute melody that matches the sound of wind.

[0059] (Example 2) The nature sound real-time background music maker according to an embodiment of the present invention is a system that collects nature sounds, analyzes them with a generation AI, and generates background music that matches the nature sounds in real time. This allows the nature sound real-time background music maker to provide users with a unique musical experience.

[0060] A natural sound real-time background music maker according to an embodiment includes a natural sound collection unit, an analysis unit, and a generation unit. The natural sound collection unit collects natural sounds. For example, the natural sound collection unit uses a microphone to collect natural sounds such as rain, wind, and waves. The natural sound collection unit can also connect to a user's smart device and collect sounds from multiple devices. For example, a wide range of natural sounds can be collected using a smart watch or smart speaker. The analysis unit analyzes the natural sounds collected by the natural sound collection unit. For example, the analysis unit performs frequency analysis of the sounds to extract characteristics of the natural sounds. The analysis unit can also analyze the rhythm and tempo of the natural sounds and provide data for generating background music based on the analysis. For example, the analysis is performed in accordance with the rhythm of the rain. The generation unit generates background music based on the natural sounds analyzed by the analysis unit. For example, the generation unit generates a quiet piano melody that harmonizes with the rain. The generation unit can also generate a flute melody that harmonizes with the wind. For example, the generation unit generates a flute melody that harmonizes with the wind. This allows the nature sound real-time background music maker according to the embodiment to generate background music in real time based on nature sounds. For example, users can enjoy music while feeling like they are in nature. This is particularly useful when studying, concentrating, meditating, or relaxing.

[0061] The generation unit can generate a quiet piano melody that harmonizes with the sound of rain. For example, the generation AI generates a piano melody that matches the rhythm of the sound of rain, harmonizing it with the sound of rain. The generation unit can also analyze the frequency components of the sound of rain and generate a piano tone that matches a specific frequency band. For example, it can generate a bass sound that matches the low-frequency components of the sound of rain, harmonizing it with the sound of rain. In this way, background music that harmonizes with the sound of rain is generated, providing a relaxing effect to the user.

[0062] The generation unit can generate a flute timbre that harmonizes with the wind sound. For example, the generation AI generates a flute melody that harmonizes with the wind sound. For example, the generation AI generates a flute melody that matches the rhythm of the wind sound and harmonizes with the wind sound. The generation unit can also analyze the frequency components of the wind sound and generate a flute timbre that matches a specific frequency band. For example, the generation unit generates a flute timbre that matches the high-frequency components of the wind sound and harmonizes with the wind sound. In this way, background music that harmonizes with the wind sound is generated, providing a relaxing effect to the user.

[0063] The generation unit can generate a light melody that harmonizes with the chirping of birds. For example, the generation AI generates a melody that matches the rhythm of the birdsong, harmonizing it with the birdsong. The generation unit can also analyze the frequency components of the birdsong and generate a melody that matches a specific frequency band. For example, a light melody can be generated that matches the high-frequency components of the birdsong, harmonizing it with the birdsong. In this way, background music that harmonizes with the birdsong is generated, providing a relaxing effect to the user.

[0064] The generation unit can dynamically adjust the style and tempo of the background music based on the user's emotions. For example, the generation unit dynamically adjusts the style and tempo of the background music based on the user's emotions. For example, if the user is relaxed using the emotion estimation function, background music with a high relaxing effect is generated based on the emotion data. The generation unit can also adjust the tempo of the background music based on the user's emotions. For example, if the user is concentrating, background music with a fast tempo is generated based on the emotion data. In this way, by adjusting the background music according to the user's emotions, a more personalized music experience is provided.

[0065] The natural sound collection unit can use multiple microphones to identify the direction and distance of a sound and analyze it as stereophonic sound. The natural sound collection unit, for example, uses multiple microphones to identify the direction and distance of a sound and analyzes it as stereophonic sound. For example, the natural sound collection unit identifies the direction from which rain is falling and recreates a stereophonic sound field based on that information. The natural sound collection unit can also identify the direction of a sound using the difference in sound arrival time. For example, multiple microphones can be placed and the difference in sound arrival time can be measured to identify the direction of the sound. This allows for analysis as stereophonic sound to recreate a more realistic sound field.

[0066] The analysis unit learns patterns of natural sounds and can predict and analyze specific natural sounds before they occur. For example, the generation AI of the analysis unit learns past natural sound data and recognizes specific patterns. For example, the generation AI learns the sound of wind and changes in humidity before rain starts to fall, and predicts and analyzes the sound of rain before it occurs. The analysis unit can also learn patterns of natural sounds and predict and analyze specific natural sounds before they occur. For example, the generation AI learns patterns of wind sounds and predicts and analyzes the sound of wind before it gets stronger. This allows for more accurate analysis by learning and predicting patterns of natural sounds.

[0067] The analysis unit can analyze the user's emotions and optimize the collection and analysis of natural sounds based on the emotions. The analysis unit, for example, uses an emotion estimation function to analyze the user's emotions when listening to natural sounds in real time. For example, if the user is relaxing by listening to the sound of rain, the collection and analysis of rain sounds is optimized based on the emotion data. The analysis unit can also optimize the collection and analysis of natural sounds based on the user's emotions. For example, if the user is relaxing by listening to the sound of wind, the collection and analysis of wind sounds is optimized based on the emotion data. In this way, the collection and analysis of natural sounds is optimized based on the user's emotions, providing a more personalized music experience.

[0068] The natural sound collection unit can link with the user's smart devices and integrate and analyze sounds from multiple devices. The natural sound collection unit, for example, links with the user's smartwatch and smart speaker and integrates and analyzes natural sounds collected from multiple devices. For example, the microphone of the smartwatch and the microphone of the smart speaker are used to collect a wider range of natural sounds. The natural sound collection unit can also integrate and analyze sounds from multiple devices. For example, the microphones of a smartphone and a tablet are used to collect natural sounds and the data is integrated and analyzed. In this way, by integrating and analyzing sounds from multiple devices, a wider range of natural sounds can be collected and the analysis accuracy can be improved.

[0069] The natural sound collection unit can simultaneously collect meteorological data such as environmental temperature and humidity and reflect it in the music generation. For example, when collecting natural sounds, the natural sound collection unit simultaneously collects meteorological data such as environmental temperature and humidity and inputs it into the generation AI. For example, when collecting rain sounds, it collects temperature and humidity data and reflects that information in the music generation. The natural sound collection unit can also collect meteorological data and generate background music based on that. For example, when collecting wind sounds, it collects wind speed and direction data and generates background music based on that information. In this way, collecting meteorological data and reflecting it in the music generation provides a more realistic musical experience.

[0070] The natural sound collection unit can analyze the user's emotions in real time and prioritize collecting natural sounds that elicit positive emotions. The natural sound collection unit, for example, uses an emotion estimation function to analyze the user's emotions when collecting natural sounds in real time. For example, if the user is relaxed, the natural sound collection unit prioritizes collecting natural sounds that have a high relaxing effect based on the emotion data. The natural sound collection unit can also prioritize collecting natural sounds based on the user's emotions. For example, if the user is relaxing by listening to the sound of wind, the natural sound collection unit prioritizes collecting wind sounds based on the emotion data. In this way, by preferentially collecting natural sounds based on the user's emotions, a more positive music experience is provided.

[0071] The generation unit can analyze the rhythm and tempo of natural sounds and generate background music that is perfectly synchronized with them. For example, the generation AI analyzes the rhythm and tempo of natural sounds and generates background music that is perfectly synchronized with them. For example, a piano melody can be generated to match the rhythm of rain sounds, harmonizing with the rain sounds. The generation unit can also analyze the rhythm and tempo of natural sounds and generate background music based on that. For example, a flute melody can be generated to match the rhythm of wind sounds, harmonizing with the wind sounds. This generates background music that is synchronized with the rhythm and tempo of natural sounds, providing a more harmonious musical experience.

[0072] The generation unit can generate background music based on the user's emotions. For example, the generation unit uses an emotion estimation function to generate background music that matches the user's emotions based on the emotions they felt when listening to natural sounds. For example, if the user is relaxed, the generation unit generates background music with a high relaxing effect based on the emotion data. The generation unit can also adjust the style and tempo of the background music based on the user's emotions. For example, if the user is concentrating, the generation unit generates background music with a fast tempo based on the emotion data. In this way, generating background music based on the user's emotions provides a more personalized music experience.

[0073] The generation unit can generate background music that incorporates traditional musical elements from different cultural spheres. For example, when the generation AI generates background music that matches natural sounds, the generation unit incorporates traditional musical elements from different cultural spheres. For example, the generation unit generates the sound of a shakuhachi, a traditional Japanese instrument, to match the sound of rain. The generation unit can also generate background music that incorporates traditional musical elements from different cultural spheres. For example, the generation unit generates the rhythm of a djembe, a traditional African instrument, to match the sound of wind. This provides a global musical experience by incorporating traditional musical elements from different cultural spheres.

[0074] The generation unit can refer to the user's past music playback history and generate background music that matches the user's preferences. For example, when the generation AI generates background music that matches natural sounds, the generation unit refers to the user's past music playback history. For example, if the user has previously preferred music that has a high relaxing effect, the generation unit generates background music that has a high relaxing effect based on that history. The generation unit can also refer to the user's past music playback history and generate background music based on that history. For example, if the user has previously preferred music that has a high concentration effect, the generation unit generates background music that has a high concentration effect based on that history. In this way, by referring to the user's past music playback history, a more personalized music experience is provided.

[0075] The generation unit can dynamically adjust the background music based on the user's emotions. For example, the generation unit uses an emotion estimation function to analyze the user's emotions in real time when listening to background music that matches natural sounds, and dynamically adjusts the background music according to the emotions. For example, if the user is relaxed, the tempo and tone of the background music are dynamically adjusted based on the emotion data. The generation unit can also adjust the style and tempo of the background music based on the user's emotions. For example, if the user is concentrating, a faster-tempo background music is generated based on the emotion data. This dynamically adjusts the background music according to the user's emotions, providing a more personalized music experience.

[0076] The generation unit can generate background music according to the user's activity status. For example, the generation unit uses sensors in a smartwatch or smartphone to detect the user's activity status. For example, if the user is walking, the generation unit generates background music suitable for walking based on that data. The generation unit can also generate background music according to the user's activity status. For example, if the user is running, the generation unit generates background music suitable for running based on that data. In this way, generating background music according to the user's activity status provides a more appropriate music experience.

[0077] The generation unit can generate background music with a relaxing effect according to the user's heart rate and stress level. The generation unit uses, for example, a smartwatch or fitness tracker to monitor the user's heart rate and stress level. For example, if the user's heart rate is high, the generation unit generates background music with a relaxing effect based on that data. The generation unit can also generate background music according to the user's stress level. For example, if the user's stress level is high, the generation unit generates background music with a relaxing effect based on that data. In this way, generating background music with a relaxing effect according to the user's heart rate and stress level provides a more appropriate music experience.

[0078] The generation unit can provide background music according to the user's emotional state. The generation unit, for example, uses an emotion estimation function to analyze the user's emotional state in real time and provide background music according to that emotion. For example, if the user is relaxed, background music with a high relaxing effect is provided based on the emotional data. The generation unit can also provide background music according to the user's emotional state. For example, if the user is concentrating, background music with a high concentration effect is provided based on the emotional data. In this way, by providing background music according to the user's emotional state, a more appropriate music experience is provided.

[0079] When a user is in a specific location, the generation unit can collect natural sounds specific to that location and generate background music that matches them. For example, when a user is in a specific location, the generation unit uses GPS data to collect natural sounds specific to that location. For example, if the user is at the beach, the generation unit can collect ocean sounds based on that data and generate background music that matches them. The generation unit can also collect natural sounds specific to a specific location and generate background music based on that. For example, if the user is in the mountains, the generation unit can collect bird chirping and wind sounds based on that data and generate background music that matches them. In this way, by collecting natural sounds specific to a specific location and generating background music that matches them, a more realistic musical experience is provided.

[0080] The generation unit can work in conjunction with other apps to provide background music that matches the activity content. For example, if a user is using a meditation app, the generation unit inputs that data into the generation AI to provide background music that is suitable for meditation. For example, background music with a high relaxing effect is generated based on session data from the meditation app. The generation unit can also work in conjunction with other apps to provide background music that matches the activity content. For example, background music suitable for exercise is generated based on data from a fitness app. This allows for collaboration with other apps to provide background music that matches the activity content, providing a more appropriate music experience.

[0081] The generation unit can analyze the emotions of the user when performing a specific activity and provide background music that corresponds to those emotions. The generation unit, for example, uses an emotion estimation function to analyze the emotions of the user when performing a specific activity in real time and provides background music that corresponds to those emotions. For example, if the user is meditating, background music with a high relaxing effect is provided based on the emotion data. The generation unit can also provide background music based on the user's emotions. For example, if the user is exercising, background music suitable for exercise is provided based on the emotion data. In this way, by providing background music that corresponds to the emotions of the user when performing a specific activity, a more appropriate musical experience is provided.

[0082] The generation unit can analyze the volume and sound quality of the environmental sound in real time and dynamically adjust the volume and sound quality of the background music accordingly. For example, the generation unit analyzes the volume and sound quality of the environmental sound in real time and dynamically adjusts the volume and sound quality of the background music based on that data. For example, if the environmental sound is loud, the volume of the background music is lowered based on that data. The generation unit can also analyze the sound quality of the environmental sound and adjust the sound quality of the background music based on that analysis. For example, if the environmental sound contains a lot of high-frequency components, the high-frequency components of the background music are adjusted based on that data. In this way, by dynamically adjusting the background music according to the volume and sound quality of the environmental sound, a more harmonious musical experience is provided.

[0083] The generation unit can adjust the tempo and rhythm of the background music in real time according to changes in the environmental sound. For example, the generation unit analyzes changes in the environmental sound in real time and dynamically adjusts the tempo and rhythm of the background music based on the data. For example, if the environmental sound becomes faster, the generation unit speeds up the tempo of the background music based on the data. The generation unit can also adjust the rhythm of the background music according to changes in the environmental sound. For example, if the environmental sound changes to a slower rhythm, the generation unit adjusts the rhythm of the background music based on the data. In this way, by adjusting the tempo and rhythm of the background music according to changes in the environmental sound, a more harmonious musical experience is provided.

[0084] The generation unit can analyze the emotions felt by the user when listening to environmental sounds and generate background music according to those emotions. The generation unit, for example, uses an emotion estimation function to analyze the emotions felt by the user when listening to environmental sounds in real time and generate background music according to those emotions. For example, if the user is relaxed, background music with a high relaxing effect is generated based on the emotion data. The generation unit can also generate background music based on the user's emotions. For example, if the user is concentrating, background music with a high concentration effect is generated based on the emotion data. In this way, by generating background music according to the emotions felt by the user when listening to environmental sounds, a more personalized music experience is provided.

[0085] The generation unit can generate background music that incorporates natural sounds specific to different seasons and times of day. For example, when generating background music that matches environmental sounds, the generation unit incorporates natural sounds specific to different seasons. For example, in spring, background music that incorporates birdsong and the sound of wind blowing through fresh greenery is generated. The generation unit can also generate background music that incorporates natural sounds specific to different times of day. For example, at night, background music that incorporates insects chirping and the sound of quiet wind is generated. In this way, incorporating natural sounds specific to different seasons and times of day provides a more realistic musical experience.

[0086] The generation unit can analyze the emotions felt by the user when listening to environmental sounds and provide background music that corresponds to those emotions. The generation unit, for example, uses an emotion estimation function to analyze the emotions felt by the user when listening to environmental sounds in real time and provides background music that corresponds to those emotions. For example, if the user is relaxed, background music with a high relaxing effect is provided based on the emotion data. The generation unit can also provide background music based on the user's emotions. For example, if the user is concentrating, background music with a high concentration effect is provided based on the emotion data. This provides background music that corresponds to the emotions felt by the user when listening to environmental sounds, thereby providing a more personalized music experience.

[0087] The generation unit can provide a function that allows the user to customize the instrumental composition and timbre of the background music. For example, the generation unit provides an interface that allows the user to customize the instrumental composition of the background music in detail. For example, the user can select instruments such as piano, guitar, and flute and adjust the timbre of each instrument. The generation unit can also provide a function that allows the user to customize the timbre of the background music. For example, the user can adjust the pitch, intensity, duration, etc. This allows the user to customize the instrumental composition and timbre of the background music, providing a more personalized musical experience.

[0088] The generation unit can provide a function that allows the user to select a background music generation algorithm. The generation unit, for example, provides an interface that allows the user to select a background music generation algorithm. For example, the user can select an algorithm that has a high relaxation effect or an algorithm that has a high concentration effect. The generation unit can also provide a function that allows the user to try out different generation AI models. For example, the user can select different generation algorithms such as rule-based generation or machine learning-based generation. This allows the user to select the background music generation algorithm, providing a more personalized music experience.

[0089] The generation unit can provide a function of suggesting optimal customization settings based on the user's emotions. The generation unit, for example, uses an emotion estimation function to automatically suggest optimal customization settings based on the user's emotions. For example, if the user is relaxed, the generation unit suggests settings with a high relaxation effect based on the emotion data. The generation unit can also suggest customization settings based on the user's emotions. For example, if the user is concentrating, the generation unit suggests settings with a high concentration effect based on the emotion data. In this way, by suggesting optimal customization settings based on the user's emotions, a more personalized music experience is provided.

[0090] The generation unit can provide a function that allows a user to share customized settings with other users and refer to popular settings within a community. The generation unit, for example, provides an interface that allows a user to share their customized settings with other users. For example, a user can share settings that have a high relaxation effect or settings that have a high concentration effect. The generation unit can also provide a function that allows a user to refer to popular settings within a community. For example, a user can refer to settings that other users prefer and incorporate them into their own settings. This allows a user to share customized settings with other users and refer to popular settings within a community, thereby providing a more diverse music experience.

[0091] The generation unit can provide a function that allows the user to select a preset customized setting that matches a specific activity. The generation unit, for example, provides an interface that allows the user to select a preset customized setting that matches a specific activity. For example, a setting that provides a high relaxation effect suitable for yoga or a setting that provides a high concentration effect suitable for reading is provided. The generation unit can also provide a function that allows the user to select a preset customized setting that matches a specific activity. For example, a fast-paced setting that matches exercise or a quiet setting that matches meditation is provided. This allows the user to select a preset customized setting that matches a specific activity, thereby providing a more appropriate music experience.

[0092] The generation unit can analyze the emotions felt by the user when using a specific customization setting and suggest customization settings that correspond to the emotions. For example, the generation unit uses an emotion estimation function to analyze the emotions felt by the user in real time when using a specific customization setting and suggest customization settings that correspond to the emotions. For example, if the user is relaxed, the generation unit suggests settings that have a high relaxation effect based on the emotion data. The generation unit can also suggest customization settings based on the user's emotions. For example, if the user is concentrating, the generation unit suggests settings that have a high concentration effect based on the emotion data. This provides a more personalized music experience by suggesting customization settings that correspond to the emotions felt by the user when using a specific customization setting.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The natural sound collection unit can link with the user's smart device and integrate and analyze sounds from multiple devices. For example, by linking with a smart watch and a smart speaker, natural sounds collected from multiple devices can be integrated and analyzed. For example, a wider range of natural sounds can be collected using the microphones of a smart watch and a smart speaker. The natural sound collection unit can also integrate and analyze sounds from multiple devices. For example, the microphones of a smartphone and a tablet can be used to collect natural sounds and the data can be integrated and analyzed. In this way, by integrating and analyzing sounds from multiple devices, a wider range of natural sounds can be collected and the analysis accuracy can be improved.

[0095] The generator can generate background music that incorporates traditional musical elements from different cultural spheres. For example, when the generator AI generates background music that matches the sounds of nature, it incorporates traditional musical elements from different cultural spheres. For example, it generates the sound of a shakuhachi, a traditional Japanese instrument, to match the sound of rain. The generator can also generate background music that incorporates traditional musical elements from different cultural spheres. For example, it generates the rhythm of a djembe, a traditional African instrument, to match the sound of wind. This incorporates traditional musical elements from different cultural spheres, providing a global musical experience.

[0096] The generation unit can refer to the user's past music playback history and generate background music that matches their preferences. For example, when the generation AI generates background music that matches natural sounds, it refers to the user's past music playback history. For example, if the user has previously preferred music that has a high relaxing effect, it will generate background music that has a high relaxing effect based on that history. The generation unit can also refer to the user's past music playback history and generate background music based on that. For example, if the user has previously preferred music that has a high concentration effect, it will generate background music that has a high concentration effect based on that history. In this way, by referring to the user's past music playback history, a more personalized music experience is provided.

[0097] The generation unit can generate background music according to the user's activity status. For example, a sensor in a smartwatch or smartphone is used to detect the user's activity status. For example, if the user is walking, the generation unit generates background music suitable for walking based on that data. The generation unit can also generate background music according to the user's activity status. For example, if the user is running, the generation unit generates background music suitable for running based on that data. In this way, generating background music according to the user's activity status provides a more appropriate music experience.

[0098] The generation unit can analyze the volume and sound quality of the environmental sound in real time and dynamically adjust the volume and sound quality of the background music accordingly. For example, the volume and sound quality of the environmental sound can be analyzed in real time and the volume and sound quality of the background music can be dynamically adjusted based on that data. For example, if the environmental sound is loud, the volume of the background music can be lowered based on that data. The generation unit can also analyze the sound quality of the environmental sound and adjust the sound quality of the background music based on that data. For example, if the environmental sound contains a lot of high-frequency components, the high-frequency components of the background music can be adjusted based on that data. In this way, the background music can be dynamically adjusted according to the volume and sound quality of the environmental sound, providing a more harmonious musical experience.

[0099] The generation unit can dynamically adjust the style and tempo of the background music based on the user's emotions. For example, the generation unit dynamically adjusts the style and tempo of the background music based on the user's emotions. For example, if the user is relaxed using the emotion estimation function, background music with a high relaxing effect is generated based on the emotion data. The generation unit can also adjust the tempo of the background music based on the user's emotions. For example, if the user is concentrating, background music with a fast tempo is generated based on the emotion data. In this way, by adjusting the background music according to the user's emotions, a more personalized music experience is provided.

[0100] The analysis unit can analyze the user's emotions and optimize the collection and analysis of natural sounds based on those emotions. For example, the emotion estimation function is used to analyze the user's emotions when listening to natural sounds in real time. For example, if the user is relaxing by listening to the sound of rain, the collection and analysis of rain sounds is optimized based on that emotional data. The analysis unit can also optimize the collection and analysis of natural sounds based on the user's emotions. For example, if the user is relaxing by listening to the sound of wind, the collection and analysis of wind sounds is optimized based on that emotional data. In this way, the collection and analysis of natural sounds is optimized based on the user's emotions, providing a more personalized music experience.

[0101] The generation unit can generate background music with a relaxing effect according to the user's heart rate and stress level. For example, a smartwatch or fitness tracker is used to monitor the user's heart rate and stress level. For example, if the user's heart rate is high, background music with a relaxing effect is generated based on that data. The generation unit can also generate background music according to the user's stress level. For example, if the user's stress level is high, background music with a relaxing effect is generated based on that data. In this way, background music with a relaxing effect is generated according to the user's heart rate and stress level, providing a more appropriate music experience.

[0102] The generation unit can analyze the emotions of a user when performing a specific activity and provide background music that corresponds to those emotions. For example, the emotion estimation function can be used to analyze the emotions of a user when performing a specific activity in real time and provide background music that corresponds to those emotions. For example, if the user is meditating, background music with a high relaxing effect can be provided based on the emotion data. The generation unit can also provide background music based on the user's emotions. For example, if the user is exercising, background music suitable for exercise can be provided based on the emotion data. In this way, background music that corresponds to the emotions of a user when performing a specific activity can be provided, providing a more appropriate musical experience.

[0103] The generation unit can analyze the emotions felt by the user when listening to environmental sounds and generate background music according to those emotions. For example, the emotion estimation function can be used to analyze the emotions felt by the user when listening to environmental sounds in real time and generate background music according to those emotions. For example, if the user is relaxed, background music with a high relaxing effect can be generated based on the emotion data. The generation unit can also generate background music based on the user's emotions. For example, if the user is concentrating, background music with a high concentration effect can be generated based on the emotion data. In this way, by generating background music according to the emotions felt by the user when listening to environmental sounds, a more personalized music experience can be provided.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The natural sound collection unit collects natural sounds. For example, the natural sound collection unit uses a microphone to collect natural sounds such as rain, wind, and waves. The natural sound collection unit can also connect to the user's smart device and collect sounds from multiple devices in an integrated manner. For example, a wide range of natural sounds can be collected using a smart watch or smart speaker. Step 2: The analysis unit analyzes the natural sounds collected by the natural sound collection unit. For example, the analysis unit performs frequency analysis of the sounds and extracts the characteristics of the natural sounds. The analysis unit can also analyze the rhythm and tempo of the natural sounds and provide data for generating background music based on the analysis. For example, the analysis is performed in accordance with the rhythm of the sound of rain. Step 3: The generator generates background music based on the natural sounds analyzed by the analyzer. For example, the generator generates a quiet piano melody that harmonizes with the sound of rain. The generator can also generate a flute melody that harmonizes with the sound of wind. For example, the generator generates a flute melody that matches the sound of wind.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] 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.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0150] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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]

[0173] 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 natural sound collection unit that collects natural sounds; an analysis unit that analyzes the natural sounds collected by the natural sound collection unit; a generation unit that generates background music based on the natural sounds analyzed by the analysis unit. A system characterized by:

2. The natural sound collection unit Using multiple microphones, the direction and distance of the sound are identified and analyzed as stereophonic sound.

2. The system of claim 1.

3. The natural sound collection unit The system works in conjunction with the user's smart devices to integrate the sounds from multiple devices and perform the analysis.

2. The system of claim 1.

4. The generation unit Analyze the rhythm and tempo of the natural sounds and generate background music that is perfectly synchronized with them.

2. The system of claim 1.

5. The generation unit Dynamically adjust background music style and tempo based on user emotions 2. The system of claim 1.

Images