System

Abstract

An object of a system according to an embodiment is to enable a general user to easily enjoy music production.SOLUTION: A system includes an interface unit, a generation unit, a style association unit, and a real-time generation unit. The interface unit creates music by taking advantage of the user's sensibility. The generation unit generates a musical piece based on the instruction input by the interface unit. The style association unit associates the musical piece generated by the generation unit with various music styles and genres. A real time generation part generates the musical piece associated by the style association part in real time.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 techniques have had the problem that music production requires specialized knowledge and skills, making it difficult for general users to easily perform.

[0005] The system according to the embodiment aims to enable general users to easily enjoy music production. [Means for solving the problem]

[0006] The system according to the embodiment includes an interface unit, a generation unit, a style support unit, and a real-time generation unit. The interface unit creates music by utilizing the user's sensibilities. The generation unit generates music based on instructions input through the interface unit. The style support unit adapts the music generated by the generation unit to various musical styles and genres. The real-time generation unit generates music adapted by the style support unit in real time. [Effects of the Invention]

[0007] The system according to the embodiment allows general users to easily enjoy music production. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The real-time music production tool according to the embodiment of the present invention is a system that utilizes the user's sensibilities to instantly create music. This allows the user to easily create original music and provides a creative experience.

[0029] A real-time music production tool according to an embodiment includes an interface unit, a generation unit, a style support unit, and a real-time generation unit. The interface unit creates music using the user's sensibilities. For example, users can arrange musical parts using drag and drop, and adjust volume and tempo by moving sliders. The interface unit is designed so that even beginners can quickly master it. The generation unit generates music based on instructions input through the interface unit. For example, if a user instructs the system that they want to create a jazz-style piece, the generation AI analyzes the instruction and generates a jazz-style piece. The generation unit also generates music based on the user's instructions. The style support unit adapts the music generated by the generation unit to various musical styles and genres. For example, music can be generated according to the user's preferences, such as pop, rock, classical, and electronica. The style support unit learns the characteristics of each genre and generates appropriate music based on the user's instructions. The real-time generation unit generates music in real time in accordance with the style support unit. For example, the generation AI responds immediately to each user operation, generating and updating the music. The real-time generation unit also allows the user to instantly complete a piece of music while enjoying the production process, allowing the real-time music production tool according to the embodiment to utilize the user's sensibilities and generate music in real time.

[0030] The interface unit can learn a user's operation history and automatically generate an interface optimized for each individual user. For example, the interface unit collects the user's operation history and analyzes frequently used functions and operation patterns. For example, for a user who frequently uses a particular effect or tempo adjustment, the interface unit places those functions in a prominent position in the interface. The interface unit also automatically sets frequently used functions as shortcuts based on the operation history. For example, for a user who frequently arranges musical parts by drag and drop, the interface unit provides a shortcut that makes that operation easy. The interface unit also learns the user's operation history and dynamically changes the interface layout. For example, for a user who frequently uses a particular function, the interface unit places that function at the top of the interface for easier access. In this way, an interface optimized based on the user's operation history is provided.

[0031] The interface unit may be equipped with a voice recognition function to enable operations to be performed by voice commands. For example, the interface unit may use voice recognition technology to enable a user to create or edit music using voice commands. For example, the interface unit recognizes voice commands such as "increase the tempo" or "add drums" and executes the corresponding operations. The interface unit may also be equipped with a voice recognition engine to enable operations to be performed by voice commands. For example, the interface unit may recognize commands such as "move to the next track" or "apply effects" and execute the corresponding operations. The interface unit may also use the voice recognition function to enable a user to create music without using their hands. For example, the interface unit may recognize voice commands such as "start playback" or "stop recording" and execute the corresponding operations. This allows operations to be performed by voice commands.

[0032] The interface unit can add haptic feedback to improve the feel of operation. For example, the interface unit adds a haptic feedback function to the interface so that the user feels vibration or pressure when performing an operation. For example, feeling a slight vibration when pressing a button improves the feel of operation. The interface unit also uses haptic feedback to enhance the sensation when the user performs an operation. For example, feeling resistance when moving a slider allows for more intuitive operation. The interface unit is also equipped with a haptic feedback function so that the user can receive feedback in real time when performing an operation. For example, feeling vibration when dragging and dropping musical parts improves the accuracy of operation. This adds haptic feedback and improves the feel of operation.

[0033] The interface unit can be combined with AR technology and linked to a physical instrument. The interface unit, for example, uses AR technology to build a system that links the interface with a physical instrument. For example, by wearing an AR headset, a display that integrates the physical instrument and the digital interface is provided. The interface unit also develops an AR interface that links with a physical instrument, allowing the user to perform digital operations while playing the instrument. For example, adjusting effects with the AR interface while playing the guitar. The interface unit also uses AR technology to integrate the physical instrument and the digital interface, providing an environment in which the user can intuitively operate it. For example, overlaying the AR interface on the piano keyboard allows music to be created in real time. This allows the interface to be combined with AR technology and linked to a physical instrument.

[0034] The generation unit can learn the user's past music production data and provide a music generation algorithm optimized for each individual user. For example, the generation unit collects the user's past music production data, and the generation AI learns that data. For example, the generation unit analyzes the style and patterns of songs the user has created in the past and provides an algorithm optimized for each individual user. The generation unit also learns the user's preferences and production style based on the past music production data, and the generation AI generates optimal songs. For example, it automatically generates songs that reflect the user's preferred rhythms and melodies. The generation unit also analyzes the user's past production data, and the generation AI provides an optimal music generation algorithm based on that data. For example, it generates songs that reflect the instruments and effects the user frequently uses. This provides a music generation algorithm optimized based on the user's past music production data.

[0035] The generation unit can incorporate natural sounds and environmental sounds into the music generation algorithm, enabling a wider variety of musical expressions. For example, the generation unit realizes a wider variety of musical expressions by incorporating natural sounds and environmental sounds into the music generation algorithm. For example, the generation unit incorporates birdsong and the sound of waves into a song. The generation unit also incorporates natural sounds and environmental sounds into the music generation algorithm, allowing the user to create unique musical expressions. For example, the sound of wind or rain is used in the background. The generation unit also incorporates natural sounds and environmental sounds into the music generation algorithm, allowing the user to create new musical styles. For example, the sound of a city bustle or the sound of a forest is incorporated into a song. In this way, incorporating natural sounds and environmental sounds enables a wider variety of musical expressions.

[0036] The generation unit can apply the music generation algorithm to creative fields. For example, the generation unit applies the music generation algorithm to video production to build a system that automatically generates music that matches the content of the video. For example, it generates background music that matches a movie scene. The generation unit also applies the music generation algorithm to game development to generate music in real time that matches the progress of the game. For example, it automatically generates music that matches the game's stages and events. The generation unit also applies the music generation algorithm to other creative fields to enable users to create a variety of content. For example, it generates music that matches animation or advertising videos. This allows the music generation algorithm to be applied to other creative fields.

[0037] The generation unit can open-source the music generation algorithm and promote improvements by the community. For example, the generation unit open-sources the music generation algorithm, making it freely accessible to the developer community. For example, the generation unit publishes the algorithm on a platform such as GitHub. To promote improvements by the community, the generation unit also publishes the source code of the music generation algorithm, allowing developers to make their own improvements. For example, new musical styles or functions can be added. The generation unit also allows the community to jointly develop the open-source music generation algorithm and improve its performance. For example, bug fixes and optimizations can be performed. In this way, the music generation algorithm is open-sourced and improvements by the community can be promoted.

[0038] The style support unit can learn the historical background and cultural elements of musical genres, enabling deeper expression. For example, the style support unit learns the historical background and cultural elements of each musical genre, and the generation AI generates music based on that knowledge. For example, the unit learns the history and culture of blues and reflects blues-specific expressions in the music. The style support unit also learns the cultural elements of musical genres, and the generation AI generates music that incorporates those elements. For example, the unit learns Latin music rhythms and instruments and generates music unique to Latin music. The style support unit also learns the historical background of each musical genre, and the generation AI generates music with deeper expression based on that knowledge. For example, the unit learns classical music composers and historical background and generates music unique to classical music. This allows the generation AI to learn the historical background and cultural elements of each musical genre, enabling deeper expression.

[0039] The style support unit can generate a new musical style by fusing multiple genres specified by the user. For example, the style support unit fuses multiple musical genres specified by the user, and the generation AI generates a new musical style. For example, it generates a song that fuses jazz and electronica. The style support unit also combines multiple musical genres, allowing the generation AI to create a unique musical style. For example, it generates a song that fuses rock and classical. The style support unit also builds a system in which the generation AI generates a new musical style based on the genres specified by the user. For example, it generates a song that fuses pop and hip hop. In this way, a new musical style is generated by fusing multiple genres specified by the user.

[0040] The style support unit can expand the database of musical styles and genres, enabling the generation of more diverse music. For example, the style support unit expands the database of musical styles and genres, enabling the generation AI to generate more diverse music. For example, new musical genres and subgenres are added to the database. By expanding the database, the style support unit also enables the generation AI to learn various musical styles and generate music that suits the user's preferences. For example, ethnic music and world music are added to the database. The style support unit also expands the database of musical styles and genres, enabling the generation AI to express more diverse music. For example, data on classical music and contemporary music is added to support a wider range of genres. This expands the database of musical styles and genres, enabling the generation AI to generate more diverse music.

[0041] The style support unit can collaborate with experts in different music genres to strengthen the learning data of the generative AI. For example, the style support unit collaborates with experts in different music genres to strengthen the learning data of the generative AI. For example, by collaborating with a jazz expert, it can learn expressions unique to jazz. The style support unit also collaborates with experts in music genres to expand the learning data of the generative AI, enabling it to generate a wider variety of music. For example, by collaborating with a classical music expert, it can add classical music data. The style support unit also collaborates with experts in different genres to enable the generative AI to learn a variety of musical styles. For example, by collaborating with experts in rock and pop, it can collect data from a wide range of genres. In this way, collaboration with experts in different music genres strengthens the learning data of the generative AI.

[0042] The real-time generation unit further accelerates the response speed of the generation AI to user operations, providing a smoother production experience. For example, the real-time generation unit accelerates the response speed of the generation AI and builds a system that responds immediately to user operations. For example, when a user adds an effect, the effect is instantly reflected. The real-time generation unit also optimizes the processing speed of the generation AI for real-time music generation, allowing users to operate it smoothly. For example, adjustments to tempo and volume are reflected in real time. The real-time generation unit also improves the response speed of the generation AI and develops a system in which music is updated instantly every time a user operates it. For example, adding or deleting instruments is reflected in real time. This further accelerates the response speed of the generation AI to user operations, providing a smoother production experience.

[0043] The real-time generation unit can add a function to emphasize specific musical elements specified by the user during real-time generation. The real-time generation unit adds a function to emphasize specific musical elements specified by the user during real-time generation. For example, the real-time generation unit may highlight a drum part to emphasize the rhythm. The real-time generation unit also provides a function to emphasize specific musical elements when the user generates music in real time. For example, the real-time generation unit may highlight a vocal part to emphasize the melody. The real-time generation unit also builds a system to emphasize musical elements specified by the user during real-time generation. For example, the real-time generation unit may strengthen the low range to emphasize the bass line. This adds a function to emphasize specific musical elements specified by the user during real-time generation.

[0044] The real-time generation unit can apply the real-time generation function to live performances to support improvisation. For example, the real-time generation unit applies the real-time generation function to live performances to build a system that supports improvisation. For example, a generation AI generates music in real time during a live performance, allowing performers to improvise. The real-time generation unit also generates music in real time during live performances, allowing performers to improvise on the spot. For example, music is generated based on the audience's reactions. The real-time generation unit also applies the real-time generation function to live performances to develop a system that allows performers to improvise music. For example, a generation AI generates music based on a theme specified by the performer. In this way, the real-time generation function is applied to live performances to support improvisation.

[0045] The real-time generation unit can add a function that allows collaboration with other users during real-time generation. The real-time generation unit, for example, builds a system that adds a function that allows collaboration with other users during real-time generation. For example, multiple users simultaneously generate music and share it in real time. The real-time generation unit also provides a function that allows music to be generated while collaborating with other users in real time. For example, remotely connected users jointly create music. The real-time generation unit also adds a function that allows collaboration with other users during real-time generation, developing a system for jointly generating music. For example, users share parts to generate music. This adds a function that allows collaboration with other users during real-time generation.

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

[0047] The interface unit can learn user operation history and automatically generate an interface optimized for each individual user. For example, it can collect user operation history and analyze frequently used functions and operation patterns. For users who frequently use specific effects or tempo adjustments, it can place those functions in prominent positions in the interface. It can also automatically set frequently used functions as shortcuts based on the operation history. For users who frequently use drag-and-drop to arrange musical parts, it can provide shortcuts that make that operation easy. It can also learn operation history and dynamically change the interface layout. For users who frequently use specific functions, it can place those functions at the top of the interface for easier access. This makes it possible to provide an interface that is optimized based on the user's operation history.

[0048] The interface unit can be added with a voice recognition function, allowing operations to be performed using voice commands. For example, voice recognition technology can be used to enable users to create and edit music using voice commands. For example, voice commands such as "increase the tempo" or "add drums" are recognized and the corresponding operations are performed. In addition, a voice recognition engine can be installed to enable operation of the interface using voice commands. For example, commands such as "move to the next track" or "apply effects" are recognized and the interface is operated. Furthermore, the voice recognition function can be used to enable users to create music without using their hands. For example, voice commands such as "start playback" or "stop recording" are recognized and the corresponding operations are performed. This allows operations to be performed using voice commands.

[0049] The interface unit can add haptic feedback to improve the feel of operation. For example, a haptic feedback function can be added to the interface so that the user feels vibrations or pressure when performing operations. Feeling a slight vibration when pressing a button improves the feel of operation. Haptic feedback can also be used to enhance the sense when the user performs operations. Feeling resistance when moving a slider allows for more intuitive operation. Furthermore, a haptic feedback function can be installed so that the user can receive feedback in real time when performing operations. Feeling vibrations when dragging and dropping musical parts improves the precision of operation. This adds haptic feedback and improves the feel of operation.

[0050] The interface unit can be combined with AR technology and linked to a physical instrument. For example, a system can be built using AR technology to link the interface with a physical instrument. By wearing an AR headset, a display that integrates the physical instrument and the digital interface is provided. An AR interface that links with a physical instrument can also be developed, allowing users to perform digital operations while playing the instrument. For example, adjusting effects with the AR interface while playing the guitar. Furthermore, AR technology can be used to integrate the physical instrument and the digital interface, providing an environment in which users can operate intuitively. The AR interface can be overlaid on the piano keys to create music in real time. This allows the system to be combined with AR technology and linked to a physical instrument.

[0051] The generation unit can learn from a user's past music production data and provide a music generation algorithm optimized for each individual user. For example, the generation AI collects a user's past music production data and learns from that data. The AI ​​analyzes the style and patterns of songs the user has created in the past and provides an algorithm optimized for each individual user. The AI ​​also learns the user's preferences and production style based on the past music production data and generates optimal songs. Songs that reflect the user's preferred rhythms and melodies are automatically generated. The AI ​​also analyzes the user's past production data and provides an optimal music generation algorithm based on that data. Songs are generated that reflect the instruments and effects the user frequently uses. This makes it possible to provide a music generation algorithm optimized based on the user's past music production data.

[0052] The generation unit can incorporate natural and environmental sounds into the music generation algorithm, enabling a wider variety of musical expressions. For example, incorporating natural and environmental sounds into the music generation algorithm enables a wider variety of musical expressions. Birdsong and the sound of waves can be incorporated into a song. Furthermore, incorporating natural and environmental sounds into the music generation algorithm allows users to create their own unique musical expressions. The sound of wind or rain can be used in the background. Furthermore, incorporating natural and environmental sounds into the music generation algorithm allows users to create new musical styles. The sound of a city bustle or the sound of a forest can be incorporated into a song. In this way, incorporating natural and environmental sounds enables a wider variety of musical expressions.

[0053] The generation unit can apply the music generation algorithm to creative fields. For example, the music generation algorithm can be applied to video production to build a system that automatically generates music that matches the content of the video. BGM can be generated to match movie scenes. The music generation algorithm can also be applied to game development to generate music in real time that matches the progress of the game. Music can be automatically generated to match the game's stages and events. Furthermore, the music generation algorithm can be applied to other creative fields, allowing users to create a variety of content. Music can be generated to match animations and advertising videos. This allows the music generation algorithm to be applied to other creative fields.

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

[0055] Step 1: The interface utilizes the user's sensibilities to create music. For example, users can arrange musical parts using drag and drop, and adjust the volume and tempo by moving sliders. The interface is also designed so that even beginners can quickly become familiar with it. Step 2: The generation unit generates music based on the instructions input through the interface unit. For example, if the user instructs the AI ​​to "create a jazz-style song," the AI ​​analyzes the instruction and generates a jazz-style song. The AI ​​then generates music based on the user's instructions. Step 3: The style support unit adapts the music generated by the generation unit to various musical styles and genres. For example, it can generate music that suits the user's preferences, such as pop, rock, classical, or electronica. The style support unit also learns the characteristics of each genre and generates appropriate music based on the user's instructions. Step 4: The real-time generation unit generates music in real time according to the style support unit. For example, every time the user performs an operation, the generation AI responds immediately, generating and updating the music. The real-time generation unit also allows the user to instantly complete a song while enjoying the production process.

[0056] (Example 2) The real-time music production tool according to the embodiment of the present invention is a system that utilizes the user's sensibilities to instantly create music. This allows the user to easily create original music and provides a creative experience.

[0057] A real-time music production tool according to an embodiment includes an interface unit, a generation unit, a style support unit, and a real-time generation unit. The interface unit creates music using the user's sensibilities. For example, users can arrange musical parts using drag and drop, and adjust volume and tempo by moving sliders. The interface unit is designed so that even beginners can quickly master it. The generation unit generates music based on instructions input through the interface unit. For example, if a user instructs the system that they want to create a jazz-style piece, the generation AI analyzes the instruction and generates a jazz-style piece. The generation unit also generates music based on the user's instructions. The style support unit adapts the music generated by the generation unit to various musical styles and genres. For example, music can be generated according to the user's preferences, such as pop, rock, classical, and electronica. The style support unit learns the characteristics of each genre and generates appropriate music based on the user's instructions. The real-time generation unit generates music in real time in accordance with the style support unit. For example, the generation AI responds immediately to each user operation, generating and updating the music. The real-time generation unit also allows the user to instantly complete a piece of music while enjoying the production process, allowing the real-time music production tool according to the embodiment to utilize the user's sensibilities and generate music in real time.

[0058] The interface unit can learn a user's operation history and automatically generate an interface optimized for each individual user. For example, the interface unit collects the user's operation history and analyzes frequently used functions and operation patterns. For example, for a user who frequently uses a particular effect or tempo adjustment, the interface unit places those functions in a prominent position in the interface. The interface unit also automatically sets frequently used functions as shortcuts based on the operation history. For example, for a user who frequently arranges musical parts by drag and drop, the interface unit provides a shortcut that makes that operation easy. The interface unit also learns the user's operation history and dynamically changes the interface layout. For example, for a user who frequently uses a particular function, the interface unit places that function at the top of the interface for easier access. In this way, an interface optimized based on the user's operation history is provided.

[0059] The interface unit may be equipped with a voice recognition function to enable operations to be performed by voice commands. For example, the interface unit may use voice recognition technology to enable a user to create or edit music using voice commands. For example, the interface unit recognizes voice commands such as "increase the tempo" or "add drums" and executes the corresponding operations. The interface unit may also be equipped with a voice recognition engine to enable operations to be performed by voice commands. For example, the interface unit may recognize commands such as "move to the next track" or "apply effects" and execute the corresponding operations. The interface unit may also use the voice recognition function to enable a user to create music without using their hands. For example, the interface unit may recognize voice commands such as "start playback" or "stop recording" and execute the corresponding operations. This allows operations to be performed by voice commands.

[0060] The interface unit can use the emotion estimation function to customize the interface according to the emotional state of the user. The interface unit, for example, uses the emotion estimation function to analyze the emotional state of the user in real time and change the design and color scheme of the interface. For example, if the user is relaxed, it provides an interface with calm colors. The interface unit also dynamically changes the functional layout of the interface according to the emotional state of the user. For example, if the user is feeling stressed, it provides a simple and intuitive interface. The interface unit also uses the emotion estimation function to customize the interface according to the emotional state of the user. For example, if the user is excited, it provides an interface with an energetic design. In this way, the interface is customized according to the emotional state of the user.

[0061] The interface unit can add haptic feedback to improve the feel of operation. For example, the interface unit adds a haptic feedback function to the interface so that the user feels vibration or pressure when performing an operation. For example, feeling a slight vibration when pressing a button improves the feel of operation. The interface unit also uses haptic feedback to enhance the sensation when the user performs an operation. For example, feeling resistance when moving a slider allows for more intuitive operation. The interface unit is also equipped with a haptic feedback function so that the user can receive feedback in real time when performing an operation. For example, feeling vibration when dragging and dropping musical parts improves the accuracy of operation. This adds haptic feedback and improves the feel of operation.

[0062] The interface unit can be combined with AR technology and linked to a physical instrument. The interface unit, for example, uses AR technology to build a system that links the interface with a physical instrument. For example, by wearing an AR headset, a display that integrates the physical instrument and the digital interface is provided. The interface unit also develops an AR interface that links with a physical instrument, allowing the user to perform digital operations while playing the instrument. For example, adjusting effects with the AR interface while playing the guitar. The interface unit also uses AR technology to integrate the physical instrument and the digital interface, providing an environment in which the user can intuitively operate it. For example, overlaying the AR interface on the piano keyboard allows music to be created in real time. This allows the interface to be combined with AR technology and linked to a physical instrument.

[0063] The interface unit can use the emotion estimation function to propose an interface design that will allow the user to feel the most relaxed. For example, the interface unit uses the emotion estimation function to analyze the user's emotional state and propose a relaxing interface design. For example, if the user is feeling stressed, it provides a design with calming colors. The interface unit also automatically generates an interface design that has a relaxing effect based on the user's emotional state. For example, it uses a natural landscape or calming music as the background. The interface unit also uses the emotion estimation function to build a system that proposes an interface design that will allow the user to feel the most relaxed. For example, it provides an optimal design in real time based on the user's emotional data. This proposes an interface design that will allow the user to feel the most relaxed.

[0064] The generation unit can learn the user's past music production data and provide a music generation algorithm optimized for each individual user. For example, the generation unit collects the user's past music production data, and the generation AI learns that data. For example, the generation unit analyzes the style and patterns of songs the user has created in the past and provides an algorithm optimized for each individual user. The generation unit also learns the user's preferences and production style based on the past music production data, and the generation AI generates optimal songs. For example, it automatically generates songs that reflect the user's preferred rhythms and melodies. The generation unit also analyzes the user's past production data, and the generation AI provides an optimal music generation algorithm based on that data. For example, it generates songs that reflect the instruments and effects the user frequently uses. This provides a music generation algorithm optimized based on the user's past music production data.

[0065] The generation unit can incorporate natural sounds and environmental sounds into the music generation algorithm, enabling a wider variety of musical expressions. For example, the generation unit realizes a wider variety of musical expressions by incorporating natural sounds and environmental sounds into the music generation algorithm. For example, the generation unit incorporates birdsong and the sound of waves into a song. The generation unit also incorporates natural sounds and environmental sounds into the music generation algorithm, allowing the user to create unique musical expressions. For example, the sound of wind or rain is used in the background. The generation unit also incorporates natural sounds and environmental sounds into the music generation algorithm, allowing the user to create new musical styles. For example, the sound of a city bustle or the sound of a forest is incorporated into a song. In this way, incorporating natural sounds and environmental sounds enables a wider variety of musical expressions.

[0066] The generation unit can use the emotion estimation function to generate music according to the user's emotions. For example, the generation unit uses the emotion estimation function to analyze the user's emotional state and generate music according to that emotion. For example, if the user is relaxed, it generates music with a calm melody. The generation unit also builds a system in which the generation AI generates optimal music based on the user's emotional state. For example, if the user is excited, it generates music with an energetic rhythm. The generation unit also uses the emotion estimation function to develop an algorithm for generating music according to the user's emotions. For example, if the user is sad, it generates music that soothes the emotions. In this way, music is generated according to the user's emotions.

[0067] The generation unit can apply the music generation algorithm to creative fields. For example, the generation unit applies the music generation algorithm to video production to build a system that automatically generates music that matches the content of the video. For example, it generates background music that matches a movie scene. The generation unit also applies the music generation algorithm to game development to generate music in real time that matches the progress of the game. For example, it automatically generates music that matches the game's stages and events. The generation unit also applies the music generation algorithm to other creative fields to enable users to create a variety of content. For example, it generates music that matches animation or advertising videos. This allows the music generation algorithm to be applied to other creative fields.

[0068] The generation unit can open-source the music generation algorithm and promote improvements by the community. For example, the generation unit open-sources the music generation algorithm, making it freely accessible to the developer community. For example, the generation unit publishes the algorithm on a platform such as GitHub. To promote improvements by the community, the generation unit also publishes the source code of the music generation algorithm, allowing developers to make their own improvements. For example, new musical styles or functions can be added. The generation unit also allows the community to jointly develop the open-source music generation algorithm and improve its performance. For example, bug fixes and optimizations can be performed. In this way, the music generation algorithm is open-sourced and improvements by the community can be promoted.

[0069] The generation unit can use the emotion estimation function to automatically generate the music pattern that most moves the user. For example, the generation unit uses the emotion estimation function to analyze the user's emotional state and automatically generate the most moving music pattern. For example, it generates melodies and harmonies that move the user. The generation unit also builds a system in which a generation AI generates optimal music patterns based on the user's emotional data. For example, it automatically generates music that matches an emotional scene. The generation unit also uses the emotion estimation function to develop an algorithm that automatically generates the music pattern that most moves the user. For example, it analyzes the user's emotional response in real time and generates optimal music. In this way, the music pattern that most moves the user is automatically generated.

[0070] The style support unit can learn the historical background and cultural elements of musical genres, enabling deeper expression. For example, the style support unit learns the historical background and cultural elements of each musical genre, and the generation AI generates music based on that knowledge. For example, the unit learns the history and culture of blues and reflects blues-specific expressions in the music. The style support unit also learns the cultural elements of musical genres, and the generation AI generates music that incorporates those elements. For example, the unit learns Latin music rhythms and instruments and generates music unique to Latin music. The style support unit also learns the historical background of each musical genre, and the generation AI generates music with deeper expression based on that knowledge. For example, the unit learns classical music composers and historical background and generates music unique to classical music. This allows the generation AI to learn the historical background and cultural elements of each musical genre, enabling deeper expression.

[0071] The style support unit can generate a new musical style by fusing multiple genres specified by the user. For example, the style support unit fuses multiple musical genres specified by the user, and the generation AI generates a new musical style. For example, it generates a song that fuses jazz and electronica. The style support unit also combines multiple musical genres, allowing the generation AI to create a unique musical style. For example, it generates a song that fuses rock and classical. The style support unit also builds a system in which the generation AI generates a new musical style based on the genres specified by the user. For example, it generates a song that fuses pop and hip hop. In this way, a new musical style is generated by fusing multiple genres specified by the user.

[0072] The style correspondence unit can use the emotion estimation function to suggest a music genre that best suits the user's emotion. For example, the style correspondence unit uses the emotion estimation function to analyze the user's emotional state and suggest a music genre that best suits that emotion. For example, if the user is relaxed, it suggests classical or ambient music. The style correspondence unit also builds a system in which a generative AI suggests the optimal music genre based on the user's emotional state. For example, if the user is excited, it suggests rock or electronica. The style correspondence unit also uses the emotion estimation function to develop an algorithm that suggests a music genre that best suits the user's emotion. For example, if the user is sad, it suggests blues or ballads. This suggests a music genre that best suits the user's emotion.

[0073] The style support unit can expand the database of musical styles and genres, enabling the generation of more diverse music. For example, the style support unit expands the database of musical styles and genres, enabling the generation AI to generate more diverse music. For example, new musical genres and subgenres are added to the database. By expanding the database, the style support unit also enables the generation AI to learn various musical styles and generate music that suits the user's preferences. For example, ethnic music and world music are added to the database. The style support unit also expands the database of musical styles and genres, enabling the generation AI to express more diverse music. For example, data on classical music and contemporary music is added to support a wider range of genres. This expands the database of musical styles and genres, enabling the generation AI to generate more diverse music.

[0074] The style support unit can collaborate with experts in different music genres to strengthen the learning data of the generative AI. For example, the style support unit collaborates with experts in different music genres to strengthen the learning data of the generative AI. For example, by collaborating with a jazz expert, it can learn expressions unique to jazz. The style support unit also collaborates with experts in music genres to expand the learning data of the generative AI, enabling it to generate a wider variety of music. For example, by collaborating with a classical music expert, it can add classical music data. The style support unit also collaborates with experts in different genres to enable the generative AI to learn a variety of musical styles. For example, by collaborating with experts in rock and pop, it can collect data from a wide range of genres. In this way, collaboration with experts in different music genres strengthens the learning data of the generative AI.

[0075] The style correspondence unit can use the emotion estimation function to automatically select the music genre that will most relax the user. For example, the style correspondence unit uses the emotion estimation function to analyze the user's emotional state and automatically select the most relaxing music genre. For example, if the user is feeling stressed, ambient or classical music is selected. The style correspondence unit also builds a system in which the generation AI automatically selects the optimal music genre based on the user's emotional state. For example, if the user wants to relax, jazz or bossa nova is selected. The style correspondence unit also uses the emotion estimation function to develop an algorithm that automatically selects the music genre that will most relax the user. For example, the optimal genre is selected in real time based on the user's emotional data. This automatically selects the music genre that will most relax the user.

[0076] The real-time generation unit further accelerates the response speed of the generation AI to user operations, providing a smoother production experience. For example, the real-time generation unit accelerates the response speed of the generation AI and builds a system that responds immediately to user operations. For example, when a user adds an effect, the effect is instantly reflected. The real-time generation unit also optimizes the processing speed of the generation AI for real-time music generation, allowing users to operate it smoothly. For example, adjustments to tempo and volume are reflected in real time. The real-time generation unit also improves the response speed of the generation AI and develops a system in which music is updated instantly every time a user operates it. For example, adding or deleting instruments is reflected in real time. This further accelerates the response speed of the generation AI to user operations, providing a smoother production experience.

[0077] The real-time generation unit can add a function to emphasize specific musical elements specified by the user during real-time generation. The real-time generation unit adds a function to emphasize specific musical elements specified by the user during real-time generation. For example, the real-time generation unit may highlight a drum part to emphasize the rhythm. The real-time generation unit also provides a function to emphasize specific musical elements when the user generates music in real time. For example, the real-time generation unit may highlight a vocal part to emphasize the melody. The real-time generation unit also builds a system to emphasize musical elements specified by the user during real-time generation. For example, the real-time generation unit may strengthen the low range to emphasize the bass line. This adds a function to emphasize specific musical elements specified by the user during real-time generation.

[0078] The real-time generation unit can use the emotion estimation function to generate music in real time according to the user's emotions. For example, the real-time generation unit uses the emotion estimation function to analyze the user's emotional state in real time and generate music according to that emotion. For example, if the user is relaxed, it generates music with a calm melody. The real-time generation unit also builds a system in which the generation AI generates optimal music in real time based on the user's emotional state. For example, if the user is excited, it generates music with an energetic rhythm. The real-time generation unit also uses the emotion estimation function to develop an algorithm for generating music in real time according to the user's emotions. For example, if the user is sad, it generates music that soothes the emotions. In this way, music is generated in real time according to the user's emotions.

[0079] The real-time generation unit can apply the real-time generation function to live performances to support improvisation. For example, the real-time generation unit applies the real-time generation function to live performances to build a system that supports improvisation. For example, a generation AI generates music in real time during a live performance, allowing performers to improvise. The real-time generation unit also generates music in real time during live performances, allowing performers to improvise on the spot. For example, music is generated based on the audience's reactions. The real-time generation unit also applies the real-time generation function to live performances to develop a system that allows performers to improvise music. For example, a generation AI generates music based on a theme specified by the performer. In this way, the real-time generation function is applied to live performances to support improvisation.

[0080] The real-time generation unit can add a function that allows collaboration with other users during real-time generation. The real-time generation unit, for example, builds a system that adds a function that allows collaboration with other users during real-time generation. For example, multiple users simultaneously generate music and share it in real time. The real-time generation unit also provides a function that allows music to be generated while collaborating with other users in real time. For example, remotely connected users jointly create music. The real-time generation unit also adds a function that allows collaboration with other users during real-time generation, developing a system for jointly generating music. For example, users share parts to generate music. This adds a function that allows collaboration with other users during real-time generation.

[0081] The real-time generation unit can use the emotion estimation function to generate music that matches the moment when the user is most excited. For example, the real-time generation unit uses the emotion estimation function to analyze the user's emotional state in real time and generate music that matches the moment when the user is most excited. For example, if the user is excited, it generates music with an energetic rhythm. The real-time generation unit also builds a system in which a generation AI generates optimal music in real time based on the user's emotional state. For example, it generates exciting music that matches the moment when the user is excited. The real-time generation unit also uses the emotion estimation function to develop an algorithm that generates music that matches the moment when the user is most excited. For example, it analyzes the user's emotional response in real time and generates optimal music. In this way, music is generated that matches the moment when the user is most excited.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The interface unit can learn user operation history and automatically generate an interface optimized for each individual user. For example, it can collect user operation history and analyze frequently used functions and operation patterns. For users who frequently use specific effects or tempo adjustments, it can place those functions in prominent positions in the interface. It can also automatically set frequently used functions as shortcuts based on the operation history. For users who frequently use drag-and-drop to arrange musical parts, it can provide shortcuts that make that operation easy. It can also learn operation history and dynamically change the interface layout. For users who frequently use specific functions, it can place those functions at the top of the interface for easier access. This makes it possible to provide an interface that is optimized based on the user's operation history.

[0084] The interface unit can be added with a voice recognition function, allowing operations to be performed using voice commands. For example, voice recognition technology can be used to enable users to create and edit music using voice commands. For example, voice commands such as "increase the tempo" or "add drums" are recognized and the corresponding operations are performed. In addition, a voice recognition engine can be installed to enable operation of the interface using voice commands. For example, commands such as "move to the next track" or "apply effects" are recognized and the interface is operated. Furthermore, the voice recognition function can be used to enable users to create music without using their hands. For example, voice commands such as "start playback" or "stop recording" are recognized and the corresponding operations are performed. This allows operations to be performed using voice commands.

[0085] The interface unit can use the emotion estimation function to customize the interface according to the emotional state of the user. For example, the emotion estimation function is used to analyze the user's emotional state in real time and change the design and color scheme of the interface. If the user is relaxed, an interface with calm colors is provided. Furthermore, the functional layout of the interface is dynamically changed according to the user's emotional state. If the user is feeling stressed, a simple and intuitive interface is provided. Furthermore, the emotion estimation function is used to customize the interface according to the user's emotional state. If the user is excited, an interface with an energetic design is provided. In this way, the interface can be customized according to the user's emotional state.

[0086] The interface unit can add haptic feedback to improve the feel of operation. For example, a haptic feedback function can be added to the interface so that the user feels vibrations or pressure when performing operations. Feeling a slight vibration when pressing a button improves the feel of operation. Haptic feedback can also be used to enhance the sense when the user performs operations. Feeling resistance when moving a slider allows for more intuitive operation. Furthermore, a haptic feedback function can be installed so that the user can receive feedback in real time when performing operations. Feeling vibrations when dragging and dropping musical parts improves the precision of operation. This adds haptic feedback and improves the feel of operation.

[0087] The interface unit can be combined with AR technology and linked to a physical instrument. For example, a system can be built using AR technology to link the interface with a physical instrument. By wearing an AR headset, a display that integrates the physical instrument and the digital interface is provided. An AR interface that links with a physical instrument can also be developed, allowing users to perform digital operations while playing the instrument. For example, adjusting effects with the AR interface while playing the guitar. Furthermore, AR technology can be used to integrate the physical instrument and the digital interface, providing an environment in which users can operate intuitively. The AR interface can be overlaid on the piano keys to create music in real time. This allows the system to be combined with AR technology and linked to a physical instrument.

[0088] The interface unit can use the emotion estimation function to propose an interface design that will help the user to relax the most. For example, the emotion estimation function can be used to analyze the user's emotional state and propose a relaxing interface design. If the user is feeling stressed, a design with calming colors can be provided. Furthermore, an interface design with a relaxing effect can be automatically generated based on the user's emotional state, using natural scenery or calming music as the background. Furthermore, a system can be built that uses the emotion estimation function to propose an interface design that will help the user to relax the most. The optimal design is provided in real time based on the user's emotional data. This makes it possible to propose an interface design that will help the user to relax the most.

[0089] The generation unit can learn from a user's past music production data and provide a music generation algorithm optimized for each individual user. For example, the generation AI collects a user's past music production data and learns from that data. The AI ​​analyzes the style and patterns of songs the user has created in the past and provides an algorithm optimized for each individual user. The AI ​​also learns the user's preferences and production style based on the past music production data and generates optimal songs. Songs that reflect the user's preferred rhythms and melodies are automatically generated. The AI ​​also analyzes the user's past production data and provides an optimal music generation algorithm based on that data. Songs are generated that reflect the instruments and effects the user frequently uses. This makes it possible to provide a music generation algorithm optimized based on the user's past music production data.

[0090] The generation unit can incorporate natural and environmental sounds into the music generation algorithm, enabling a wider variety of musical expressions. For example, incorporating natural and environmental sounds into the music generation algorithm enables a wider variety of musical expressions. Birdsong and the sound of waves can be incorporated into a song. Furthermore, incorporating natural and environmental sounds into the music generation algorithm allows users to create their own unique musical expressions. The sound of wind or rain can be used in the background. Furthermore, incorporating natural and environmental sounds into the music generation algorithm allows users to create new musical styles. The sound of a city bustle or the sound of a forest can be incorporated into a song. In this way, incorporating natural and environmental sounds enables a wider variety of musical expressions.

[0091] The generation unit can use the emotion estimation function to generate music according to the user's emotions. For example, the emotion estimation function can be used to analyze the user's emotional state and generate music according to that emotion. If the user is relaxed, a song with a calm melody can be generated. We also build a system in which the generation AI generates optimal music based on the user's emotional state. If the user is excited, a song with an energetic rhythm can be generated. Furthermore, we develop an algorithm that uses the emotion estimation function to generate music according to the user's emotions. If the user is sad, a song that soothes the emotions can be generated. This makes it possible to generate music according to the user's emotions.

[0092] The generation unit can apply the music generation algorithm to creative fields. For example, the music generation algorithm can be applied to video production to build a system that automatically generates music that matches the content of the video. BGM can be generated to match movie scenes. The music generation algorithm can also be applied to game development to generate music in real time that matches the progress of the game. Music can be automatically generated to match the game's stages and events. Furthermore, the music generation algorithm can be applied to other creative fields, allowing users to create a variety of content. Music can be generated to match animations and advertising videos. This allows the music generation algorithm to be applied to other creative fields.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The interface utilizes the user's sensibilities to create music. For example, users can arrange musical parts using drag and drop, and adjust the volume and tempo by moving sliders. The interface is also designed so that even beginners can quickly become familiar with it. Step 2: The generation unit generates music based on the instructions input through the interface unit. For example, if the user instructs the AI ​​to "create a jazz-style song," the AI ​​analyzes the instruction and generates a jazz-style song. The AI ​​then generates music based on the user's instructions. Step 3: The style support unit adapts the music generated by the generation unit to various musical styles and genres. For example, it can generate music that suits the user's preferences, such as pop, rock, classical, or electronica. The style support unit also learns the characteristics of each genre and generates appropriate music based on the user's instructions. Step 4: The real-time generation unit generates music in real time according to the style support unit. For example, every time the user performs an operation, the generation AI responds immediately, generating and updating the music. The real-time generation unit also allows the user to instantly complete a song while enjoying the production process.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0161] 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]

[0162] 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. The interface section uses the user's sensibilities to create music, a generation unit that generates music based on instructions input via the interface unit; a style matching unit that matches the music generated by the generation unit with various music styles and genres; a real-time generating unit that generates music in real time according to the style correspondence unit. A system characterized by:

2. The interface unit The operation history of the user is learned, and an interface optimized for each individual user is automatically generated.

2. The system of claim 1.

3. The interface unit Add voice recognition functionality to allow operations to be performed using voice commands 2. The system of claim 1.

4. The interface unit Customizing the interface according to the emotional state of the user 2. The system of claim 1.

5. The interface unit Add haptic feedback to improve the experience 2. The system of claim 1.

6. The interface unit Combined with the AR technology, it will link with physical instruments.

2. The system of claim 1.

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