system

Abstract

An object of a system according to an embodiment is to provide a basic music theory and a trend tendency to a user who learns composition, and to support composition in cooperation with a DTM tool.SOLUTION: A system according to an embodiment includes a generation AI, a music theory providing unit, a trend providing unit, a DTM tool cooperation unit, a demonstration support unit, and a theme support unit. The music theory provider provides basic music theory necessary for the user to learn to compose music. The trend trend providing unit analyzes trends and statistical data currently prevailing in the music industry, and provides the results to the user. The DTM tool cooperation unit operates in cooperation with a DTM tool such as a garage band or a logic pro. The demonstration support part supports learning while performing each step of composition. The theme support unit supports composition based on a specific theme.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] With conventional technology, it was difficult to understand basic music theory and current trends when learning composition, and there were issues with insufficient integration with DTM tools.

[0005] The system according to the embodiment aims to provide basic music theory and trends to users who are learning to compose music, and to support composition in cooperation with DTM tools. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a music theory providing unit, a trend trend providing unit, a DTM tool linking unit, a performance support unit, and a theme support unit. The music theory providing unit provides basic music theory necessary for users to learn composition. The trend trend providing unit analyzes current trends and statistical data in the music industry and provides them to users. The DTM tool linking unit works in conjunction with DTM tools such as GarageBand and Logic Pro. The performance support unit supports learning by providing demonstrations for each step of composition. The theme support unit supports composition based on a specific theme. [Effects of the Invention]

[0007] The system according to the embodiment can provide basic music theory and trends to users who are learning to compose music, and can support composition in cooperation with DTM tools. [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 composition support system according to the embodiment of the present invention is a system that utilizes a generative AI to enable users to actually compose music while learning basic music theory, trends in fashion, and statistics. This allows users to learn basic music theory, understand trends in fashion and statistics, and actually compose music.

[0029] A composition support system according to an embodiment includes a generation AI, a music theory provider, a trend trend provider, a DTM tool linkage unit, a performance support unit, and a theme support unit. The generation AI includes a music theory provider that provides basic music theory necessary for users to learn composition. For example, the music theory provider explains basic concepts such as chord progressions, scales, and rhythm patterns, providing specific examples to help users understand. The music theory provider can also analyze a user's past learning history and provide an individually customized music theory study plan. For example, it can suggest the next topic to learn based on the user's past learning and level of understanding. Furthermore, the music theory provider can analyze a user's performance in real time and provide instant feedback on mistakes and areas for improvement. For example, it can point out deviations in rhythm or pitch during performance and show the correct way to play. The trend trend provider analyzes current trends and statistical data in the music industry and provides them to the user. For example, the trend trend provider can analyze data from social media and music streaming services to provide the latest trend information in real time. The trend trend provider can also analyze past music trends and develop algorithms to predict future trends. The DTM tool integration unit works in conjunction with DTM tools such as GarageBand and Logic Pro. For example, the DTM tool integration unit analyzes user operations within the DTM tool in real time and suggests optimal operating procedures and settings. The DTM tool integration unit can also learn the user's composition style and provide individually customized DTM tool presets. The performance support unit supports learning by providing demonstrations at each step of composition. For example, the performance support unit analyzes the user's progress in real time and dynamically suggests the next step to learn. The performance support unit can also test the user's understanding at each step and provide focused explanations on areas where understanding is lacking. The theme support unit supports composition based on a specific theme. For example, the theme support unit analyzes the user's past composition history and suggests a composition style based on a specific theme. When providing composition support for a theme, the theme support unit can also simultaneously explain related music theory and historical background.As a result, the composition support system according to the embodiment allows users to learn basic music theory, grasp trends and statistics, and actually compose music. For example, even beginners can easily start composing and acquire the skills necessary to create professional-level music. Furthermore, by linking with DTM tools, the actual composition process progresses smoothly, allowing for efficient learning.

[0030] The music theory providing unit can analyze the user's past learning history and provide an individually customized music theory learning plan. For example, the generation AI in the music theory providing unit analyzes the user's past learning history and provides an individually customized music theory learning plan. For example, the generation AI suggests the next topic to learn based on the content the user has learned in the past and their level of understanding. The music theory providing unit also provides individually customized practice problems and assignments based on the user's learning history. For example, this allows the user to focus on learning parts of music theory that the user finds difficult. The music theory providing unit also analyzes the user's learning history and provides feedback according to the user's progress. For example, the generation AI instructs the user to proceed to the next step when they have reached a certain level of understanding. This makes it possible to provide an individually customized learning plan based on the user's learning history.

[0031] The music theory provider can analyze the user's performance in real time and provide instant feedback on mistakes and areas for improvement. For example, the generative AI in the music theory provider can analyze the user's performance in real time and provide instant feedback on mistakes and areas for improvement. For example, it can point out discrepancies in rhythm or pitch during performance and show the correct way to play. The music theory provider can also analyze the user's performance in real time, and the generative AI can suggest specific areas for improvement. For example, it can provide advice on adjusting finger movement or hand position. The music theory provider can also analyze the user's performance in real time and suggest practice methods that will help improve performance skills. For example, it can instruct the user to repeatedly practice a specific phrase. This allows the user's performance to be analyzed in real time and instant feedback to be provided.

[0032] The trend trend providing unit can analyze data from social media and music streaming services to provide the latest trend information in real time. For example, the generation AI in the trend trend providing unit analyzes social media data to provide the latest trend information in real time. For example, it analyzes posts on Twitter and Instagram to identify popular genres and artists. The trend trend providing unit also analyzes data from music streaming services to provide the latest trend information in real time. For example, it analyzes the number of plays and playlists on Spotify and Apple Music. The trend trend providing unit also integrates data from social media and music streaming services to provide the latest trend information in real time. For example, it predicts trends based on both data. This allows the generation AI to analyze data from social media and music streaming services to provide the latest trend information in real time.

[0033] The trend trend providing unit can analyze past music trends and develop an algorithm that predicts future trends. For example, the generation AI in the trend trend providing unit analyzes past music trends and develops an algorithm that predicts future trends. For example, the unit analyzes the characteristics of past hit songs and predicts the next genre that is likely to become popular. The trend trend providing unit also develops an algorithm that uses past music trend data to predict future trends with the generation AI. For example, the unit uses past data to predict seasonal trends. The trend trend providing unit also builds a model that uses the generation AI to analyze past music trends and predict future trends. For example, machine learning is used to predict future trends from past data. This makes it possible to develop an algorithm that analyzes past music trends and predicts future trends.

[0034] The DTM tool integration unit can analyze user operations within the DTM tool in real time and propose optimal operating procedures and settings. For example, the DTM tool integration unit's generation AI can analyze user operations within the DTM tool in real time and propose optimal operating procedures and settings. For example, it can provide advice on effect settings and mixing procedures. The DTM tool integration unit also uses the generation AI to propose optimal operating procedures based on the user's operation history. For example, it can analyze past operation patterns and provide an efficient workflow. The DTM tool integration unit also uses the generation AI to analyze user operations within the DTM tool in real time and display alerts to prevent operating errors. For example, it can detect and warn of setting errors or inappropriate operations. This allows the DTM tool integration unit to analyze user operations within the DTM tool in real time and propose optimal operating procedures and settings.

[0035] The DTM tool collaboration unit can learn the user's composition style and provide individually customized DTM tool presets. In the DTM tool collaboration unit, for example, the generation AI learns the user's composition style and provides individually customized DTM tool presets. For example, it suggests tone and effect settings that match the user's preferences. The DTM tool collaboration unit also provides customized presets based on the user's composition history. For example, it creates new presets based on previously used tones and effects. The DTM tool collaboration unit also learns the user's composition style and provides customized presets in real time. For example, it automatically sets the optimal tones and effects while composing. This makes it possible to learn the user's composition style and provide individually customized DTM tool presets.

[0036] The demonstration support unit can analyze the user's progress in real time and dynamically suggest the next step to learn. For example, the demonstration support unit uses a generation AI to analyze the user's progress in real time and dynamically suggest the next step to learn. For example, it instructs the user to proceed to the next step when they have reached a certain level of understanding. The demonstration support unit also uses a generation AI to suggest the next step to learn based on the user's progress. For example, it allows the user to focus on learning areas in which they are weak. The demonstration support unit also uses a generation AI to analyze the user's progress in real time and dynamically adjust the learning plan. For example, it customizes the learning content according to the user's level of understanding. This allows the user's progress to be analyzed in real time and dynamically suggest the next step to learn.

[0037] The demonstration support unit tests the user's level of understanding at each step and can provide focused explanations on areas where understanding is insufficient. For example, the generation AI tests the user's level of understanding at each step and provides focused explanations on areas where understanding is insufficient. For example, it measures the level of understanding using quizzes or tests. The demonstration support unit also determines the content on which the generation AI will focus its explanations based on the user's level of understanding. For example, it provides detailed explanations on areas where the user does not understand. The demonstration support unit also tests the user's level of understanding at each step and provides additional practice questions or assignments for areas where understanding is insufficient. For example, it has the user study repeatedly until they understand. This allows the generation AI to test the user's level of understanding at each step and provide focused explanations on areas where understanding is insufficient.

[0038] The theme support unit can analyze the user's past composition history and suggest a composition style based on a specific theme. In the theme support unit, for example, the generation AI analyzes the user's past composition history and suggests a composition style based on a specific theme. For example, it suggests a new composition style based on the characteristics of songs the user has created in the past. In addition, the theme support unit can use the generation AI to suggest a composition style suitable for a specific theme based on the user's composition history. For example, it can suggest melodies and rhythm patterns that match the theme selected by the user. In addition, the theme support unit can use the generation AI to analyze the user's past composition history and suggest a composition style based on a specific theme. For example, it can provide new composition ideas based on the analysis of songs the user has created in the past. In this way, the user's past composition history can be analyzed and a composition style based on a specific theme can be suggested.

[0039] The theme support unit can simultaneously explain related music theory and historical background when providing theme-specific composition support. For example, when the generation AI provides theme-specific composition support, the theme support unit simultaneously explains related music theory. For example, it explains chord progressions and scales that are appropriate for a particular theme. In addition, when the generation AI provides theme-specific composition support, the theme support unit simultaneously explains historical background. For example, it explains the history and development process of a particular music genre. In addition, when the generation AI provides theme-specific composition support, the theme support unit simultaneously explains related music theory and historical background. For example, it introduces famous composers and pieces related to the theme. This allows for simultaneous explanations of related music theory and historical background when providing theme-specific composition support.

[0040] The theme support unit can introduce other artists and works related to the theme selected by the user when providing theme-specific composition support. For example, when the generation AI provides theme-specific composition support, the theme support unit introduces other artists and works related to the theme selected by the user. For example, it introduces famous artists and songs that have composed on the same theme. The theme support unit also provides information that the generation AI can use as reference for composing, based on artists and works related to the theme selected by the user. For example, it analyzes and explains related songs. Furthermore, when the generation AI provides theme-specific composition support, the theme support unit introduces other artists and works related to the theme selected by the user and provides advice on how to gain inspiration from those works. In this way, it is possible to introduce other artists and works related to the theme selected by the user when providing theme-specific composition support.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The composition support system can further include a health management unit that monitors the user's health condition. For example, the health management unit may measure the user's heart rate and stress level and suggest appropriate times to take a break. The health management unit can also suggest the optimal working environment while composing based on the user's health data. For example, it may provide advice on appropriate lighting and volume settings. Furthermore, the health management unit can suggest relaxation methods according to the user's health condition. For example, it may show how to take deep breaths or stretch. In this way, the user's health condition can be monitored and health management during composition can be supported.

[0043] The composition support system may further include an inspiration providing unit to stimulate the user's creativity. For example, the inspiration providing unit may introduce new music genres and artists based on the user's past composition history and preferences. The inspiration providing unit may also provide visuals and stories related to themes that interest the user, such as movie scenes or passages from literary works. The inspiration providing unit may also suggest creative workshops and exercises to help the user gain new ideas. This may stimulate the user's creativity and support the generation of new composition ideas.

[0044] The composition support system may further include a community collaboration unit to strengthen the user's social connections. For example, the community collaboration unit may provide an online forum or chat room where the user can interact with other composers and music lovers. The community collaboration unit may also provide a platform for users to share their compositions and receive feedback. Furthermore, the community collaboration unit may host online events or workshops in which users can participate. This may strengthen the user's social connections and further enrich the user's composition activities.

[0045] The composition support system may further include a skill improvement unit for improving the user's composition skills. For example, the skill improvement unit may analyze the user's past composition history and provide a practice plan for improving a specific skill. The skill improvement unit may also provide exercises and assignments tailored to the user's composition style. Furthermore, the skill improvement unit may suggest the next skill to learn based on the user's progress. This allows for effective support in improving the user's composition skills.

[0046] The composition support system may further include an automation support unit to streamline the user's composition activities. For example, the automation support unit may analyze the user's composition patterns and suggest ways to automate repetitive tasks. The automation support unit may also provide automation tools and plug-ins tailored to the user's composition style. Furthermore, the automation support unit may analyze the user's composition process and suggest efficient workflows. This may improve the efficiency of the user's composition activities, allowing them to spend more time on creative work.

[0047] The composition support system may further include a resource provider to support the user's composition activities. For example, the resource provider may provide materials related to music theory and composition techniques that the user needs. The resource provider may also provide information on songs and artists that the user can refer to. Furthermore, the resource provider may introduce online courses and workshops that the user can participate in. This provides a wealth of resources to support the user's composition activities and promotes learning and growth.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The music theory provider provides the user with the basic music theory they need to learn composition. For example, it explains basic concepts such as chord progressions, scales, and rhythmic patterns, and provides specific examples. It can also analyze the user's past learning history and provide an individually customized music theory learning plan. Furthermore, it analyzes the user's performance in real time and provides immediate feedback on mistakes and areas for improvement. Step 2: The trend trend provider analyzes current trends and statistical data in the music industry and provides them to users. For example, it analyzes data from social media and music streaming services to provide the latest trend data in real time. It can also analyze past music trends and develop algorithms to predict future trends. Step 3: The DTM tool integration section works in conjunction with DTM tools such as GarageBand and Logic Pro. For example, it analyzes the user's operations in real time within the DTM tool and suggests optimal operation procedures and settings. It can also learn the user's composition style and provide individually customized presets for the DTM tool. Step 4: The performance support unit supports learning by demonstrating each step of composition. For example, it analyzes the user's progress in real time and dynamically suggests the next step to learn. It can also test the user's level of understanding at each step and provide focused explanations on areas where they have insufficient understanding. Step 5: The theme support unit assists with composing music based on a specific theme. For example, it analyzes the user's past composition history and suggests a composition style based on a specific theme. When providing composition support for each theme, it can also simultaneously explain related music theory and historical background.

[0050] (Example 2) The composition support system according to the embodiment of the present invention is a system that utilizes a generative AI to enable users to actually compose music while learning basic music theory, trends in fashion, and statistics. This allows users to learn basic music theory, understand trends in fashion and statistics, and actually compose music.

[0051] A composition support system according to an embodiment includes a generation AI, a music theory provider, a trend trend provider, a DTM tool linkage unit, a performance support unit, and a theme support unit. The generation AI includes a music theory provider that provides basic music theory necessary for users to learn composition. For example, the music theory provider explains basic concepts such as chord progressions, scales, and rhythm patterns, providing specific examples to help users understand. The music theory provider can also analyze a user's past learning history and provide an individually customized music theory study plan. For example, it can suggest the next topic to learn based on the user's past learning and level of understanding. Furthermore, the music theory provider can analyze a user's performance in real time and provide instant feedback on mistakes and areas for improvement. For example, it can point out deviations in rhythm or pitch during performance and show the correct way to play. The trend trend provider analyzes current trends and statistical data in the music industry and provides them to the user. For example, the trend trend provider can analyze data from social media and music streaming services to provide the latest trend information in real time. The trend trend provider can also analyze past music trends and develop algorithms to predict future trends. The DTM tool integration unit works in conjunction with DTM tools such as GarageBand and Logic Pro. For example, the DTM tool integration unit analyzes user operations within the DTM tool in real time and suggests optimal operating procedures and settings. The DTM tool integration unit can also learn the user's composition style and provide individually customized DTM tool presets. The performance support unit supports learning by providing demonstrations at each step of composition. For example, the performance support unit analyzes the user's progress in real time and dynamically suggests the next step to learn. The performance support unit can also test the user's understanding at each step and provide focused explanations on areas where understanding is lacking. The theme support unit supports composition based on a specific theme. For example, the theme support unit analyzes the user's past composition history and suggests a composition style based on a specific theme. When providing composition support for a theme, the theme support unit can also simultaneously explain related music theory and historical background.As a result, the composition support system according to the embodiment allows users to learn basic music theory, grasp trends and statistics, and actually compose music. For example, even beginners can easily start composing and acquire the skills necessary to create professional-level music. Furthermore, by linking with DTM tools, the actual composition process progresses smoothly, allowing for efficient learning.

[0052] The music theory providing unit can analyze the user's past learning history and provide an individually customized music theory learning plan. For example, the generation AI in the music theory providing unit analyzes the user's past learning history and provides an individually customized music theory learning plan. For example, the generation AI suggests the next topic to learn based on the content the user has learned in the past and their level of understanding. The music theory providing unit also provides individually customized practice problems and assignments based on the user's learning history. For example, this allows the user to focus on learning parts of music theory that the user finds difficult. The music theory providing unit also analyzes the user's learning history and provides feedback according to the user's progress. For example, the generation AI instructs the user to proceed to the next step when they have reached a certain level of understanding. This makes it possible to provide an individually customized learning plan based on the user's learning history.

[0053] The music theory provider can analyze the user's performance in real time and provide instant feedback on mistakes and areas for improvement. For example, the generative AI in the music theory provider can analyze the user's performance in real time and provide instant feedback on mistakes and areas for improvement. For example, it can point out discrepancies in rhythm or pitch during performance and show the correct way to play. The music theory provider can also analyze the user's performance in real time, and the generative AI can suggest specific areas for improvement. For example, it can provide advice on adjusting finger movement or hand position. The music theory provider can also analyze the user's performance in real time and suggest practice methods that will help improve performance skills. For example, it can instruct the user to repeatedly practice a specific phrase. This allows the user's performance to be analyzed in real time and instant feedback to be provided.

[0054] The music theory providing unit can use the emotion estimation function to analyze the user's emotions while studying and provide encouragement and advice to maintain motivation. For example, the music theory providing unit can use the emotion estimation function to analyze the user's emotions while studying and provide encouragement and advice to maintain motivation. For example, if the user is tired, it can recommend taking a break. The music theory providing unit can also analyze the user's emotions in real time, and the generation AI can provide appropriate encouraging messages. For example, it can display words of praise if the user's studies are progressing smoothly. The music theory providing unit can also use the emotion estimation function to analyze the user's stress level while studying and suggest ways to relax. For example, it can recommend deep breathing and stretching. This allows the music theory providing unit to analyze the user's emotions while studying and provide encouragement and advice to maintain motivation.

[0055] The trend trend providing unit can analyze data from social media and music streaming services to provide the latest trend information in real time. For example, the generation AI in the trend trend providing unit analyzes social media data to provide the latest trend information in real time. For example, it analyzes posts on Twitter and Instagram to identify popular genres and artists. The trend trend providing unit also analyzes data from music streaming services to provide the latest trend information in real time. For example, it analyzes the number of plays and playlists on Spotify and Apple Music. The trend trend providing unit also integrates data from social media and music streaming services to provide the latest trend information in real time. For example, it predicts trends based on both data. This allows the generation AI to analyze data from social media and music streaming services to provide the latest trend information in real time.

[0056] The trend trend providing unit can analyze past music trends and develop an algorithm that predicts future trends. For example, the generation AI in the trend trend providing unit analyzes past music trends and develops an algorithm that predicts future trends. For example, the unit analyzes the characteristics of past hit songs and predicts the next genre that is likely to become popular. The trend trend providing unit also develops an algorithm that uses past music trend data to predict future trends with the generation AI. For example, the unit uses past data to predict seasonal trends. The trend trend providing unit also builds a model that uses the generation AI to analyze past music trends and predict future trends. For example, machine learning is used to predict future trends from past data. This makes it possible to develop an algorithm that analyzes past music trends and predicts future trends.

[0057] The trend trend providing unit can use the emotion estimation function to analyze the emotional reactions of the user regarding trend trends in which the user is interested and provide personalized trend information. The trend trend providing unit, for example, uses the emotion estimation function to analyze the emotional reactions of the user regarding trend trends in which the user is interested. For example, it analyzes the emotions the user shows toward a particular genre or artist. The trend trend providing unit also provides personalized trend information using a generation AI based on the user's emotional reactions. For example, it provides detailed information regarding trends in which the user is interested. The trend trend providing unit also uses the emotion estimation function to analyze the emotional reactions of the user regarding trend trends in which the user is interested and provides personalized trend information based on the results. For example, it preferentially displays trends in which the user shows positive emotions. This makes it possible to analyze the user's emotional reactions and provide personalized trend information.

[0058] The DTM tool integration unit can analyze user operations within the DTM tool in real time and propose optimal operating procedures and settings. For example, the DTM tool integration unit's generation AI can analyze user operations within the DTM tool in real time and propose optimal operating procedures and settings. For example, it can provide advice on effect settings and mixing procedures. The DTM tool integration unit also uses the generation AI to propose optimal operating procedures based on the user's operation history. For example, it can analyze past operation patterns and provide an efficient workflow. The DTM tool integration unit also uses the generation AI to analyze user operations within the DTM tool in real time and display alerts to prevent operating errors. For example, it can detect and warn of setting errors or inappropriate operations. This allows the DTM tool integration unit to analyze user operations within the DTM tool in real time and propose optimal operating procedures and settings.

[0059] The DTM tool collaboration unit can learn the user's composition style and provide individually customized DTM tool presets. In the DTM tool collaboration unit, for example, the generation AI learns the user's composition style and provides individually customized DTM tool presets. For example, it suggests tone and effect settings that match the user's preferences. The DTM tool collaboration unit also provides customized presets based on the user's composition history. For example, it creates new presets based on previously used tones and effects. The DTM tool collaboration unit also learns the user's composition style and provides customized presets in real time. For example, it automatically sets the optimal tones and effects while composing. This makes it possible to learn the user's composition style and provide individually customized DTM tool presets.

[0060] The DTM tool linkage unit can use the emotion estimation function to analyze the user's emotions while composing and suggest advice to reduce stress and relaxation methods. For example, the DTM tool linkage unit can use the emotion estimation function to analyze the user's emotions while composing and provide advice to reduce stress. For example, it can suggest music to help the user relax if the user is feeling stressed. The DTM tool linkage unit also analyzes the user's emotions in real time, and the generation AI suggests relaxation methods. For example, it can display messages recommending deep breathing and stretching. The DTM tool linkage unit also uses the emotion estimation function to analyze the user's emotions while composing and suggest environmental settings to reduce stress. For example, it can provide advice to adjust the lighting and volume of the work environment. In this way, it can analyze the user's emotions while composing and suggest advice to reduce stress and relaxation methods.

[0061] The demonstration support unit can analyze the user's progress in real time and dynamically suggest the next step to learn. For example, the demonstration support unit uses a generation AI to analyze the user's progress in real time and dynamically suggest the next step to learn. For example, it instructs the user to proceed to the next step when they have reached a certain level of understanding. The demonstration support unit also uses a generation AI to suggest the next step to learn based on the user's progress. For example, it allows the user to focus on learning areas in which they are weak. The demonstration support unit also uses a generation AI to analyze the user's progress in real time and dynamically adjust the learning plan. For example, it customizes the learning content according to the user's level of understanding. This allows the user's progress to be analyzed in real time and dynamically suggest the next step to learn.

[0062] The demonstration support unit tests the user's level of understanding at each step and can provide focused explanations on areas where understanding is insufficient. For example, the generation AI tests the user's level of understanding at each step and provides focused explanations on areas where understanding is insufficient. For example, it measures the level of understanding using quizzes or tests. The demonstration support unit also determines the content on which the generation AI will focus its explanations based on the user's level of understanding. For example, it provides detailed explanations on areas where the user does not understand. The demonstration support unit also tests the user's level of understanding at each step and provides additional practice questions or assignments for areas where understanding is insufficient. For example, it has the user study repeatedly until they understand. This allows the generation AI to test the user's level of understanding at each step and provide focused explanations on areas where understanding is insufficient.

[0063] The performance support unit can use the emotion estimation function to identify steps that the user finds particularly difficult and provide an approach to increase motivation for those steps. The performance support unit, for example, uses the emotion estimation function to identify steps that the user finds particularly difficult. For example, it focuses on analyzing steps where the user shows negative emotions. The performance support unit also provides an approach to increase motivation for steps that the generation AI finds difficult based on the user's emotional response. For example, it presents encouraging messages or success stories. The performance support unit also uses the emotion estimation function to identify steps that the user finds particularly difficult and provides additional resources or reference materials to increase motivation for those steps. For example, it introduces related videos or articles. This makes it possible to identify steps that the user finds particularly difficult and provide an approach to increase motivation for those steps.

[0064] The theme support unit can analyze the user's past composition history and suggest a composition style based on a specific theme. In the theme support unit, for example, the generation AI analyzes the user's past composition history and suggests a composition style based on a specific theme. For example, it suggests a new composition style based on the characteristics of songs the user has created in the past. In addition, the theme support unit can use the generation AI to suggest a composition style suitable for a specific theme based on the user's composition history. For example, it can suggest melodies and rhythm patterns that match the theme selected by the user. In addition, the theme support unit can use the generation AI to analyze the user's past composition history and suggest a composition style based on a specific theme. For example, it can provide new composition ideas based on the analysis of songs the user has created in the past. In this way, the user's past composition history can be analyzed and a composition style based on a specific theme can be suggested.

[0065] The theme support unit can simultaneously explain related music theory and historical background when providing theme-specific composition support. For example, when the generation AI provides theme-specific composition support, the theme support unit simultaneously explains related music theory. For example, it explains chord progressions and scales that are appropriate for a particular theme. In addition, when the generation AI provides theme-specific composition support, the theme support unit simultaneously explains historical background. For example, it explains the history and development process of a particular music genre. In addition, when the generation AI provides theme-specific composition support, the theme support unit simultaneously explains related music theory and historical background. For example, it introduces famous composers and pieces related to the theme. This allows for simultaneous explanations of related music theory and historical background when providing theme-specific composition support.

[0066] The theme support unit can use the emotion estimation function to analyze the emotional response to a theme selected by the user and propose an emotion-based composition approach. For example, the theme support unit uses the emotion estimation function to analyze the emotional response to a theme selected by the user. For example, it proposes a composition approach based on a theme for which the user expresses positive emotions. Furthermore, the theme support unit uses a generative AI to propose an emotion-based composition approach based on the user's emotional response. For example, it proposes a melody or rhythm pattern that matches a theme that the user emotionally identifies with. Furthermore, the theme support unit can use the emotion estimation function to analyze the emotional response to a theme selected by the user and propose an emotion-based composition approach based on the results. For example, it provides composition ideas inspired by a theme that the user emotionally identifies with. In this way, it is possible to analyze the emotional response to a theme selected by the user and propose an emotion-based composition approach.

[0067] The theme support unit can introduce other artists and works related to the theme selected by the user when providing theme-specific composition support. For example, when the generation AI provides theme-specific composition support, the theme support unit introduces other artists and works related to the theme selected by the user. For example, it introduces famous artists and songs that have composed on the same theme. The theme support unit also provides information that the generation AI can use as reference for composing, based on artists and works related to the theme selected by the user. For example, it analyzes and explains related songs. Furthermore, when the generation AI provides theme-specific composition support, the theme support unit introduces other artists and works related to the theme selected by the user and provides advice on how to gain inspiration from those works. In this way, it is possible to introduce other artists and works related to the theme selected by the user when providing theme-specific composition support.

[0068] The theme support unit can use the emotion estimation function to identify a theme with which the user most emotionally identifies and propose new composition ideas based on that theme. For example, the theme support unit uses the emotion estimation function to identify a theme with which the user most emotionally identifies. For example, the theme support unit preferentially displays themes with which the user expresses positive emotions. The theme support unit also uses a generative AI to propose new composition ideas based on the user's emotional response. For example, it proposes melodies and rhythm patterns based on themes with which the user emotionally identifies. The theme support unit also uses the emotion estimation function to identify a theme with which the user most emotionally identifies and proposes new composition ideas based on that theme. For example, it provides composition ideas inspired by themes with which the user emotionally identifies. This makes it possible to identify a theme with which the user most emotionally identifies and propose new composition ideas based on that theme.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The composition support system can further include a health management unit that monitors the user's health condition. For example, the health management unit may measure the user's heart rate and stress level and suggest appropriate times to take a break. The health management unit can also suggest the optimal working environment while composing based on the user's health data. For example, it may provide advice on appropriate lighting and volume settings. Furthermore, the health management unit can suggest relaxation methods according to the user's health condition. For example, it may show how to take deep breaths or stretch. In this way, the user's health condition can be monitored and health management during composition can be supported.

[0071] The composition support system may further include an inspiration providing unit to stimulate the user's creativity. For example, the inspiration providing unit may introduce new music genres and artists based on the user's past composition history and preferences. The inspiration providing unit may also provide visuals and stories related to themes that interest the user, such as movie scenes or passages from literary works. The inspiration providing unit may also suggest creative workshops and exercises to help the user gain new ideas. This may stimulate the user's creativity and support the generation of new composition ideas.

[0072] The composition support system may further include a community collaboration unit to strengthen the user's social connections. For example, the community collaboration unit may provide an online forum or chat room where the user can interact with other composers and music lovers. The community collaboration unit may also provide a platform for users to share their compositions and receive feedback. Furthermore, the community collaboration unit may host online events or workshops in which users can participate. This may strengthen the user's social connections and further enrich the user's composition activities.

[0073] The composition assistance system may further include an emotional theme suggestion unit that estimates the user's emotions and suggests a composition theme and style based on the estimated emotions. For example, if the user is expressing positive emotions, the emotional theme suggestion unit may suggest a composition with a bright and cheerful theme. Alternatively, if the user is expressing negative emotions, the emotional theme suggestion unit may suggest a composition in a melancholic style to express the emotion. Furthermore, the emotional theme suggestion unit may suggest a specific musical genre or rhythm pattern based on the user's emotions. This allows the system to suggest a composition theme and style based on the user's emotions and support the creation of more emotionally rich music.

[0074] The composition support system may further include an emotion progression adjustment unit that estimates the user's emotion and adjusts the progress of composition based on the estimated emotion. For example, the emotion progression adjustment unit may suggest temporarily slowing down the progress of composition when the user is feeling stressed. The emotion progression adjustment unit may also suggest accelerating the progress of composition when the user is concentrating. Furthermore, the emotion progression adjustment unit may dynamically adjust the steps of composition based on the user's emotion. This allows the progress of composition to be adjusted according to the user's emotion, thereby supporting more effective composition activities.

[0075] The composition support system may further include an emotion feedback unit that estimates the user's emotion and provides composition feedback based on the estimated emotion. For example, the emotion feedback unit may provide an encouraging message or positive feedback when the user is expressing a positive emotion. The emotion feedback unit may also provide suggestions for improvement or advice when the user is expressing a negative emotion. Furthermore, the emotion feedback unit may adjust the content and tone of the feedback based on the user's emotion. This allows for providing feedback according to the user's emotion and more effectively supporting the composition activity.

[0076] The composition support system may further include an emotion motivation maintenance unit that estimates the user's emotion and maintains the motivation to compose based on the estimated emotion. For example, the emotion motivation maintenance unit may recommend a break if the user is tired. The emotion motivation maintenance unit may also provide a message encouraging the user to continue composing if the user is concentrating. Furthermore, the emotion motivation maintenance unit may suggest music or videos to increase motivation based on the user's emotion. This supports the user in maintaining motivation based on their emotion, allowing them to compose more effectively.

[0077] The composition support system may further include a skill improvement unit for improving the user's composition skills. For example, the skill improvement unit may analyze the user's past composition history and provide a practice plan for improving a specific skill. The skill improvement unit may also provide exercises and assignments tailored to the user's composition style. Furthermore, the skill improvement unit may suggest the next skill to learn based on the user's progress. This allows for effective support in improving the user's composition skills.

[0078] The composition support system may further include an automation support unit to streamline the user's composition activities. For example, the automation support unit may analyze the user's composition patterns and suggest ways to automate repetitive tasks. The automation support unit may also provide automation tools and plug-ins tailored to the user's composition style. Furthermore, the automation support unit may analyze the user's composition process and suggest efficient workflows. This may improve the efficiency of the user's composition activities, allowing them to spend more time on creative work.

[0079] The composition support system may further include a resource provider to support the user's composition activities. For example, the resource provider may provide materials related to music theory and composition techniques that the user needs. The resource provider may also provide information on songs and artists that the user can refer to. Furthermore, the resource provider may introduce online courses and workshops that the user can participate in. This provides a wealth of resources to support the user's composition activities and promotes learning and growth.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The music theory provider provides the user with the basic music theory they need to learn composition. For example, it explains basic concepts such as chord progressions, scales, and rhythmic patterns, and provides specific examples. It can also analyze the user's past learning history and provide an individually customized music theory learning plan. Furthermore, it analyzes the user's performance in real time and provides immediate feedback on mistakes and areas for improvement. Step 2: The trend trend provider analyzes current trends and statistical data in the music industry and provides them to users. For example, it analyzes data from social media and music streaming services to provide the latest trend data in real time. It can also analyze past music trends and develop algorithms to predict future trends. Step 3: The DTM tool integration section works in conjunction with DTM tools such as GarageBand and Logic Pro. For example, it analyzes the user's operations in real time within the DTM tool and suggests optimal operation procedures and settings. It can also learn the user's composition style and provide individually customized presets for the DTM tool. Step 4: The performance support unit supports learning by demonstrating each step of composition. For example, it analyzes the user's progress in real time and dynamically suggests the next step to learn. It can also test the user's level of understanding at each step and provide focused explanations on areas where they have insufficient understanding. Step 5: The theme support unit assists with composing music based on a specific theme. For example, it analyzes the user's past composition history and suggests a composition style based on a specific theme. When providing composition support for each theme, it can also simultaneously explain related music theory and historical background.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] 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]

[0149] 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. Equipped with generative AI, The generated AI is a music theory provider that provides basic music theory necessary for users to learn composition; A trend trend providing section that analyzes current trends and statistical data in the music industry and provides them to users; A DTM tool linking section that works in conjunction with DTM tools such as GarageBand and Logic Pro, The performance support department provides support for learning by demonstrating each step of composition, and a theme support unit that supports composition based on a specific theme; A system characterized by:

2. The music theory providing unit Analyzing the user's past learning history and providing an individually customized music theory learning plan 2. The system of claim 1.

3. The music theory providing unit Analyze the user's performance in real time and provide immediate feedback on mistakes and areas for improvement 2. The system of claim 1.

4. The music theory providing unit Analyzing the user's emotions during learning and providing encouragement and advice to maintain motivation 2. The system of claim 1.

5. The trend provision department is Analyzes data from social media and music streaming services to provide the latest trends in real time 2. The system of claim 1.

6. The trend provision department is Developing algorithms to analyze past music trends and predict future trends 2. The system of claim 1.

Images