system

Abstract

The system according to this embodiment aims to provide a comprehensive service, from generating musical scores and providing music instruction to matching members and suggesting performance opportunities. [Solution] The system according to the embodiment comprises a generation unit, a teaching unit, a matching unit, and a suggestion unit. The generation unit reads a song and generates a musical score. The teaching unit provides music instruction based on the musical score generated by the generation unit. The matching unit matches members based on the user data provided by the teaching unit. The suggestion unit proposes a performance setting based on the members matched by the matching unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there has been a problem that it is difficult to consistently perform operations from music score generation to music guidance, member matching, and proposal of performance scenes.

[0005] The system according to the embodiment aims to consistently perform operations from music score generation to music guidance, member matching, and proposal of performance scenes.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a generation unit, a teaching unit, a matching unit, and a suggestion unit. The generation unit reads a musical piece and generates a musical score. The teaching unit provides music instruction based on the musical score generated by the generation unit. The matching unit matches members based on the user data provided by the teaching unit. The suggestion unit proposes a performance setting based on the members matched by the matching unit. [Effects of the Invention]

[0007] The system according to this embodiment can handle everything from generating musical scores to providing music instruction, matching members, and suggesting performance venues. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The music platform according to an embodiment of the present invention is an online + real-world salon that handles everything from sheet music generation and music instruction to member matching and performance venue suggestions. This music platform is designed to lower the barrier to enjoying music, allowing anyone to enjoy music regardless of age or skill level. First, users can generate sheet music for their favorite songs using a generation AI. The generation AI reads songs and generates sheet music, and also supports Braille sheet music. This solves the problem that not all songs have sheet music, making it easy for even beginners to obtain sheet music. Next, music instruction is provided by the generation AI. When a user performs, the generation AI evaluates the rhythm and accuracy and provides comments. This allows users to improve their skills. Furthermore, member matching is performed by the generation AI. Based on the data registered by the user (instrument played, genre, skill level, region, age, gender, etc.), the generation AI suggests the most suitable members. It also suggests studios (date, budget, location, etc.), allowing users to enjoy playing together with others. Finally, performance venue suggestions are made by the generation AI. The generation AI suggests venues and locations where users can perform, providing them with opportunities to perform. This will maximize the enjoyment of music and provide an experience for skill improvement. The platform will be offered for 500 yen per month, with a target of 100,000 registered users in three years. From the fourth year onward, the goal is to achieve an annual operating profit of over 600 million yen, and within 10 years, the target is 1 million registered users and an annual operating profit of 5 billion yen. This will enable the music platform to provide integrated services such as sheet music generation, music instruction, member matching, and performance venue suggestions.

[0029] The music platform according to this embodiment comprises a generation unit, an instruction unit, a matching unit, and a suggestion unit. The generation unit reads a song and generates a musical score. The generation unit analyzes the song using, for example, a generation AI and generates the musical score. The generation unit can handle songs of various genres, such as classical, pop, and jazz. The generation unit considers elements such as note placement, tempo, and key when generating the musical score. The generation unit can also generate musical scores in Braille, for example. The instruction unit provides music instruction based on the musical score generated by the generation unit. The instruction unit analyzes the user's performance using, for example, a generation AI and evaluates rhythm and accuracy. The instruction unit provides, for example, text comments and audio feedback to the user's performance. The instruction unit can adjust the instruction content based on, for example, the user's performance history and practice time. The matching unit matches members based on the user's data as instructed by the instruction unit. The matching unit suggests the most suitable members by considering data such as the instrument the user plays, genre, skill level, region, age, and gender. The matching unit suggests members based on, for example, the user's skill level and music genre. The matching unit can also suggest members considering factors such as region, age, and gender. The suggestion unit suggests performance venues based on the members matched by the matching unit. The suggestion unit suggests venues and locations where performances can take place. The suggestion unit can suggest various performance venues such as concert halls, schools, and outdoor stages. The suggestion unit can also provide performance opportunities. The suggestion unit can provide performance opportunities such as regular recitals and special events. As a result, the music platform according to this embodiment can consistently perform score generation, music instruction, member matching, and performance venue suggestion.

[0030] The generation unit reads a musical piece and generates a musical score. For example, it uses a generation AI to analyze the music and generate the score. Specifically, the generation AI receives a music file as input and analyzes its sound wave data. This analysis includes elements such as note pitch, length, dynamics, tempo, and key. Based on these elements, the generation AI automatically generates the musical score. For example, in classical music, accurate reproduction of complex chords and rhythmic patterns is required. In pop music, the melody line and lyric placement are important, and in jazz, elements of improvisation must be considered. The generation unit understands these genre-specific characteristics and generates an appropriate score. Furthermore, the generation unit also has a function to generate Braille scores, supporting music education for the visually impaired. Generating Braille scores requires special formats and symbols, and the generation AI considers these elements when generating the score. In addition, the generation unit can output the generated score in PDF or MIDI format, making it easily accessible to users. This allows the generation unit to analyze a wide variety of music and quickly and accurately provide scores that meet user needs.

[0031] The instruction department provides music instruction based on the musical scores generated by the generation department. For example, the instruction department uses a generation AI to analyze the user's performance and evaluate rhythm and accuracy. Specifically, the user records the music they play and inputs the sound wave data into the generation AI. The generation AI analyzes the sound wave data and evaluates the accuracy of the rhythm, tempo, and pitch of the performance. For example, if the user is playing the piano, the generation AI analyzes the timing and dynamics of each note to determine if it is being played accurately. Based on these analysis results, the instruction department provides the user with text comments and voice feedback. For example, it may give specific advice such as, "The rhythm is a little fast. Try playing a little slower," or "The pitch is a little off. Pay attention to playing with accurate pitch." The instruction department can also record the user's performance history and practice time and adjust the instruction based on this data. For example, if the user is repeatedly making mistakes in a particular section, the instruction will focus on that section. This allows the instruction department to provide personalized music instruction that meets the individual needs of each user.

[0032] The matching department matches members based on user data provided by the instruction department. Specifically, it considers data such as the instrument played, genre, skill level, region, age, and gender to suggest the most suitable members. For example, if a user who plays the piano is looking for jazz band members, the matching department will suggest members from among other jazz musicians who match in skill level and region. The matching department not only suggests members based on the user's skill level and musical genre, but also considers factors such as region, age, and gender. For example, prioritizing matching users who live in the same region makes it easier for them to meet in person to practice and perform. Also, considering factors such as age and gender can provide a more friendly environment. Furthermore, the matching department can collect user feedback and continuously improve the matching algorithm. For example, if the matched members are a good match, that data can be used to improve the accuracy of the next match. In this way, the matching department can suggest the most suitable members to users and enrich their musical activities.

[0033] The Proposal Department proposes performance venues based on members matched by the Matching Department. Specifically, it proposes venues and locations where performances can take place. For example, it can propose various performance venues such as concert halls, schools, and outdoor stages. The Proposal Department selects the most suitable performance venue according to the user's musical genre and performance style. For example, a concert hall is suitable for classical music performances, while an outdoor stage is suitable for pop music performances. The Proposal Department can also provide performance opportunities. For example, it can provide opportunities such as regular recitals and special events. This allows users to have a place to showcase their performances and increase their motivation for musical activities. Furthermore, the Proposal Department can also reserve performance venues and manage schedules. For example, by reserving a performance venue at the date and time desired by the user and managing the schedule, a smooth performance can be achieved. The Proposal Department also provides support regarding the preparation and operation of performance venues. For example, it supports the preparations necessary for a performance, such as arranging sound equipment and coordinating rehearsal schedules. In this way, the Proposal Department provides users with a place to perform and helps them enrich their musical activities.

[0034] The generation unit can generate Braille musical scores. For example, the generation unit analyzes a musical piece using a generation AI and generates a Braille musical score. The generation unit generates the score considering, for example, the placement and type of Braille characters. The generation unit provides Braille musical scores so that, for example, visually impaired people can also use them. For example, when generating Braille musical scores, the generation unit considers elements such as the placement of notes, tempo, and key. This makes the musical scores usable by visually impaired people. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs musical data into the generation AI and generates a Braille musical score.

[0035] The instruction department can evaluate rhythm and accuracy and provide comments. For example, the instruction department can analyze the user's performance using generative AI and evaluate rhythm and accuracy. The instruction department makes evaluations based on criteria such as tempo consistency and note accuracy. The instruction department can provide text comments and audio feedback on the user's performance. The instruction department can adjust the content of instruction based on the user's performance history and practice time. The instruction department can provide specific advice and suggestions for improvement on the user's performance. This can improve the user's performance skills. Some or all of the above processes in the instruction department are performed using generative AI. For example, the instruction department inputs the user's performance data into the generative AI, evaluates rhythm and accuracy, and provides comments.

[0036] The matching unit can suggest members based on data such as the instrument played, genre, skill level, region, age, and gender of the user. For example, the matching unit can analyze the user's data using generative AI and suggest the most suitable members. For example, the matching unit can suggest members considering the type of instrument played, genre, and skill level of the user. For example, the matching unit can suggest members considering factors such as region, age, and gender. For example, the matching unit can suggest members based on the user's data, according to their skill level and musical genre. This allows the matching unit to suggest the most suitable members. Some or all of the above processing in the matching unit is performed using generative AI. For example, the matching unit inputs user data into the generative AI and suggests the most suitable members.

[0037] The proposal department can suggest venues and locations where presentations can be given. For example, the proposal department uses generative AI to suggest venues and locations where presentations can be given. The proposal department can suggest various presentation settings, such as concert halls, schools, and outdoor stages. The proposal department makes suggestions considering factors such as the size of the venue, the type of audience, and the format of the presentation. The proposal department suggests the most suitable venue or location according to the user's skill level and the purpose of the presentation. This provides users with opportunities to give presentations. Some or all of the above processing in the proposal department is performed using generative AI. For example, the proposal department inputs user data into the generative AI and suggests venues and locations where presentations can be given.

[0038] The proposal department can provide opportunities for presentations. The proposal department can provide presentation opportunities using, for example, generative AI. The proposal department can provide presentation opportunities such as regular presentation meetings or special events. The proposal department can provide the most suitable presentation opportunity according to the user's skill level and presentation objectives. The proposal department can consider, for example, the size of the venue, the type of audience, and the format of the presentation when providing presentation opportunities. This allows the proposal department to provide users with presentation opportunities. Some or all of the above processing in the proposal department is performed using generative AI. For example, the proposal department inputs user data into the generative AI and provides presentation opportunities.

[0039] The generation unit can generate the optimal sheet music by referring to the user's past performance history during sheet music generation. For example, the generation unit uses a generation AI to analyze the user's past performance history and generate the optimal sheet music. For example, the generation unit uses data of songs the user has played in the past to generate sheet music for similar songs using the generation AI. For example, the generation unit generates sheet music specialized for a specific genre from the user's performance history. For example, the generation unit analyzes the user's past performance data to generate sheet music that helps improve their skills. This makes it possible to provide sheet music based on the user's past performance history. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs the user's past performance data into the generation AI to generate the optimal sheet music.

[0040] The generation unit can apply different score generation algorithms depending on the user's playing style when generating sheet music. For example, the generation unit uses a generation AI to analyze the user's playing style and apply the optimal score generation algorithm. For example, if the user prefers jazz, the generation AI will generate a jazz-style score. For example, if the user prefers classical music, the generation AI will generate a classical-style score. For example, if the user prefers pop music, the generation AI will generate a pop-style score. This allows the system to provide sheet music tailored to the user's playing style. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit inputs the user's playing style data into the generation AI and applies the optimal score generation algorithm.

[0041] The generation unit can prioritize generating region-specific music by considering the user's geographical location information when generating sheet music. For example, the generation unit uses a generation AI to analyze the user's geographical location information and generate region-specific music. For example, if the user is in Japan, the generation AI will generate sheet music for traditional Japanese music. For example, if the user is in the United States, the generation AI will generate sheet music for American pop music. For example, if the user is in Brazil, the generation AI will generate sheet music for samba. This allows for the provision of region-specific music. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's geographical location information into the generation AI and generates region-specific music.

[0042] The generation unit can analyze a user's social media activity and generate relevant songs when generating sheet music. For example, the generation unit uses a generation AI to analyze a user's social media activity and generate relevant songs. For example, the generation unit uses a generation AI to generate sheet music based on songs shared by the user on social media. For example, the generation unit uses a generation AI to generate sheet music based on songs by artists the user follows. For example, the generation unit uses a generation AI to generate sheet music based on trends in music communities the user participates in. This allows the system to provide songs based on the user's social media activity. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs the user's social media data into the generation AI and generates relevant songs.

[0043] The instruction department can select the optimal teaching method by referring to the user's past performance data during instruction. For example, the instruction department uses generative AI to analyze the user's past performance data and select the optimal teaching method. For example, the instruction department may focus on teaching areas that the user has struggled with in the past. For example, the instruction department may provide instruction that helps improve the user's skills based on the user's past performance data. For example, the instruction department may analyze the user's past performance data and suggest the optimal practice method. This allows the instruction department to provide instruction based on the user's past performance data. Some or all of the above processes in the instruction department are performed using generative AI. For example, the instruction department inputs the user's past performance data into the generative AI and selects the optimal teaching method.

[0044] The instruction unit can apply different instruction algorithms depending on the user's playing style during instruction. For example, the instruction unit uses generative AI to analyze the user's playing style and apply the optimal instruction algorithm. For example, if the user plays jazz, the generative AI will provide jazz-specific instruction. For example, if the user plays classical music, the generative AI will provide classical music-specific instruction. For example, if the user plays pop music, the generative AI will provide pop music-specific instruction. This allows the instruction unit to provide instruction tailored to the user's playing style. Some or all of the above processing in the instruction unit is performed using generative AI. For example, the instruction unit inputs the user's playing style data into the generative AI and applies the optimal instruction algorithm.

[0045] The instruction unit can determine instruction priorities based on the user's performance history during instruction. For example, the instruction unit can analyze the user's performance history using generative AI to determine instruction priorities. For example, the instruction unit may prioritize instruction on areas the user has struggled with in the past. For example, the instruction unit may prioritize instruction on areas from the user's performance history that will help improve their skills. For example, the instruction unit may analyze the user's performance history to determine the most effective instruction sequence. This allows the instruction unit to provide instruction priorities based on the user's performance history. Some or all of the above processes in the instruction unit are performed using generative AI. For example, the instruction unit inputs the user's performance history data into the generative AI to determine instruction priorities.

[0046] The instruction unit can improve the accuracy of its instruction by referring to the user's relevant literature during instruction. For example, the instruction unit can use generative AI to analyze the user's relevant literature and improve the accuracy of its instruction. For example, the instruction unit can refer to literature related to the music the user is playing to enrich the content of the instruction. For example, the instruction unit can refer to literature related to the user's playing style to optimize the instruction method. For example, the instruction unit can refer to literature that helps improve the user's skills to strengthen the content of the instruction. This enables the instruction unit to provide accurate instruction based on the user's relevant literature. Some or all of the above processes in the instruction unit are performed using generative AI. For example, the instruction unit inputs the user's relevant literature data into the generative AI to improve the accuracy of the instruction.

[0047] The matching unit can suggest the most suitable members by referring to the user's past performance data during the matching process. For example, the matching unit can analyze the user's past performance data using a generative AI and suggest the most suitable members. For example, the matching unit can use data on songs the user has played in the past to have the generative AI suggest members who play similar songs. For example, the matching unit can suggest members with similar skill levels based on the user's performance data. For example, the matching unit can analyze the user's performance data and suggest the most compatible members. This allows the system to provide the most suitable members based on the user's past performance data. Some or all of the above processes in the matching unit are performed using a generative AI. For example, the matching unit inputs the user's past performance data into the generative AI and suggests the most suitable members.

[0048] The matching unit can apply different matching algorithms depending on the user's playing style during the matching process. For example, the matching unit uses generative AI to analyze the user's playing style and applies the optimal matching algorithm. For example, if the user plays jazz, the generative AI suggests members who play jazz. For example, if the user plays classical music, the generative AI suggests members who play classical music. For example, if the user plays pop music, the generative AI suggests members who play pop music. This allows the matching unit to provide the optimal members according to the user's playing style. Some or all of the above processing in the matching unit is performed using generative AI. For example, the matching unit inputs the user's playing style data into the generative AI and applies the optimal matching algorithm.

[0049] The matching unit can prioritize suggesting region-specific members by considering the user's geographical location information during the matching process. For example, the matching unit uses a generative AI to analyze the user's geographical location information and suggest region-specific members. For example, if the user is in Japan, the generative AI will suggest region-specific members for Japan. For example, if the user is in the United States, the generative AI will suggest region-specific members for the United States. For example, if the user is in Brazil, the generative AI will suggest region-specific members for Brazil. This allows the matching unit to provide region-specific members. Some or all of the above processing in the matching unit is performed using the generative AI. For example, the matching unit inputs the user's geographical location information into the generative AI and suggests region-specific members.

[0050] The matching unit can analyze a user's social media activity during the matching process and suggest relevant members. For example, the matching unit can use generative AI to analyze a user's social media activity and suggest relevant members. For example, the matching unit can use generative AI to suggest relevant members based on songs the user has shared on social media. For example, the matching unit can use generative AI to suggest relevant members based on songs by artists the user follows. For example, the matching unit can use generative AI to suggest relevant members based on trends in music communities the user participates in. This allows the matching unit to provide members based on the user's social media activity. Some or all of the above processing in the matching unit is performed using generative AI. For example, the matching unit inputs the user's social media data into the generative AI and suggests relevant members.

[0051] The proposal unit can suggest the optimal presentation setting by referring to the user's past presentation history when making a proposal. For example, the proposal unit can analyze the user's past presentation history using generative AI and suggest the optimal presentation setting. For example, the proposal unit can use data on the locations where the user has previously presented to have the generative AI suggest similar presentation settings. For example, the proposal unit can suggest presentation settings that are useful for improving technology based on the user's presentation history. For example, the proposal unit can analyze the user's past presentation data and suggest the most effective presentation setting. This allows the proposal unit to provide presentation settings based on the user's past presentation history. Some or all of the above processing in the proposal unit is performed using generative AI. For example, the proposal unit inputs the user's past presentation history data into the generative AI and suggests the optimal presentation setting.

[0052] The proposal unit can apply different presentation scene suggestion algorithms depending on the user's playing style when making a proposal. For example, the proposal unit uses generative AI to analyze the user's playing style and applies the optimal presentation scene suggestion algorithm. For example, if the user plays jazz, the generative AI will suggest a presentation scene suitable for jazz. For example, if the user plays classical music, the generative AI will suggest a presentation scene suitable for classical music. For example, if the user plays pop music, the generative AI will suggest a presentation scene suitable for pop music. This allows the proposal unit to provide presentation scenes that match the user's playing style. Some or all of the above processing in the proposal unit is performed using generative AI. For example, the proposal unit inputs the user's playing style data into the generative AI and applies the optimal presentation scene suggestion algorithm.

[0053] The proposal unit can prioritize suggesting region-specific presentation scenarios by considering the user's geographical location information during the proposal process. For example, the proposal unit uses generative AI to analyze the user's geographical location information and suggest region-specific presentation scenarios. For example, if the user is in Japan, the generative AI will suggest region-specific presentation scenarios for Japan. For example, if the user is in the United States, the generative AI will suggest region-specific presentation scenarios for the United States. For example, if the user is in Brazil, the generative AI will suggest region-specific presentation scenarios for Brazil. This allows the proposal unit to provide region-specific presentation scenarios. Some or all of the above processing in the proposal unit is performed using generative AI. For example, the proposal unit inputs the user's geographical location information into the generative AI and suggests region-specific presentation scenarios.

[0054] The proposal unit can analyze the user's social media activity and suggest relevant announcements when making a proposal. For example, the proposal unit can use generative AI to analyze the user's social media activity and suggest relevant announcements. For example, the proposal unit can use generative AI to suggest relevant announcements based on announcements the user has shared on social media. For example, the proposal unit can use generative AI to suggest relevant announcements based on announcements from artists the user follows. For example, the proposal unit can use generative AI to suggest relevant announcements based on trends in music communities the user participates in. This allows the proposal unit to provide announcements based on the user's social media activity. Some or all of the above processing in the proposal unit is performed using generative AI. For example, the proposal unit inputs the user's social media data into the generative AI and suggests relevant announcements.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The generation unit can generate the optimal sheet music by referring to the user's past performance history during sheet music generation. For example, the generation unit uses a generation AI to analyze the user's past performance history and generate the optimal sheet music. For example, the generation unit uses data of songs the user has played in the past to generate sheet music for similar songs using the generation AI. For example, the generation unit generates sheet music specialized for a specific genre from the user's performance history. For example, the generation unit analyzes the user's past performance data to generate sheet music that helps improve their skills. This makes it possible to provide sheet music based on the user's past performance history. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs the user's past performance data into the generation AI to generate the optimal sheet music.

[0057] The instruction department can select the optimal teaching method by referring to the user's past performance data during instruction. For example, the instruction department uses generative AI to analyze the user's past performance data and select the optimal teaching method. For example, the instruction department may focus on teaching areas that the user has struggled with in the past. For example, the instruction department may provide instruction that helps improve the user's skills based on the user's past performance data. For example, the instruction department may analyze the user's past performance data and suggest the optimal practice method. This allows the instruction department to provide instruction based on the user's past performance data. Some or all of the above processes in the instruction department are performed using generative AI. For example, the instruction department inputs the user's past performance data into the generative AI and selects the optimal teaching method.

[0058] The matching unit can suggest the most suitable members by referring to the user's past performance data during the matching process. For example, the matching unit can analyze the user's past performance data using a generative AI and suggest the most suitable members. For example, the matching unit can use data on songs the user has played in the past to have the generative AI suggest members who play similar songs. For example, the matching unit can suggest members with similar skill levels based on the user's performance data. For example, the matching unit can analyze the user's performance data and suggest the most compatible members. This allows the system to provide the most suitable members based on the user's past performance data. Some or all of the above processes in the matching unit are performed using a generative AI. For example, the matching unit inputs the user's past performance data into the generative AI and suggests the most suitable members.

[0059] The proposal unit can suggest the optimal presentation setting by referring to the user's past presentation history when making a proposal. For example, the proposal unit can analyze the user's past presentation history using generative AI and suggest the optimal presentation setting. For example, the proposal unit can use data on the locations where the user has previously presented to have the generative AI suggest similar presentation settings. For example, the proposal unit can suggest presentation settings that are useful for improving technology based on the user's presentation history. For example, the proposal unit can analyze the user's past presentation data and suggest the most effective presentation setting. This allows the proposal unit to provide presentation settings based on the user's past presentation history. Some or all of the above processing in the proposal unit is performed using generative AI. For example, the proposal unit inputs the user's past presentation history data into the generative AI and suggests the optimal presentation setting.

[0060] The generation unit can prioritize generating region-specific music by considering the user's geographical location information when generating sheet music. For example, the generation unit uses a generation AI to analyze the user's geographical location information and generate region-specific music. For example, if the user is in Japan, the generation AI will generate sheet music for traditional Japanese music. For example, if the user is in the United States, the generation AI will generate sheet music for American pop music. For example, if the user is in Brazil, the generation AI will generate sheet music for samba. This allows for the provision of region-specific music. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's geographical location information into the generation AI and generates region-specific music.

[0061] The proposal unit can prioritize suggesting region-specific presentation scenarios by considering the user's geographical location information during the proposal process. For example, the proposal unit uses generative AI to analyze the user's geographical location information and suggest region-specific presentation scenarios. For example, if the user is in Japan, the generative AI will suggest region-specific presentation scenarios for Japan. For example, if the user is in the United States, the generative AI will suggest region-specific presentation scenarios for the United States. For example, if the user is in Brazil, the generative AI will suggest region-specific presentation scenarios for Brazil. This allows the proposal unit to provide region-specific presentation scenarios. Some or all of the above processing in the proposal unit is performed using generative AI. For example, the proposal unit inputs the user's geographical location information into the generative AI and suggests region-specific presentation scenarios.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The generation unit reads the music and generates the musical score. The generation unit analyzes the music using, for example, a generation AI and generates the musical score. The generation unit handles music from various genres such as classical, pop, and jazz, and takes into account elements such as note placement, tempo, and key. It can also generate musical scores in Braille. Step 2: The instruction unit provides music instruction based on the musical score generated by the generation unit. The instruction unit uses the generation AI to analyze the user's performance and evaluate rhythm and accuracy. Furthermore, it can provide text comments and voice feedback on the user's performance and adjust the instruction content based on the performance history and practice time. Step 3: The matching department matches members based on the user data provided by the instruction department. The matching department proposes the most suitable members by considering data such as the instrument played, genre, skill level, region, age, and gender of the user. Step 4: The proposal team proposes presentation venues based on the members matched by the matching team. The proposal team can suggest venues and locations where presentations can be given, such as concert halls, schools, and outdoor stages. They also provide opportunities for presentations, such as regular presentations and special events.

[0064] (Example of form 2) The music platform according to an embodiment of the present invention is an online + real-world salon that handles everything from sheet music generation and music instruction to member matching and performance venue suggestions. This music platform is designed to lower the barrier to enjoying music, allowing anyone to enjoy music regardless of age or skill level. First, users can generate sheet music for their favorite songs using a generation AI. The generation AI reads songs and generates sheet music, and also supports Braille sheet music. This solves the problem that not all songs have sheet music, making it easy for even beginners to obtain sheet music. Next, music instruction is provided by the generation AI. When a user performs, the generation AI evaluates the rhythm and accuracy and provides comments. This allows users to improve their skills. Furthermore, member matching is performed by the generation AI. Based on the data registered by the user (instrument played, genre, skill level, region, age, gender, etc.), the generation AI suggests the most suitable members. It also suggests studios (date, budget, location, etc.), allowing users to enjoy playing together with others. Finally, performance venue suggestions are made by the generation AI. The generation AI suggests venues and locations where users can perform, providing them with opportunities to perform. This will maximize the enjoyment of music and provide an experience for skill improvement. The platform will be offered for 500 yen per month, with a target of 100,000 registered users in three years. From the fourth year onward, the goal is to achieve an annual operating profit of over 600 million yen, and within 10 years, the target is 1 million registered users and an annual operating profit of 5 billion yen. This will enable the music platform to provide integrated services such as sheet music generation, music instruction, member matching, and performance venue suggestions.

[0065] The music platform according to this embodiment comprises a generation unit, an instruction unit, a matching unit, and a suggestion unit. The generation unit reads a song and generates a musical score. The generation unit analyzes the song using, for example, a generation AI and generates the musical score. The generation unit can handle songs of various genres, such as classical, pop, and jazz. The generation unit considers elements such as note placement, tempo, and key when generating the musical score. The generation unit can also generate musical scores in Braille, for example. The instruction unit provides music instruction based on the musical score generated by the generation unit. The instruction unit analyzes the user's performance using, for example, a generation AI and evaluates rhythm and accuracy. The instruction unit provides, for example, text comments and audio feedback to the user's performance. The instruction unit can adjust the instruction content based on, for example, the user's performance history and practice time. The matching unit matches members based on the user's data as instructed by the instruction unit. The matching unit suggests the most suitable members by considering data such as the instrument the user plays, genre, skill level, region, age, and gender. The matching unit suggests members based on, for example, the user's skill level and music genre. The matching unit can also suggest members considering factors such as region, age, and gender. The suggestion unit suggests performance venues based on the members matched by the matching unit. The suggestion unit suggests venues and locations where performances can take place. The suggestion unit can suggest various performance venues such as concert halls, schools, and outdoor stages. The suggestion unit can also provide performance opportunities. The suggestion unit can provide performance opportunities such as regular recitals and special events. As a result, the music platform according to this embodiment can consistently perform score generation, music instruction, member matching, and performance venue suggestion.

[0066] The generation unit reads a musical piece and generates a musical score. For example, it uses a generation AI to analyze the music and generate the score. Specifically, the generation AI receives a music file as input and analyzes its sound wave data. This analysis includes elements such as note pitch, length, dynamics, tempo, and key. Based on these elements, the generation AI automatically generates the musical score. For example, in classical music, accurate reproduction of complex chords and rhythmic patterns is required. In pop music, the melody line and lyric placement are important, and in jazz, elements of improvisation must be considered. The generation unit understands these genre-specific characteristics and generates an appropriate score. Furthermore, the generation unit also has a function to generate Braille scores, supporting music education for the visually impaired. Generating Braille scores requires special formats and symbols, and the generation AI considers these elements when generating the score. In addition, the generation unit can output the generated score in PDF or MIDI format, making it easily accessible to users. This allows the generation unit to analyze a wide variety of music and quickly and accurately provide scores that meet user needs.

[0067] The instruction department provides music instruction based on the musical scores generated by the generation department. For example, the instruction department uses a generation AI to analyze the user's performance and evaluate rhythm and accuracy. Specifically, the user records the music they play and inputs the sound wave data into the generation AI. The generation AI analyzes the sound wave data and evaluates the accuracy of the rhythm, tempo, and pitch of the performance. For example, if the user is playing the piano, the generation AI analyzes the timing and dynamics of each note to determine if it is being played accurately. Based on these analysis results, the instruction department provides the user with text comments and voice feedback. For example, it may give specific advice such as, "The rhythm is a little fast. Try playing a little slower," or "The pitch is a little off. Pay attention to playing with accurate pitch." The instruction department can also record the user's performance history and practice time and adjust the instruction based on this data. For example, if the user is repeatedly making mistakes in a particular section, the instruction will focus on that section. This allows the instruction department to provide personalized music instruction that meets the individual needs of each user.

[0068] The matching department matches members based on user data provided by the instruction department. Specifically, it considers data such as the instrument played, genre, skill level, region, age, and gender to suggest the most suitable members. For example, if a user who plays the piano is looking for jazz band members, the matching department will suggest members from among other jazz musicians who match in skill level and region. The matching department not only suggests members based on the user's skill level and musical genre, but also considers factors such as region, age, and gender. For example, prioritizing matching users who live in the same region makes it easier for them to meet in person to practice and perform. Also, considering factors such as age and gender can provide a more friendly environment. Furthermore, the matching department can collect user feedback and continuously improve the matching algorithm. For example, if the matched members are a good match, that data can be used to improve the accuracy of the next match. In this way, the matching department can suggest the most suitable members to users and enrich their musical activities.

[0069] The Proposal Department proposes performance venues based on members matched by the Matching Department. Specifically, it proposes venues and locations where performances can take place. For example, it can propose various performance venues such as concert halls, schools, and outdoor stages. The Proposal Department selects the most suitable performance venue according to the user's musical genre and performance style. For example, a concert hall is suitable for classical music performances, while an outdoor stage is suitable for pop music performances. The Proposal Department can also provide performance opportunities. For example, it can provide opportunities such as regular recitals and special events. This allows users to have a place to showcase their performances and increase their motivation for musical activities. Furthermore, the Proposal Department can also reserve performance venues and manage schedules. For example, by reserving a performance venue at the date and time desired by the user and managing the schedule, a smooth performance can be achieved. The Proposal Department also provides support regarding the preparation and operation of performance venues. For example, it supports the preparations necessary for a performance, such as arranging sound equipment and coordinating rehearsal schedules. In this way, the Proposal Department provides users with a place to perform and helps them enrich their musical activities.

[0070] The generation unit can generate Braille musical scores. For example, the generation unit analyzes a musical piece using a generation AI and generates a Braille musical score. The generation unit generates the score considering, for example, the placement and type of Braille characters. The generation unit provides Braille musical scores so that, for example, visually impaired people can also use them. For example, when generating Braille musical scores, the generation unit considers elements such as the placement of notes, tempo, and key. This makes the musical scores usable by visually impaired people. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs musical data into the generation AI and generates a Braille musical score.

[0071] The instruction department can evaluate rhythm and accuracy and provide comments. For example, the instruction department can analyze the user's performance using generative AI and evaluate rhythm and accuracy. The instruction department makes evaluations based on criteria such as tempo consistency and note accuracy. The instruction department can provide text comments and audio feedback on the user's performance. The instruction department can adjust the content of instruction based on the user's performance history and practice time. The instruction department can provide specific advice and suggestions for improvement on the user's performance. This can improve the user's performance skills. Some or all of the above processes in the instruction department are performed using generative AI. For example, the instruction department inputs the user's performance data into the generative AI, evaluates rhythm and accuracy, and provides comments.

[0072] The matching unit can suggest members based on data such as the instrument played, genre, skill level, region, age, and gender of the user. For example, the matching unit can analyze the user's data using generative AI and suggest the most suitable members. For example, the matching unit can suggest members considering the type of instrument played, genre, and skill level of the user. For example, the matching unit can suggest members considering factors such as region, age, and gender. For example, the matching unit can suggest members based on the user's data, according to their skill level and musical genre. This allows the matching unit to suggest the most suitable members. Some or all of the above processing in the matching unit is performed using generative AI. For example, the matching unit inputs user data into the generative AI and suggests the most suitable members.

[0073] The proposal department can suggest venues and locations where presentations can be given. For example, the proposal department uses generative AI to suggest venues and locations where presentations can be given. The proposal department can suggest various presentation settings, such as concert halls, schools, and outdoor stages. The proposal department makes suggestions considering factors such as the size of the venue, the type of audience, and the format of the presentation. The proposal department suggests the most suitable venue or location according to the user's skill level and the purpose of the presentation. This provides users with opportunities to give presentations. Some or all of the above processing in the proposal department is performed using generative AI. For example, the proposal department inputs user data into the generative AI and suggests venues and locations where presentations can be given.

[0074] The proposal department can provide opportunities for presentations. The proposal department can provide presentation opportunities using, for example, generative AI. The proposal department can provide presentation opportunities such as regular presentation meetings or special events. The proposal department can provide the most suitable presentation opportunity according to the user's skill level and presentation objectives. The proposal department can consider, for example, the size of the venue, the type of audience, and the format of the presentation when providing presentation opportunities. This allows the proposal department to provide users with presentation opportunities. Some or all of the above processing in the proposal department is performed using generative AI. For example, the proposal department inputs user data into the generative AI and provides presentation opportunities.

[0075] The generation unit can estimate the user's emotions and adjust the difficulty level of the sheet music based on the estimated emotions. For example, the generation unit estimates the user's emotions using a generation AI. For example, if the user is stressed, the generation unit uses the generation AI to generate easy sheet music. For example, if the user is relaxed, the generation unit uses the generation AI to generate intermediate-level sheet music. For example, if the user is in a challenging mood, the generation unit uses the generation AI to generate difficult sheet music. This allows the system to provide sheet music that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit inputs user emotion data into the generation AI and adjusts the difficulty level of the sheet music.

[0076] The generation unit can generate the optimal sheet music by referring to the user's past performance history during sheet music generation. For example, the generation unit uses a generation AI to analyze the user's past performance history and generate the optimal sheet music. For example, the generation unit uses data of songs the user has played in the past to generate sheet music for similar songs using the generation AI. For example, the generation unit generates sheet music specialized for a specific genre from the user's performance history. For example, the generation unit analyzes the user's past performance data to generate sheet music that helps improve their skills. This makes it possible to provide sheet music based on the user's past performance history. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs the user's past performance data into the generation AI to generate the optimal sheet music.

[0077] The generation unit can apply different score generation algorithms depending on the user's playing style when generating sheet music. For example, the generation unit uses a generation AI to analyze the user's playing style and apply the optimal score generation algorithm. For example, if the user prefers jazz, the generation AI will generate a jazz-style score. For example, if the user prefers classical music, the generation AI will generate a classical-style score. For example, if the user prefers pop music, the generation AI will generate a pop-style score. This allows the system to provide sheet music tailored to the user's playing style. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit inputs the user's playing style data into the generation AI and applies the optimal score generation algorithm.

[0078] The generation unit can estimate the user's emotions and adjust the display format of the musical score based on the estimated emotions. For example, the generation unit estimates the user's emotions using a generation AI. For example, if the user is tense, the generation AI provides a simple display format of the musical score. For example, if the user is relaxed, the generation AI provides a detailed display format of the musical score. For example, if the user is focused, the generation AI provides a visually stimulating display format of the musical score. This makes it possible to provide a musical score display format that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's emotion data into the generation AI and adjusts the display format of the musical score.

[0079] The generation unit can prioritize generating region-specific music by considering the user's geographical location information when generating sheet music. For example, the generation unit uses a generation AI to analyze the user's geographical location information and generate region-specific music. For example, if the user is in Japan, the generation AI will generate sheet music for traditional Japanese music. For example, if the user is in the United States, the generation AI will generate sheet music for American pop music. For example, if the user is in Brazil, the generation AI will generate sheet music for samba. This allows for the provision of region-specific music. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's geographical location information into the generation AI and generates region-specific music.

[0080] The generation unit can analyze a user's social media activity and generate relevant songs when generating sheet music. For example, the generation unit uses a generation AI to analyze a user's social media activity and generate relevant songs. For example, the generation unit uses a generation AI to generate sheet music based on songs shared by the user on social media. For example, the generation unit uses a generation AI to generate sheet music based on songs by artists the user follows. For example, the generation unit uses a generation AI to generate sheet music based on trends in music communities the user participates in. This allows the system to provide songs based on the user's social media activity. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs the user's social media data into the generation AI and generates relevant songs.

[0081] The instruction unit can estimate the user's emotions and adjust the way instruction is delivered based on the estimated emotions. For example, the instruction unit uses generative AI to estimate the user's emotions. For example, if the user is tense, the generative AI will provide instruction in a gentle tone. For example, if the user is relaxed, the generative AI will provide detailed instruction. For example, if the user is focused, the generative AI will provide strict instruction. This allows the instruction unit to provide instruction that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit is performed using generative AI. For example, the instruction unit inputs user emotion data into the generative AI and adjusts the way instruction is delivered.

[0082] The instruction department can select the optimal teaching method by referring to the user's past performance data during instruction. For example, the instruction department uses generative AI to analyze the user's past performance data and select the optimal teaching method. For example, the instruction department may focus on teaching areas that the user has struggled with in the past. For example, the instruction department may provide instruction that helps improve the user's skills based on the user's past performance data. For example, the instruction department may analyze the user's past performance data and suggest the optimal practice method. This allows the instruction department to provide instruction based on the user's past performance data. Some or all of the above processes in the instruction department are performed using generative AI. For example, the instruction department inputs the user's past performance data into the generative AI and selects the optimal teaching method.

[0083] The instruction unit can apply different instruction algorithms depending on the user's playing style during instruction. For example, the instruction unit uses generative AI to analyze the user's playing style and apply the optimal instruction algorithm. For example, if the user plays jazz, the generative AI will provide jazz-specific instruction. For example, if the user plays classical music, the generative AI will provide classical music-specific instruction. For example, if the user plays pop music, the generative AI will provide pop music-specific instruction. This allows the instruction unit to provide instruction tailored to the user's playing style. Some or all of the above processing in the instruction unit is performed using generative AI. For example, the instruction unit inputs the user's playing style data into the generative AI and applies the optimal instruction algorithm.

[0084] The instruction unit can estimate the user's emotions and adjust the length of the instruction based on the estimated emotions. The instruction unit estimates the user's emotions, for example, using generative AI. For example, if the user is tired, the generative AI will provide short instruction. For example, if the user is focused, the generative AI will provide long instruction. For example, if the user is relaxed, the generative AI will provide instruction of an appropriate length. This allows the instruction unit to provide instruction lengths that are appropriate for the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit is performed using generative AI. For example, the instruction unit inputs user emotion data into the generative AI and adjusts the length of the instruction.

[0085] The instruction unit can determine instruction priorities based on the user's performance history during instruction. For example, the instruction unit can analyze the user's performance history using generative AI to determine instruction priorities. For example, the instruction unit may prioritize instruction on areas the user has struggled with in the past. For example, the instruction unit may prioritize instruction on areas from the user's performance history that will help improve their skills. For example, the instruction unit may analyze the user's performance history to determine the most effective instruction sequence. This allows the instruction unit to provide instruction priorities based on the user's performance history. Some or all of the above processes in the instruction unit are performed using generative AI. For example, the instruction unit inputs the user's performance history data into the generative AI to determine instruction priorities.

[0086] The instruction unit can improve the accuracy of its instruction by referring to the user's relevant literature during instruction. For example, the instruction unit can use generative AI to analyze the user's relevant literature and improve the accuracy of its instruction. For example, the instruction unit can refer to literature related to the music the user is playing to enrich the content of the instruction. For example, the instruction unit can refer to literature related to the user's playing style to optimize the instruction method. For example, the instruction unit can refer to literature that helps improve the user's skills to strengthen the content of the instruction. This enables the instruction unit to provide accurate instruction based on the user's relevant literature. Some or all of the above processes in the instruction unit are performed using generative AI. For example, the instruction unit inputs the user's relevant literature data into the generative AI to improve the accuracy of the instruction.

[0087] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, the matching unit estimates the user's emotions using a generative AI. For example, if the user is tense, the generative AI suggests members who can help them relax. For example, if the user is relaxed, the generative AI suggests challenging members. For example, if the user is focused, the generative AI suggests members who are also focused. This allows the matching unit to provide matching criteria that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the matching unit are performed using the generative AI. For example, the matching unit inputs the user's emotion data into the generative AI and adjusts the matching criteria.

[0088] The matching unit can suggest the most suitable members by referring to the user's past performance data during the matching process. For example, the matching unit can analyze the user's past performance data using a generative AI and suggest the most suitable members. For example, the matching unit can use data on songs the user has played in the past to have the generative AI suggest members who play similar songs. For example, the matching unit can suggest members with similar skill levels based on the user's performance data. For example, the matching unit can analyze the user's performance data and suggest the most compatible members. This allows the system to provide the most suitable members based on the user's past performance data. Some or all of the above processes in the matching unit are performed using a generative AI. For example, the matching unit inputs the user's past performance data into the generative AI and suggests the most suitable members.

[0089] The matching unit can apply different matching algorithms depending on the user's playing style during the matching process. For example, the matching unit uses generative AI to analyze the user's playing style and applies the optimal matching algorithm. For example, if the user plays jazz, the generative AI suggests members who play jazz. For example, if the user plays classical music, the generative AI suggests members who play classical music. For example, if the user plays pop music, the generative AI suggests members who play pop music. This allows the matching unit to provide the optimal members according to the user's playing style. Some or all of the above processing in the matching unit is performed using generative AI. For example, the matching unit inputs the user's playing style data into the generative AI and applies the optimal matching algorithm.

[0090] The matching unit can estimate the user's emotions and adjust the order in which matching results are displayed based on the estimated emotions. For example, the matching unit estimates the user's emotions using a generative AI. For example, if the user is tense, the matching unit will prioritize displaying members who are relaxing using the generative AI. For example, if the user is relaxed, the matching unit will prioritize displaying members who are challenging using the generative AI. For example, if the user is focused, the matching unit will prioritize displaying members who are also focused using the generative AI. This allows for a display order of matching results that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the matching unit is performed using the generative AI. For example, the matching unit inputs the user's emotion data into the generative AI and adjusts the order in which matching results are displayed.

[0091] The matching unit can prioritize suggesting region-specific members by considering the user's geographical location information during the matching process. For example, the matching unit uses a generative AI to analyze the user's geographical location information and suggest region-specific members. For example, if the user is in Japan, the generative AI will suggest region-specific members for Japan. For example, if the user is in the United States, the generative AI will suggest region-specific members for the United States. For example, if the user is in Brazil, the generative AI will suggest region-specific members for Brazil. This allows the matching unit to provide region-specific members. Some or all of the above processing in the matching unit is performed using the generative AI. For example, the matching unit inputs the user's geographical location information into the generative AI and suggests region-specific members.

[0092] The matching unit can analyze a user's social media activity during the matching process and suggest relevant members. For example, the matching unit can use generative AI to analyze a user's social media activity and suggest relevant members. For example, the matching unit can use generative AI to suggest relevant members based on songs the user has shared on social media. For example, the matching unit can use generative AI to suggest relevant members based on songs by artists the user follows. For example, the matching unit can use generative AI to suggest relevant members based on trends in music communities the user participates in. This allows the matching unit to provide members based on the user's social media activity. Some or all of the above processing in the matching unit is performed using generative AI. For example, the matching unit inputs the user's social media data into the generative AI and suggests relevant members.

[0093] The suggestion unit can estimate the user's emotions and adjust the presentation scenario suggestion method based on the estimated user emotions. For example, the suggestion unit estimates the user's emotions using generative AI. For example, if the user is nervous, the suggestion unit has the generative AI suggest a relaxing presentation scenario. For example, if the user is relaxed, the suggestion unit has the generative AI suggest a challenging presentation scenario. For example, if the user is focused, the suggestion unit has the generative AI suggest a presentation scenario that also allows for focus. This makes it possible to provide a presentation scenario suggestion method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit is performed using generative AI. For example, the suggestion unit inputs user emotion data into the generative AI and adjusts the presentation scenario suggestion method.

[0094] The proposal unit can suggest the optimal presentation setting by referring to the user's past presentation history when making a proposal. For example, the proposal unit can analyze the user's past presentation history using generative AI and suggest the optimal presentation setting. For example, the proposal unit can use data on the locations where the user has previously presented to have the generative AI suggest similar presentation settings. For example, the proposal unit can suggest presentation settings that are useful for improving technology based on the user's presentation history. For example, the proposal unit can analyze the user's past presentation data and suggest the most effective presentation setting. This allows the proposal unit to provide presentation settings based on the user's past presentation history. Some or all of the above processing in the proposal unit is performed using generative AI. For example, the proposal unit inputs the user's past presentation history data into the generative AI and suggests the optimal presentation setting.

[0095] The proposal unit can apply different presentation scene suggestion algorithms depending on the user's playing style when making a proposal. For example, the proposal unit uses generative AI to analyze the user's playing style and applies the optimal presentation scene suggestion algorithm. For example, if the user plays jazz, the generative AI will suggest a presentation scene suitable for jazz. For example, if the user plays classical music, the generative AI will suggest a presentation scene suitable for classical music. For example, if the user plays pop music, the generative AI will suggest a presentation scene suitable for pop music. This allows the proposal unit to provide presentation scenes that match the user's playing style. Some or all of the above processing in the proposal unit is performed using generative AI. For example, the proposal unit inputs the user's playing style data into the generative AI and applies the optimal presentation scene suggestion algorithm.

[0096] The proposal unit can estimate the user's emotions and determine the priority of presentation scenes based on the estimated emotions. For example, the proposal unit estimates the user's emotions using generative AI. For example, if the user is nervous, the proposal unit will prioritize suggesting presentation scenes that allow the user to relax. For example, if the user is relaxed, the proposal unit will prioritize suggesting challenging presentation scenes. For example, if the user is focused, the proposal unit will prioritize suggesting presentation scenes that allow for focused listening. This allows the proposal unit to provide a priority of presentation scenes that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit is performed using generative AI. For example, the proposal unit inputs user emotion data into the generative AI and determines the priority of presentation scenes.

[0097] The proposal unit can prioritize suggesting region-specific presentation scenarios by considering the user's geographical location information during the proposal process. For example, the proposal unit uses generative AI to analyze the user's geographical location information and suggest region-specific presentation scenarios. For example, if the user is in Japan, the generative AI will suggest region-specific presentation scenarios for Japan. For example, if the user is in the United States, the generative AI will suggest region-specific presentation scenarios for the United States. For example, if the user is in Brazil, the generative AI will suggest region-specific presentation scenarios for Brazil. This allows the proposal unit to provide region-specific presentation scenarios. Some or all of the above processing in the proposal unit is performed using generative AI. For example, the proposal unit inputs the user's geographical location information into the generative AI and suggests region-specific presentation scenarios.

[0098] The proposal unit can analyze the user's social media activity and suggest relevant announcements when making a proposal. For example, the proposal unit can use generative AI to analyze the user's social media activity and suggest relevant announcements. For example, the proposal unit can use generative AI to suggest relevant announcements based on announcements the user has shared on social media. For example, the proposal unit can use generative AI to suggest relevant announcements based on announcements from artists the user follows. For example, the proposal unit can use generative AI to suggest relevant announcements based on trends in music communities the user participates in. This allows the proposal unit to provide announcements based on the user's social media activity. Some or all of the above processing in the proposal unit is performed using generative AI. For example, the proposal unit inputs the user's social media data into the generative AI and suggests relevant announcements.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The generation unit can estimate the user's emotions and adjust the difficulty level of the sheet music based on the estimated emotions. For example, the generation unit estimates the user's emotions using a generation AI. For example, if the user is stressed, the generation unit uses the generation AI to generate easy sheet music. For example, if the user is relaxed, the generation unit uses the generation AI to generate intermediate-level sheet music. For example, if the user is in a challenging mood, the generation unit uses the generation AI to generate difficult sheet music. This allows the system to provide sheet music that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit inputs user emotion data into the generation AI and adjusts the difficulty level of the sheet music.

[0101] The instruction unit can estimate the user's emotions and adjust the way instruction is delivered based on the estimated emotions. For example, the instruction unit uses generative AI to estimate the user's emotions. For example, if the user is tense, the generative AI will provide instruction in a gentle tone. For example, if the user is relaxed, the generative AI will provide detailed instruction. For example, if the user is focused, the generative AI will provide strict instruction. This allows the instruction unit to provide instruction that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit is performed using generative AI. For example, the instruction unit inputs user emotion data into the generative AI and adjusts the way instruction is delivered.

[0102] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, the matching unit estimates the user's emotions using a generative AI. For example, if the user is tense, the generative AI suggests members who can help them relax. For example, if the user is relaxed, the generative AI suggests challenging members. For example, if the user is focused, the generative AI suggests members who are also focused. This allows the matching unit to provide matching criteria that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the matching unit are performed using the generative AI. For example, the matching unit inputs the user's emotion data into the generative AI and adjusts the matching criteria.

[0103] The suggestion unit can estimate the user's emotions and adjust the presentation scenario suggestion method based on the estimated user emotions. For example, the suggestion unit estimates the user's emotions using generative AI. For example, if the user is nervous, the suggestion unit has the generative AI suggest a relaxing presentation scenario. For example, if the user is relaxed, the suggestion unit has the generative AI suggest a challenging presentation scenario. For example, if the user is focused, the suggestion unit has the generative AI suggest a presentation scenario that also allows for focus. This makes it possible to provide a presentation scenario suggestion method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit is performed using generative AI. For example, the suggestion unit inputs user emotion data into the generative AI and adjusts the presentation scenario suggestion method.

[0104] The generation unit can generate the optimal sheet music by referring to the user's past performance history during sheet music generation. For example, the generation unit uses a generation AI to analyze the user's past performance history and generate the optimal sheet music. For example, the generation unit uses data of songs the user has played in the past to generate sheet music for similar songs using the generation AI. For example, the generation unit generates sheet music specialized for a specific genre from the user's performance history. For example, the generation unit analyzes the user's past performance data to generate sheet music that helps improve their skills. This makes it possible to provide sheet music based on the user's past performance history. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs the user's past performance data into the generation AI to generate the optimal sheet music.

[0105] The instruction department can select the optimal teaching method by referring to the user's past performance data during instruction. For example, the instruction department uses generative AI to analyze the user's past performance data and select the optimal teaching method. For example, the instruction department may focus on teaching areas that the user has struggled with in the past. For example, the instruction department may provide instruction that helps improve the user's skills based on the user's past performance data. For example, the instruction department may analyze the user's past performance data and suggest the optimal practice method. This allows the instruction department to provide instruction based on the user's past performance data. Some or all of the above processes in the instruction department are performed using generative AI. For example, the instruction department inputs the user's past performance data into the generative AI and selects the optimal teaching method.

[0106] The matching unit can suggest the most suitable members by referring to the user's past performance data during the matching process. For example, the matching unit can analyze the user's past performance data using a generative AI and suggest the most suitable members. For example, the matching unit can use data on songs the user has played in the past to have the generative AI suggest members who play similar songs. For example, the matching unit can suggest members with similar skill levels based on the user's performance data. For example, the matching unit can analyze the user's performance data and suggest the most compatible members. This allows the system to provide the most suitable members based on the user's past performance data. Some or all of the above processes in the matching unit are performed using a generative AI. For example, the matching unit inputs the user's past performance data into the generative AI and suggests the most suitable members.

[0107] The proposal unit can suggest the optimal presentation setting by referring to the user's past presentation history when making a proposal. For example, the proposal unit can analyze the user's past presentation history using generative AI and suggest the optimal presentation setting. For example, the proposal unit can use data on the locations where the user has previously presented to have the generative AI suggest similar presentation settings. For example, the proposal unit can suggest presentation settings that are useful for improving technology based on the user's presentation history. For example, the proposal unit can analyze the user's past presentation data and suggest the most effective presentation setting. This allows the proposal unit to provide presentation settings based on the user's past presentation history. Some or all of the above processing in the proposal unit is performed using generative AI. For example, the proposal unit inputs the user's past presentation history data into the generative AI and suggests the optimal presentation setting.

[0108] The generation unit can prioritize generating region-specific music by considering the user's geographical location information when generating sheet music. For example, the generation unit uses a generation AI to analyze the user's geographical location information and generate region-specific music. For example, if the user is in Japan, the generation AI will generate sheet music for traditional Japanese music. For example, if the user is in the United States, the generation AI will generate sheet music for American pop music. For example, if the user is in Brazil, the generation AI will generate sheet music for samba. This allows for the provision of region-specific music. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's geographical location information into the generation AI and generates region-specific music.

[0109] The proposal unit can prioritize suggesting region-specific presentation scenarios by considering the user's geographical location information during the proposal process. For example, the proposal unit uses generative AI to analyze the user's geographical location information and suggest region-specific presentation scenarios. For example, if the user is in Japan, the generative AI will suggest region-specific presentation scenarios for Japan. For example, if the user is in the United States, the generative AI will suggest region-specific presentation scenarios for the United States. For example, if the user is in Brazil, the generative AI will suggest region-specific presentation scenarios for Brazil. This allows the proposal unit to provide region-specific presentation scenarios. Some or all of the above processing in the proposal unit is performed using generative AI. For example, the proposal unit inputs the user's geographical location information into the generative AI and suggests region-specific presentation scenarios.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The generation unit reads the music and generates the musical score. The generation unit analyzes the music using, for example, a generation AI and generates the musical score. The generation unit handles music from various genres such as classical, pop, and jazz, and takes into account elements such as note placement, tempo, and key. It can also generate musical scores in Braille. Step 2: The instruction unit provides music instruction based on the musical score generated by the generation unit. The instruction unit uses the generation AI to analyze the user's performance and evaluate rhythm and accuracy. Furthermore, it can provide text comments and voice feedback on the user's performance and adjust the instruction content based on the performance history and practice time. Step 3: The matching department matches members based on the user data provided by the instruction department. The matching department proposes the most suitable members by considering data such as the instrument played, genre, skill level, region, age, and gender of the user. Step 4: The proposal team proposes presentation venues based on the members matched by the matching team. The proposal team can suggest venues and locations where presentations can be given, such as concert halls, schools, and outdoor stages. They also provide opportunities for presentations, such as regular presentations and special events.

[0112] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0115] Each of the multiple elements described above, including the generation unit, instruction unit, matching unit, and suggestion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the generation unit is implemented by the computer 36 of the smart device 14 and the processor 28 of the data processing unit 12, and reads a song and generates a musical score. The instruction unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12, and analyzes the user's performance and evaluates the rhythm and accuracy. The matching unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12, and suggests the optimal members based on the user's data. The suggestion unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12, and suggests venues and locations where performances can be held. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] As shown in Figure 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.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the generation unit, instruction unit, matching unit, and suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the computer 36 of the smart glasses 214 and the processor 28 of the data processing unit 12, and reads a song and generates a musical score. The instruction unit is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12, and analyzes the user's performance and evaluates the rhythm and accuracy. The matching unit is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12, and suggests the optimal members based on the user's data. The suggestion unit is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12, and suggests venues and locations where a performance can be held. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the generation unit, instruction unit, matching unit, and suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the computer 36 of the headset terminal 314 and the processor 28 of the data processing unit 12, and reads a song and generates a musical score. The instruction unit is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12, and analyzes the user's performance and evaluates the rhythm and accuracy. The matching unit is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12, and suggests the optimal members based on the user's data. The suggestion unit is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12, and suggests venues and locations where a performance can be held. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] As shown in Figure 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.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the generation unit, instruction unit, matching unit, and suggestion unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the computer 36 of the robot 414 and the processor 28 of the data processing unit 12, and reads a musical piece and generates a musical score. The instruction unit is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12, and analyzes the user's performance and evaluates the rhythm and accuracy. The matching unit is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12, and suggests the optimal members based on the user's data. The suggestion unit is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12, and suggests venues and locations where performances can be held. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0165] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] 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.

[0175] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) The generation section reads the music and generates the sheet music, A teaching unit that provides music instruction based on the musical score generated by the generation unit, A matching unit that matches members based on user data provided by the aforementioned leadership unit, The system includes a proposal unit that proposes presentation scenarios based on members matched by the matching unit. A system characterized by the following features. (Note 2) The generating unit is Generate Braille musical scores The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned leadership, Evaluate rhythm and accuracy and provide comments. The system described in Appendix 1, characterized by the features described herein. (Note 4) The matching unit is We suggest members based on data such as the instrument the user plays, genre, skill level, region, age, and gender. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Suggest venues and locations where you can give your presentation. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, To provide an opportunity for presentation The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is It estimates the user's emotions and adjusts the difficulty level of the sheet music based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is When generating sheet music, the system references the user's past performance history to generate the most suitable sheet music. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is When generating sheet music, different sheet music generation algorithms are applied depending on the user's playing style. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is It estimates the user's emotions and adjusts the display format of the musical score based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating sheet music, the system prioritizes generating region-specific songs by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating sheet music, the system analyzes the user's social media activity and generates relevant songs. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned leadership, The system estimates the user's emotions and adjusts the way instructions are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned leadership, During instruction, the system selects the optimal teaching method by referring to the user's past performance data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned leadership, During instruction, different teaching algorithms are applied depending on the user's playing style. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned leadership, It estimates the user's emotions and adjusts the length of instruction based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned leadership, During instruction, the system prioritizes instruction based on the user's performance history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned leadership, During instruction, refer to relevant literature for the user to improve the accuracy of the instruction. The system described in Appendix 1, characterized by the features described herein. (Note 19) The matching unit is It estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The matching unit is During the matching process, the system will refer to the user's past performance data to suggest the most suitable members. The system described in Appendix 1, characterized by the features described herein. (Note 21) The matching unit is During the matching process, different matching algorithms are applied depending on the user's playing style. The system described in Appendix 1, characterized by the features described herein. (Note 22) The matching unit is It estimates the user's sentiment and adjusts the order in which matching results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The matching unit is During the matching process, the system prioritizes suggesting members specific to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The matching unit is During the matching process, the system analyzes the user's social media activity and suggests relevant members. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the presentation scenarios based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, we refer to the user's past presentation history to suggest the most suitable presentation setting. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, different presentation scenario suggestion algorithms are applied depending on the user's performance style. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, It estimates user emotions and prioritizes presentation scenes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making proposals, prioritize suggesting region-specific presentation scenarios, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and suggest relevant announcement opportunities. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The generation section reads the music and generates the sheet music, A teaching unit that provides music instruction based on the musical score generated by the generation unit, A matching unit that matches members based on user data provided by the aforementioned leadership unit, The system includes a proposal unit that proposes presentation scenarios based on members matched by the matching unit. A system characterized by the following features.

2. The generating unit is Generate Braille musical scores The system according to feature 1.

3. The aforementioned leadership, Evaluate rhythm and accuracy and provide comments. The system according to feature 1.

4. The matching unit is We suggest members based on data such as the instrument the user plays, genre, skill level, region, age, and gender. The system according to feature 1.

5. The aforementioned proposal section is, Suggest venues and locations where you can give your presentation. The system according to feature 1.

6. The aforementioned proposal section is, To provide an opportunity for presentation The system according to feature 1.

7. The generating unit is It estimates the user's emotions and adjusts the difficulty level of the sheet music based on those estimated emotions. The system according to feature 1.

8. The generating unit is When generating sheet music, the system references the user's past performance history to generate the most suitable sheet music. The system according to feature 1.

9. The generating unit is When generating sheet music, different sheet music generation algorithms are applied depending on the user's playing style. The system according to feature 1.

10. The generating unit is It estimates the user's emotions and adjusts the display format of the musical score based on those emotions. The system according to feature 1.

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