System

Abstract

An object of a system according to an embodiment is to provide a musical piece corresponding to a learning level of a musical instrument of a user and to lower a hurdle of practice.SOLUTION: A system includes a reception unit, a generation unit, and a feedback unit. The reception unit inputs a user's skill level of a musical instrument. The generation unit adjusts the difficulty level of the musical piece based on the information input by the reception unit. The feedback unit tracks the progress of the user based on the score generated by the generation unit and suggests the next practice step.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult for beginner instrument players to find music that suited their level of proficiency, making practice difficult.

[0005] The system according to the embodiment aims to provide music that matches the user's level of proficiency with the instrument, thereby lowering the barrier to practice. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, and a feedback unit. The receiving unit receives input of a user's proficiency with a musical instrument. The generating unit adjusts the difficulty level of a piece of music based on the information received by the receiving unit. The feedback unit tracks the user's progress based on the score generated by the generating unit and suggests the user's next practice step. [Effects of the Invention]

[0007] The system according to the embodiment provides music that matches the user's level of proficiency with the instrument, thereby lowering the barrier to practice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A musical score generation system according to an embodiment of the present invention automatically generates musical scores tailored to a user's instrument proficiency, supporting efficient practice. The musical score generation system inputs the user's instrument proficiency, adjusts the difficulty of the music piece based on that information, tracks the user's progress based on the generated musical score, and suggests next practice steps. For example, the musical score generation system requires the user to input their own instrument proficiency. For example, the user is required to specifically input their playing experience and technical level. The musical score generation system then uses a generation AI to analyze the input information and adjust the difficulty of the music piece based on the user's proficiency. For example, the generation AI analyzes each part of the music piece and uses an algorithm to gradually adjust the difficulty level. The generated musical score is designed to gradually increase in difficulty according to the user's proficiency. For example, the user may initially play only the basic melody line, gradually adding chords and ornaments. Furthermore, the musical score generation system includes a feedback system that tracks the user's progress and suggests next practice steps. For example, as the user practices, the generation AI analyzes a recording of the performance and suggests next practice steps. This allows the music score generation system to use scores that match the user's level of proficiency, allowing for efficient practice. This allows even beginners to effortlessly practice their favorite pieces of music. For example, users can practice efficiently by using scores that match their own level of proficiency. In addition, because the generation AI automatically generates scores, users can always obtain the latest scores. This makes it possible for the music score generation system to eliminate the hurdles to musical practice for all of humanity.

[0029] A musical score generation system according to an embodiment includes a receiving unit, a generating unit, and a feedback unit. The receiving unit inputs the user's musical instrument proficiency. The user's musical instrument proficiency includes, but is not limited to, the number of years of playing experience, a list of songs the user can play, and technical skills. For example, the receiving unit requests the user to specifically input their own playing experience and technical level. The receiving unit can also input the user's number of years of playing experience, a list of songs the user can play, and technical skills. The generating unit uses a generation AI to adjust the difficulty of the musical piece based on the information input by the receiving unit. For example, the generating unit analyzes each part of the musical piece and uses an algorithm that gradually adjusts the difficulty. The generating unit can also simplify complex chord progressions and fast passages to make them easier to play even for beginners. The generating unit generates musical scores with gradually increasing difficulty levels according to the user's proficiency. For example, the generating AI analyzes each part of the musical piece and uses an algorithm that gradually adjusts the difficulty level. The generation AI simplifies complex chord progressions and fast passages, converting them into a form that is easy for even beginners to play. The generation AI generates sheet music that gradually increases in difficulty according to the user's level of proficiency. The feedback unit tracks the user's progress based on the sheet music generated by the generation unit and suggests the next practice step. The feedback unit, for example, analyzes a recording of the user's performance and suggests the next practice step. The feedback unit is equipped with a feedback system that tracks the user's progress and suggests the next practice step. As a result, the sheet music generation system according to the embodiment can automatically generate sheet music that matches the user's level of proficiency with the instrument, supporting efficient practice.

[0030] The receiving unit can input the user's years of performance experience, a list of songs the user can play, and technical skills. Examples of years of performance experience include, but are not limited to, actual playing time and practice time. For example, the receiving unit requests the user to input their own years of performance experience. The receiving unit can also input a list of songs the user can play. Examples of the list of songs the user can play include, but are not limited to, song titles, composers, and difficulty levels. The receiving unit can also input the user's technical skills. Examples of technical skills include, but are not limited to, scale playing speed and chord change accuracy. By inputting detailed performance information about the user, more accurate sheet music can be generated. Some or all of the above-described processing by the receiving unit may be performed using, or without, AI. For example, the receiving unit may provide an interface for inputting the user's years of performance experience, a list of songs the user can play, and technical skills, and the input information can be analyzed by AI.

[0031] The generation unit can use an algorithm that analyzes each part of a musical piece and gradually adjusts the difficulty level. Examples of parts of a musical piece include, but are not limited to, melody, harmony, and rhythm sections. For example, the generation unit can analyze a melody part of a musical piece and gradually adjust the difficulty level. The generation unit can also analyze a harmony part of a musical piece and gradually adjust the difficulty level. The generation unit can also analyze a rhythm section of a musical piece and gradually adjust the difficulty level. Examples of algorithms that gradually adjust the difficulty level include, but are not limited to, machine learning algorithms and rule-based algorithms. For example, the generation unit can analyze each part of a musical piece using a machine learning algorithm and gradually adjust the difficulty level. The generation unit can also analyze each part of a musical piece using a rule-based algorithm and gradually adjust the difficulty level. This allows the generation unit to analyze each part of a musical piece and generate a score that corresponds to the user's level of proficiency. Some or all of the above-described processing in the generation unit can be performed using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input each part of a song into the generation AI and have the generation AI adjust the difficulty level.

[0032] The generation unit can simplify complex chord progressions and fast passages, converting them into a form that is easy for beginners to play. Examples of complex chord progressions include, but are not limited to, chord types and progression patterns. For example, the generation unit can simplify complex chord progressions, converting them into a form that is easy for beginners to play. The generation unit can also simplify fast passages, converting them into a form that is easy for beginners to play. Examples of fast passages include, but are not limited to, tempo and note types. For example, the generation unit can slow the tempo of a fast passage, converting it into a form that is easy for beginners to play. The generation unit can also simplify note types, converting it into a form that is easy for beginners to play. This allows for the generation of sheet music that is easy for beginners to play. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input complex chord progressions and fast passages into the generation AI and have the generation AI perform the simplification.

[0033] The feedback unit can analyze a recording of the user's performance and suggest the next practice step. Analyzing the recording can be done using, for example, voice recognition technology or a method for evaluating the accuracy of the performance, but is not limited to these examples. For example, the feedback unit can analyze the recording of the user's performance using voice recognition technology. The feedback unit can also evaluate the accuracy of the performance and suggest the next practice step. Furthermore, the feedback unit can analyze the recording of the user's performance and suggest specific areas for improvement. This makes it possible to provide feedback according to the progress of the user's performance. Some or all of the above-described processing in the feedback unit can be performed using AI, or can be performed without using AI. For example, the feedback unit can input the recording of the user's performance into AI and have the AI ​​perform the analysis and suggest the next practice step.

[0034] The generation unit can generate a musical score whose difficulty level increases gradually according to the user's proficiency. Examples of increasing the difficulty level include, but are not limited to, a method for setting practice steps and a difficulty evaluation standard. For example, the generation unit sets practice steps based on the user's proficiency and generates a musical score whose difficulty level increases gradually. The generation unit can also generate a musical score based on a difficulty evaluation standard. This makes it possible to provide a musical score whose difficulty level increases gradually according to the user's proficiency. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can set practice steps based on the user's proficiency and cause the generation AI to execute a musical score whose difficulty level increases gradually.

[0035] The reception unit can automatically acquire the user's past performance history, thereby reducing the effort of inputting information. Past performance history includes, for example, a list of songs played, performance time, and performance evaluations, but is not limited to these examples. For example, the reception unit can automatically acquire a list of songs the user has played in the past and display it as an input item. The reception unit can also prioritize and display songs that have been played frequently based on the user's past performance history. Furthermore, the reception unit can automatically suggest songs based on the user's past performance history, according to the user's proficiency level. By automatically acquiring the user's past performance history, the effort of inputting information can be reduced. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's past performance history into AI and have the AI ​​analyze the performance history.

[0036] The reception unit can customize the input items taking into account the user's playing style and preferences. Examples of playing styles include, but are not limited to, classical, jazz, and rock. The reception unit customizes the input items, for example, by using the user's preferred instrument type and genre. The reception unit can also adjust the order and content of the input items to match the user's playing style. Furthermore, the reception unit can suggest input items that match the user's preferences based on the user's past selection history. Examples of preferences include, but are not limited to, favorite song genres and favorite artists. This makes it possible to provide input items that match the user's playing style and preferences. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's playing style and preferences into AI and have the AI ​​customize the input items.

[0037] The reception unit can analyze the user's input data in real time and provide appropriate input assistance. Examples of real-time analysis include, but are not limited to, streaming data analysis and real-time feedback technology. For example, the reception unit can analyze the input content in real time while the user is typing and display appropriate completion candidates. The reception unit can also perform real-time error checks and suggest corrections based on the data entered by the user. Furthermore, the reception unit can provide relevant information in real time while the user is typing to support the input process. Examples of appropriate input assistance include, but are not limited to, an autocomplete function and input guides. This allows the user's input data to be analyzed in real time and appropriate input assistance to be provided. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's input data to AI and have the AI ​​perform real-time analysis and input assistance.

[0038] The reception unit can suggest regional music and styles taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, the reception unit can suggest regionally popular music based on the user's current location. The reception unit can also suggest regional performance styles and techniques based on the user's geographical location information. Furthermore, the reception unit can suggest regional music events and workshops based on the user's geographical location information. Examples of regional music and styles include, but are not limited to, traditional music and popular artists. This allows the reception unit to suggest music and styles based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's geographical location information into AI and have the AI ​​suggest regional music and styles.

[0039] The reception unit can analyze the user's social media activity and automatically input relevant performance experience and technical skills. Social media activity includes, but is not limited to, the content of posts, follower reactions, and analytical tools used. For example, the reception unit can analyze the user's performance videos on social media and automatically input the performance experience and technical skills. The reception unit can also automatically input relevant performance experience and technical skills based on the content of the user's social media posts. Furthermore, the reception unit can automatically input relevant performance experience and technical skills based on the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and the relevant performance experience and technical skills can be automatically input. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's social media activity into AI and have the AI ​​automatically input the performance experience and technical skills.

[0040] The reception unit can customize the input interface by reflecting the user's past feedback. Past feedback includes, but is not limited to, the user's evaluation comments and feedback history. For example, the reception unit can adjust the design of the input interface based on the user's past feedback. The reception unit can also customize the order and content of input items based on the user's past feedback. Furthermore, the reception unit can improve the function of the input interface based on the user's past feedback. In this way, the input interface can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI or without AI. For example, the reception unit can input the user's past feedback into AI and have the AI ​​customize the input interface.

[0041] The generation unit can analyze each part of a musical piece and apply different score generation algorithms depending on the user's level of proficiency. Each part of a musical piece can include, but is not limited to, a melody, a harmony, a rhythm section, etc. For example, the generation unit can analyze a melody part of a musical piece and apply different score generation algorithms depending on the user's level of proficiency. The generation unit can also analyze a harmony part of a musical piece and apply different score generation algorithms depending on the user's level of proficiency. Furthermore, the generation unit can analyze a rhythm section of a musical piece and apply different score generation algorithms depending on the user's level of proficiency. Examples of different score generation algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. For example, the generation unit can generate a score that includes only a simple melody line for beginners. For intermediate players, the generation unit can also generate a score that includes chords and ornaments. For advanced players, the generation unit can also generate a score that includes complex chord progressions and fast passages. This allows a score generation algorithm to be provided that corresponds to the user's level of proficiency. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit may input each part of a piece of music into the generation AI and have the generation AI execute a score generation algorithm that corresponds to the user's level of proficiency.

[0042] The generation unit can improve the accuracy of musical score generation by referring to the user's past performance data. Past performance data includes, for example, a list of songs played, performance time, and performance evaluations, but is not limited to these examples. The generation unit, for example, generates easy-to-play musical scores based on the user's past performance data. The generation unit can also analyze the user's past performance data and generate musical scores that match the user's performance habits and preferences. Furthermore, the generation unit can generate musical scores with adjusted performance difficulty by referring to the user's past performance data. This allows the accuracy of musical score generation to be improved by referring to the user's past performance data. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past performance data into the generation AI and cause the generation AI to improve the accuracy of musical score generation.

[0043] The generation unit can customize the score based on the user's playing style and preferences when generating the score. Performance styles include, but are not limited to, classical, jazz, and rock. The generation unit generates the score to match the user's preferred playing style. The generation unit can also customize the presentation and layout of the score based on the user's preferences. Furthermore, the generation unit can generate a score that matches the user's preferences based on the user's past selection history. Preferences include, but are not limited to, favorite song genres and favorite artists. This makes it possible to provide a score based on the user's playing style and preferences. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's playing style and preferences into the generation AI and have the generation AI customize the score.

[0044] When generating a musical score, the generation unit can prioritize generating regional music by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP address. For example, the generation unit can generate music popular in a region based on the user's current location. The generation unit can also generate a musical score that reflects regional performance styles and techniques based on the user's geographical location information. Furthermore, the generation unit can generate a musical score related to a regional music event or workshop based on the user's geographical location information. Examples of regional music include, but are not limited to, traditional music of the region or popular artists of the region. This allows for the provision of regional music based on the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and cause the generation AI to generate a regional music.

[0045] When generating the musical score, the generation unit can analyze the user's social media activity and suggest related music. Social media activity includes, but is not limited to, the content of posts, the reactions of followers, and the analytical tools used. For example, the generation unit can analyze the user's performance videos on social media and suggest related music. The generation unit can also suggest related music based on the content of the user's social media posts. Furthermore, the generation unit can suggest related music based on the activity of the user's friends on social media. This makes it possible to provide related music based on the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's social media activity into the generation AI and have the generation AI suggest related music.

[0046] When generating a musical score, the generation unit can adjust the generation algorithm by reflecting the user's past feedback. Past feedback includes, but is not limited to, the user's evaluation comments and feedback history. The generation unit adjusts the generation algorithm, for example, based on feedback provided by the user in the past. The generation unit can also improve the expression method and layout of the musical score based on the user's past feedback. Furthermore, the generation unit can adjust the difficulty level of the musical score based on the user's past feedback. This makes it possible to provide a musical score generation algorithm that reflects the user's past feedback. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past feedback into the generation AI and have the generation AI adjust the generation algorithm.

[0047] The feedback unit can analyze a recording of the user's performance and suggest specific improvements. Analyzing the recording can be done using, but is not limited to, voice recognition technology or a method for evaluating the accuracy of the performance. For example, the feedback unit can analyze the recording of the user's performance using voice recognition technology. The feedback unit can also evaluate the accuracy of the performance and suggest specific improvements. Furthermore, the feedback unit can analyze the recording of the user's performance and point out any deviations in rhythm or pitch. This allows the recording of the user's performance to be analyzed and specific improvements to be suggested. Some or all of the above-described processing in the feedback unit can be performed using AI or without AI. For example, the feedback unit can input the recording of the user's performance into AI and have the AI ​​perform the analysis and suggest specific improvements.

[0048] The feedback unit can provide optimal feedback by referring to the user's past feedback history. Examples of the past feedback history include, but are not limited to, the user's evaluation comments and feedback history. For example, the feedback unit can suggest not repeating the same problem based on the user's past feedback history. The feedback unit can also provide feedback according to the user's progress based on the user's past feedback history. Furthermore, the feedback unit can provide feedback to support the improvement of specific technical skills based on the user's past feedback history. This allows optimal feedback to be provided by referring to the user's past feedback history. Some or all of the above-described processing in the feedback unit may be performed using AI, or may be performed without AI. For example, the feedback unit can input the user's past feedback history into AI and cause the AI ​​to provide optimal feedback.

[0049] The feedback unit can customize the feedback content based on the user's playing style and preferences. Examples of playing styles include, but are not limited to, classical, jazz, and rock. The feedback unit provides feedback that matches the user's preferred playing style. The feedback unit can also customize the method of expression and content of the feedback based on the user's preferences. Furthermore, the feedback unit can provide feedback that matches the user's preferences based on the user's past selection history. Examples of preferences include, but are not limited to, favorite song genres and favorite artists. This makes it possible to provide feedback based on the user's playing style and preferences. Some or all of the above-described processing in the feedback unit may be performed using AI or without AI. For example, the feedback unit can input the user's playing style and preferences into AI and have the AI ​​customize the feedback content.

[0050] The feedback unit can provide feedback based on a region-specific playing style, taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP address. For example, the feedback unit can provide feedback tailored to a playing style common in a region, based on the user's current location. The feedback unit can also provide feedback on region-specific techniques and styles, based on the user's geographical location information. Furthermore, the feedback unit can provide feedback related to regional music events and workshops, based on the user's geographical location information. Examples of region-specific playing styles include, but are not limited to, traditional music of a region or popular artists of a region. This allows region-specific feedback to be provided based on the user's geographical location information. Some or all of the above-described processing by the feedback unit can be performed using AI, or can be performed without AI. For example, the feedback unit can input the user's geographical location information into AI and cause the AI ​​to provide region-specific feedback.

[0051] The feedback unit can analyze the user's social media activity and suggest relevant feedback. Social media activity includes, but is not limited to, the content of posts, follower reactions, and analytical tools used. For example, the feedback unit can analyze the user's performance videos on social media and suggest specific areas for improvement. The feedback unit can also provide feedback to support the improvement of related technical skills based on the content of the user's social media posts. Furthermore, the feedback unit can provide relevant feedback based on the activity of the user's friends on social media. This allows for relevant feedback to be provided based on the user's social media activity. Some or all of the above-described processing in the feedback unit may be performed using AI, or may be performed without AI. For example, the feedback unit can input the user's social media activity into AI and have the AI ​​execute relevant feedback suggestions.

[0052] The feedback unit can adjust the feedback algorithm by reflecting the user's past feedback. Past feedback includes, but is not limited to, the user's evaluation comments and feedback history. The feedback unit can adjust the feedback algorithm, for example, based on feedback provided by the user in the past. The feedback unit can also improve the expression method and content of the feedback based on the user's past feedback. Furthermore, the feedback unit can adjust the priority of the feedback based on the user's past feedback. This makes it possible to provide a feedback algorithm that reflects the user's past feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using AI or without AI. For example, the feedback unit can input the user's past feedback into AI and have the AI ​​adjust the feedback algorithm.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The generator can customize the way the score is presented based on the user's playing style. For example, for a user who prefers classical music, the generator can generate a score using traditional notation. For a user who prefers jazz, the generator can provide a score that emphasizes chord progressions and scales for improvisation. Furthermore, for a user who prefers rock, the generator can generate a score that includes guitar tablature and drum notation. This allows the generator to provide a score that suits the user's playing style.

[0055] The reception unit can visualize the progress of a user's practice based on the user's past practice data. For example, it can display past practice time and a list of songs practiced in graphs and charts. It can also provide indicators showing the user's improvement in technical skills. Furthermore, it can analyze the frequency and patterns of the user's practice and suggest effective practice methods. This allows the user to understand the progress of their practice and maintain motivation.

[0056] The feedback unit can analyze recordings of the user's performance and suggest specific areas for improvement. For example, it can point out pitch or rhythm discrepancies and suggest specific practice methods. It can also point out areas for improvement in the expressiveness or dynamics of the performance and provide specific advice. Furthermore, it can analyze recordings of the user's performance and provide feedback to support the improvement of technical skills. This allows the user to identify specific areas for improvement and practice effectively.

[0057] The generation unit can suggest regional music and styles by taking into account the user's geographical location information. For example, if the user is in Japan, traditional Japanese music and pop music can be suggested. If the user is in America, jazz and blues can be suggested. Furthermore, if the user is in Brazil, samba and bossa nova can be suggested. This makes it possible to provide music and styles based on the user's geographical location information.

[0058] The generation unit can analyze the user's social media activity and suggest related songs. For example, it can analyze performance videos shared by the user on social media and suggest songs in the same genre. It can also suggest related songs based on songs liked by the user's followers. It can also suggest new songs and artists based on the user's social media activity. This makes it possible to provide related songs based on the user's social media activity.

[0059] The generation unit can adjust the generation algorithm by reflecting the user's past feedback. For example, the generation algorithm can be adjusted based on feedback provided by the user in the past. The expression method and layout of the score can also be improved based on the user's past feedback. Furthermore, the difficulty level of the score can also be adjusted based on the user's past feedback. In this way, it is possible to provide a score generation algorithm that reflects the user's past feedback.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit inputs the user's instrument proficiency, which includes the number of years of playing experience, a list of songs they can play, and their technical skills. The user is required to specifically input their own playing experience and technical level. Step 2: The generator uses a generation AI to adjust the difficulty of the song based on the information entered by the receiver. The generator analyzes each part of the song and uses an algorithm to gradually adjust the difficulty. For example, it simplifies complex chord progressions and fast passages, converting them into a form that is easy for beginners to play. The generator generates sheet music that gradually increases in difficulty according to the user's level of proficiency. Step 3: The feedback unit tracks the user's progress based on the score generated by the generation unit and suggests next practice steps. The feedback unit analyzes the recording of the user's performance and suggests next practice steps.

[0062] (Example 2) A musical score generation system according to an embodiment of the present invention automatically generates musical scores tailored to a user's instrument proficiency, supporting efficient practice. The musical score generation system inputs the user's instrument proficiency, adjusts the difficulty of the music piece based on that information, tracks the user's progress based on the generated musical score, and suggests next practice steps. For example, the musical score generation system requires the user to input their own instrument proficiency. For example, the user is required to specifically input their playing experience and technical level. The musical score generation system then uses a generation AI to analyze the input information and adjust the difficulty of the music piece based on the user's proficiency. For example, the generation AI analyzes each part of the music piece and uses an algorithm to gradually adjust the difficulty level. The generated musical score is designed to gradually increase in difficulty according to the user's proficiency. For example, the user may initially play only the basic melody line, gradually adding chords and ornaments. Furthermore, the musical score generation system includes a feedback system that tracks the user's progress and suggests next practice steps. For example, as the user practices, the generation AI analyzes a recording of the performance and suggests next practice steps. This allows the music score generation system to use scores that match the user's level of proficiency, allowing for efficient practice. This allows even beginners to effortlessly practice their favorite pieces of music. For example, users can practice efficiently by using scores that match their own level of proficiency. In addition, because the generation AI automatically generates scores, users can always obtain the latest scores. This makes it possible for the music score generation system to eliminate the hurdles to musical practice for all of humanity.

[0063] A musical score generation system according to an embodiment includes a receiving unit, a generating unit, and a feedback unit. The receiving unit inputs the user's musical instrument proficiency. The user's musical instrument proficiency includes, but is not limited to, the number of years of playing experience, a list of songs the user can play, and technical skills. For example, the receiving unit requests the user to specifically input their own playing experience and technical level. The receiving unit can also input the user's number of years of playing experience, a list of songs the user can play, and technical skills. The generating unit uses a generation AI to adjust the difficulty of the musical piece based on the information input by the receiving unit. For example, the generating unit analyzes each part of the musical piece and uses an algorithm that gradually adjusts the difficulty. The generating unit can also simplify complex chord progressions and fast passages to make them easier to play even for beginners. The generating unit generates musical scores with gradually increasing difficulty levels according to the user's proficiency. For example, the generating AI analyzes each part of the musical piece and uses an algorithm that gradually adjusts the difficulty level. The generation AI simplifies complex chord progressions and fast passages, converting them into a form that is easy for even beginners to play. The generation AI generates sheet music that gradually increases in difficulty according to the user's level of proficiency. The feedback unit tracks the user's progress based on the sheet music generated by the generation unit and suggests the next practice step. The feedback unit, for example, analyzes a recording of the user's performance and suggests the next practice step. The feedback unit is equipped with a feedback system that tracks the user's progress and suggests the next practice step. As a result, the sheet music generation system according to the embodiment can automatically generate sheet music that matches the user's level of proficiency with the instrument, supporting efficient practice.

[0064] The receiving unit can input the user's years of performance experience, a list of songs the user can play, and technical skills. Examples of years of performance experience include, but are not limited to, actual playing time and practice time. For example, the receiving unit requests the user to input their own years of performance experience. The receiving unit can also input a list of songs the user can play. Examples of the list of songs the user can play include, but are not limited to, song titles, composers, and difficulty levels. The receiving unit can also input the user's technical skills. Examples of technical skills include, but are not limited to, scale playing speed and chord change accuracy. By inputting detailed performance information about the user, more accurate sheet music can be generated. Some or all of the above-described processing by the receiving unit may be performed using, or without, AI. For example, the receiving unit may provide an interface for inputting the user's years of performance experience, a list of songs the user can play, and technical skills, and the input information can be analyzed by AI.

[0065] The generation unit can use an algorithm that analyzes each part of a musical piece and gradually adjusts the difficulty level. Examples of parts of a musical piece include, but are not limited to, melody, harmony, and rhythm sections. For example, the generation unit can analyze a melody part of a musical piece and gradually adjust the difficulty level. The generation unit can also analyze a harmony part of a musical piece and gradually adjust the difficulty level. The generation unit can also analyze a rhythm section of a musical piece and gradually adjust the difficulty level. Examples of algorithms that gradually adjust the difficulty level include, but are not limited to, machine learning algorithms and rule-based algorithms. For example, the generation unit can analyze each part of a musical piece using a machine learning algorithm and gradually adjust the difficulty level. The generation unit can also analyze each part of a musical piece using a rule-based algorithm and gradually adjust the difficulty level. This allows the generation unit to analyze each part of a musical piece and generate a score that corresponds to the user's level of proficiency. Some or all of the above-described processing in the generation unit can be performed using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input each part of a song into the generation AI and have the generation AI adjust the difficulty level.

[0066] The generation unit can simplify complex chord progressions and fast passages, converting them into a form that is easy for beginners to play. Examples of complex chord progressions include, but are not limited to, chord types and progression patterns. For example, the generation unit can simplify complex chord progressions, converting them into a form that is easy for beginners to play. The generation unit can also simplify fast passages, converting them into a form that is easy for beginners to play. Examples of fast passages include, but are not limited to, tempo and note types. For example, the generation unit can slow the tempo of a fast passage, converting it into a form that is easy for beginners to play. The generation unit can also simplify note types, converting it into a form that is easy for beginners to play. This allows for the generation of sheet music that is easy for beginners to play. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input complex chord progressions and fast passages into the generation AI and have the generation AI perform the simplification.

[0067] The feedback unit can analyze a recording of the user's performance and suggest the next practice step. Analyzing the recording can be done using, for example, voice recognition technology or a method for evaluating the accuracy of the performance, but is not limited to these examples. For example, the feedback unit can analyze the recording of the user's performance using voice recognition technology. The feedback unit can also evaluate the accuracy of the performance and suggest the next practice step. Furthermore, the feedback unit can analyze the recording of the user's performance and suggest specific areas for improvement. This makes it possible to provide feedback according to the progress of the user's performance. Some or all of the above-described processing in the feedback unit can be performed using AI, or can be performed without using AI. For example, the feedback unit can input the recording of the user's performance into AI and have the AI ​​perform the analysis and suggest the next practice step.

[0068] The generation unit can generate a musical score whose difficulty level increases gradually according to the user's proficiency. Examples of increasing the difficulty level include, but are not limited to, a method for setting practice steps and a difficulty evaluation standard. For example, the generation unit sets practice steps based on the user's proficiency and generates a musical score whose difficulty level increases gradually. The generation unit can also generate a musical score based on a difficulty evaluation standard. This makes it possible to provide a musical score whose difficulty level increases gradually according to the user's proficiency. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can set practice steps based on the user's proficiency and cause the generation AI to execute a musical score whose difficulty level increases gradually.

[0069] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user emotions. Techniques for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and biometrics. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the reception unit can collect the user's biometric data with a sensor and estimate the emotion using biometric technology. The design of the input interface can include, but is not limited to, color changes and layout adjustments. For example, if the user is nervous, the reception unit can provide an interface with calm colors to reduce visual stress. If the user is enjoying themselves, the reception unit can provide an interface with bright colors to make input tasks more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple, highly visible interface to make input tasks easier. This makes it possible to provide an input interface that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0070] The reception unit can automatically acquire the user's past performance history, thereby reducing the effort of inputting information. Past performance history includes, for example, a list of songs played, performance time, and performance evaluations, but is not limited to these examples. For example, the reception unit can automatically acquire a list of songs the user has played in the past and display it as an input item. The reception unit can also prioritize and display songs that have been played frequently based on the user's past performance history. Furthermore, the reception unit can automatically suggest songs based on the user's past performance history, according to the user's proficiency level. By automatically acquiring the user's past performance history, the effort of inputting information can be reduced. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's past performance history into AI and have the AI ​​analyze the performance history.

[0071] The reception unit can customize the input items taking into account the user's playing style and preferences. Examples of playing styles include, but are not limited to, classical, jazz, and rock. The reception unit customizes the input items, for example, by using the user's preferred instrument type and genre. The reception unit can also adjust the order and content of the input items to match the user's playing style. Furthermore, the reception unit can suggest input items that match the user's preferences based on the user's past selection history. Examples of preferences include, but are not limited to, favorite song genres and favorite artists. This makes it possible to provide input items that match the user's playing style and preferences. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's playing style and preferences into AI and have the AI ​​customize the input items.

[0072] The reception unit can analyze the user's input data in real time and provide appropriate input assistance. Examples of real-time analysis include, but are not limited to, streaming data analysis and real-time feedback technology. For example, the reception unit can analyze the input content in real time while the user is typing and display appropriate completion candidates. The reception unit can also perform real-time error checks and suggest corrections based on the data entered by the user. Furthermore, the reception unit can provide relevant information in real time while the user is typing to support the input process. Examples of appropriate input assistance include, but are not limited to, an autocomplete function and input guides. This allows the user's input data to be analyzed in real time and appropriate input assistance to be provided. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's input data to AI and have the AI ​​perform real-time analysis and input assistance.

[0073] The reception unit can estimate the user's emotions and prioritize input items based on the estimated user emotions. Techniques for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and biometrics. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the reception unit can collect the user's biometric data with a sensor and estimate the emotion using biometric technology. The prioritization of input items can include, but is not limited to, a prioritization algorithm based on the user's emotional state. For example, when the user is feeling stressed, the reception unit can prioritize displaying the most important input items to simplify the input work. Furthermore, when the user is relaxed, the reception unit can display detailed input items and provide a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize displaying items that can be input most quickly. This makes it possible to provide a priority order of input items according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0074] The reception unit can suggest regional music and styles taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, the reception unit can suggest regionally popular music based on the user's current location. The reception unit can also suggest regional performance styles and techniques based on the user's geographical location information. Furthermore, the reception unit can suggest regional music events and workshops based on the user's geographical location information. Examples of regional music and styles include, but are not limited to, traditional music and popular artists. This allows the reception unit to suggest music and styles based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's geographical location information into AI and have the AI ​​suggest regional music and styles.

[0075] The reception unit can analyze the user's social media activity and automatically input relevant performance experience and technical skills. Social media activity includes, but is not limited to, the content of posts, follower reactions, and analytical tools used. For example, the reception unit can analyze the user's performance videos on social media and automatically input the performance experience and technical skills. The reception unit can also automatically input relevant performance experience and technical skills based on the content of the user's social media posts. Furthermore, the reception unit can automatically input relevant performance experience and technical skills based on the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and the relevant performance experience and technical skills can be automatically input. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's social media activity into AI and have the AI ​​automatically input the performance experience and technical skills.

[0076] The reception unit can customize the input interface by reflecting the user's past feedback. Past feedback includes, but is not limited to, the user's evaluation comments and feedback history. For example, the reception unit can adjust the design of the input interface based on the user's past feedback. The reception unit can also customize the order and content of input items based on the user's past feedback. Furthermore, the reception unit can improve the function of the input interface based on the user's past feedback. In this way, the input interface can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI or without AI. For example, the reception unit can input the user's past feedback into AI and have the AI ​​customize the input interface.

[0077] The generation unit can estimate the user's emotions and adjust the musical score representation method based on the estimated user's emotions. Techniques for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and biometrics. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the generation unit can collect the user's biometric data with a sensor and estimate the emotion using biometrics technology. The musical score representation method can include, but is not limited to, changes in notation and visual emphasis. For example, if the user is relaxed, the generation unit can generate a visually appealing musical score. If the user is nervous, the generation unit can generate a simple, highly visible musical score. Furthermore, if the user is excited, the generation unit can generate a musical score with visually stimulating effects. This makes it possible to provide a musical score representation method that corresponds to 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 may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit may input user facial expression data into the generation AI and cause the generation AI to estimate emotions and adjust the way the musical score is expressed.

[0078] The generation unit can analyze each part of a musical piece and apply different score generation algorithms depending on the user's level of proficiency. Each part of a musical piece can include, but is not limited to, a melody, a harmony, a rhythm section, etc. For example, the generation unit can analyze a melody part of a musical piece and apply different score generation algorithms depending on the user's level of proficiency. The generation unit can also analyze a harmony part of a musical piece and apply different score generation algorithms depending on the user's level of proficiency. Furthermore, the generation unit can analyze a rhythm section of a musical piece and apply different score generation algorithms depending on the user's level of proficiency. Examples of different score generation algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. For example, the generation unit can generate a score that includes only a simple melody line for beginners. For intermediate players, the generation unit can also generate a score that includes chords and ornaments. For advanced players, the generation unit can also generate a score that includes complex chord progressions and fast passages. This allows a score generation algorithm to be provided that corresponds to the user's level of proficiency. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit may input each part of a piece of music into the generation AI and have the generation AI execute a score generation algorithm that corresponds to the user's level of proficiency.

[0079] The generation unit can improve the accuracy of musical score generation by referring to the user's past performance data. Past performance data includes, for example, a list of songs played, performance time, and performance evaluations, but is not limited to these examples. The generation unit, for example, generates easy-to-play musical scores based on the user's past performance data. The generation unit can also analyze the user's past performance data and generate musical scores that match the user's performance habits and preferences. Furthermore, the generation unit can generate musical scores with adjusted performance difficulty by referring to the user's past performance data. This allows the accuracy of musical score generation to be improved by referring to the user's past performance data. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past performance data into the generation AI and cause the generation AI to improve the accuracy of musical score generation.

[0080] The generation unit can customize the score based on the user's playing style and preferences when generating the score. Performance styles include, but are not limited to, classical, jazz, and rock. The generation unit generates the score to match the user's preferred playing style. The generation unit can also customize the presentation and layout of the score based on the user's preferences. Furthermore, the generation unit can generate a score that matches the user's preferences based on the user's past selection history. Preferences include, but are not limited to, favorite song genres and favorite artists. This makes it possible to provide a score based on the user's playing style and preferences. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's playing style and preferences into the generation AI and have the generation AI customize the score.

[0081] The generation unit can estimate the user's emotions and adjust the difficulty level of the musical score based on the estimated user's emotions. Techniques for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and biometrics. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the generation unit can collect the user's biometric data with a sensor and estimate the emotion using biometric technology. The difficulty level of the musical score can include, but is not limited to, the complexity of the tempo and rhythm, the type of chords used, and the like. For example, the generation unit can generate a slightly more difficult musical score if the user is relaxed. Furthermore, the generation unit can generate an easier musical score if the user is nervous. Furthermore, the generation unit can generate a more challenging musical score if the user is excited. This allows the difficulty level of the musical score to be adjusted according to 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 may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions and adjust the difficulty level of the musical score.

[0082] When generating a musical score, the generation unit can prioritize generating regional music by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP address. For example, the generation unit can generate music popular in a region based on the user's current location. The generation unit can also generate a musical score that reflects regional performance styles and techniques based on the user's geographical location information. Furthermore, the generation unit can generate a musical score related to a regional music event or workshop based on the user's geographical location information. Examples of regional music include, but are not limited to, traditional music of the region or popular artists of the region. This allows for the provision of regional music based on the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and cause the generation AI to generate a regional music.

[0083] When generating the musical score, the generation unit can analyze the user's social media activity and suggest related music. Social media activity includes, but is not limited to, the content of posts, the reactions of followers, and the analytical tools used. For example, the generation unit can analyze the user's performance videos on social media and suggest related music. The generation unit can also suggest related music based on the content of the user's social media posts. Furthermore, the generation unit can suggest related music based on the activity of the user's friends on social media. This makes it possible to provide related music based on the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's social media activity into the generation AI and have the generation AI suggest related music.

[0084] When generating a musical score, the generation unit can adjust the generation algorithm by reflecting the user's past feedback. Past feedback includes, but is not limited to, the user's evaluation comments and feedback history. The generation unit adjusts the generation algorithm, for example, based on feedback provided by the user in the past. The generation unit can also improve the expression method and layout of the musical score based on the user's past feedback. Furthermore, the generation unit can adjust the difficulty level of the musical score based on the user's past feedback. This makes it possible to provide a musical score generation algorithm that reflects the user's past feedback. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past feedback into the generation AI and have the generation AI adjust the generation algorithm.

[0085] The feedback unit can estimate the user's emotion and adjust the feedback expression method based on the estimated user's emotion. Techniques for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and biometrics. For example, the feedback unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The feedback unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the feedback unit can collect the user's biometric data with a sensor and estimate the emotion using biometric technology. Feedback expression methods include, but are not limited to, text tone and visual emphasis. For example, the feedback unit can provide detailed feedback when the user is relaxed. For example, the feedback unit can provide simple, highly visible feedback when the user is nervous. For example, the feedback unit can provide feedback with visually stimulating effects when the user is excited. This makes it possible to provide a feedback expression method that corresponds to the user's emotion. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using AI, or may be performed without using AI. For example, the feedback unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions and adjust the feedback expression method.

[0086] The feedback unit can analyze a recording of the user's performance and suggest specific improvements. Analyzing the recording can be done using, but is not limited to, voice recognition technology or a method for evaluating the accuracy of the performance. For example, the feedback unit can analyze the recording of the user's performance using voice recognition technology. The feedback unit can also evaluate the accuracy of the performance and suggest specific improvements. Furthermore, the feedback unit can analyze the recording of the user's performance and point out any deviations in rhythm or pitch. This allows the recording of the user's performance to be analyzed and specific improvements to be suggested. Some or all of the above-described processing in the feedback unit can be performed using AI or without AI. For example, the feedback unit can input the recording of the user's performance into AI and have the AI ​​perform the analysis and suggest specific improvements.

[0087] The feedback unit can provide optimal feedback by referring to the user's past feedback history. Examples of the past feedback history include, but are not limited to, the user's evaluation comments and feedback history. For example, the feedback unit can suggest not repeating the same problem based on the user's past feedback history. The feedback unit can also provide feedback according to the user's progress based on the user's past feedback history. Furthermore, the feedback unit can provide feedback to support the improvement of specific technical skills based on the user's past feedback history. This allows optimal feedback to be provided by referring to the user's past feedback history. Some or all of the above-described processing in the feedback unit may be performed using AI, or may be performed without AI. For example, the feedback unit can input the user's past feedback history into AI and cause the AI ​​to provide optimal feedback.

[0088] The feedback unit can customize the feedback content based on the user's playing style and preferences. Examples of playing styles include, but are not limited to, classical, jazz, and rock. The feedback unit provides feedback that matches the user's preferred playing style. The feedback unit can also customize the method of expression and content of the feedback based on the user's preferences. Furthermore, the feedback unit can provide feedback that matches the user's preferences based on the user's past selection history. Examples of preferences include, but are not limited to, favorite song genres and favorite artists. This makes it possible to provide feedback based on the user's playing style and preferences. Some or all of the above-described processing in the feedback unit may be performed using AI or without AI. For example, the feedback unit can input the user's playing style and preferences into AI and have the AI ​​customize the feedback content.

[0089] The feedback unit can estimate the user's emotions and prioritize feedback based on the estimated user emotions. Techniques for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and biometrics. For example, the feedback unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The feedback unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the feedback unit can collect the user's biometric data with a sensor and estimate the emotion using biometric technology. Feedback prioritization can include, but is not limited to, a prioritization algorithm based on the user's emotional state. For example, if the user is feeling stressed, the feedback unit can prioritize the most important feedback. Furthermore, if the user is relaxed, the feedback unit can provide detailed feedback. Furthermore, if the user is in a hurry, the feedback unit can quickly provide feedback that points out areas for improvement. This allows feedback prioritization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using AI, or may be performed without using AI. For example, the feedback unit may input user facial expression data into the generation AI and cause the generation AI to estimate emotions and determine feedback priorities.

[0090] The feedback unit can provide feedback based on a region-specific playing style, taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP address. For example, the feedback unit can provide feedback tailored to a playing style common in a region, based on the user's current location. The feedback unit can also provide feedback on region-specific techniques and styles, based on the user's geographical location information. Furthermore, the feedback unit can provide feedback related to regional music events and workshops, based on the user's geographical location information. Examples of region-specific playing styles include, but are not limited to, traditional music of a region or popular artists of a region. This allows region-specific feedback to be provided based on the user's geographical location information. Some or all of the above-described processing by the feedback unit can be performed using AI, or can be performed without AI. For example, the feedback unit can input the user's geographical location information into AI and cause the AI ​​to provide region-specific feedback.

[0091] The feedback unit can analyze the user's social media activity and suggest relevant feedback. Social media activity includes, but is not limited to, the content of posts, follower reactions, and analytical tools used. For example, the feedback unit can analyze the user's performance videos on social media and suggest specific areas for improvement. The feedback unit can also provide feedback to support the improvement of related technical skills based on the content of the user's social media posts. Furthermore, the feedback unit can provide relevant feedback based on the activity of the user's friends on social media. This allows for relevant feedback to be provided based on the user's social media activity. Some or all of the above-described processing in the feedback unit may be performed using AI, or may be performed without AI. For example, the feedback unit can input the user's social media activity into AI and have the AI ​​execute relevant feedback suggestions.

[0092] The feedback unit can adjust the feedback algorithm by reflecting the user's past feedback. Past feedback includes, but is not limited to, the user's evaluation comments and feedback history. The feedback unit can adjust the feedback algorithm, for example, based on feedback provided by the user in the past. The feedback unit can also improve the expression method and content of the feedback based on the user's past feedback. Furthermore, the feedback unit can adjust the priority of the feedback based on the user's past feedback. This makes it possible to provide a feedback algorithm that reflects the user's past feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using AI or without AI. For example, the feedback unit can input the user's past feedback into AI and have the AI ​​adjust the feedback algorithm. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described reception unit, generation unit, and feedback unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and inputs the user's proficiency with the instrument. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the difficulty level of the music using a generation AI. For example, the feedback unit is realized by the control unit 46A of the smart device 14 and tracks the user's progress based on the generated sheet music and suggests the next practice step. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, and feedback unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and inputs the user's proficiency with the instrument. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the difficulty level of the music using a generation AI. For example, the feedback unit is realized by the control unit 46A of the smart glasses 214 and tracks the user's progress based on the generated sheet music and suggests the next practice step. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and feedback unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and inputs the user's proficiency with the instrument. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the difficulty level of the music using a generation AI. For example, the feedback unit is realized by the control unit 46A of the headset-type terminal 314 and tracks the user's progress based on the generated musical score and suggests the next practice step. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and feedback unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and inputs the user's proficiency with the instrument. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the difficulty level of the music piece using a generation AI. For example, the feedback unit is realized by the control unit 46A of the robot 414 and tracks the user's progress based on the generated sheet music and suggests the next practice step.

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

[0094] The generator can customize the way the score is presented based on the user's playing style. For example, for a user who prefers classical music, the generator can generate a score using traditional notation. For a user who prefers jazz, the generator can provide a score that emphasizes chord progressions and scales for improvisation. Furthermore, for a user who prefers rock, the generator can generate a score that includes guitar tablature and drum notation. This allows the generator to provide a score that suits the user's playing style.

[0095] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is nervous, the feedback unit can provide words of encouragement or simple suggestions for improvement. If the user is relaxed, the feedback unit can provide detailed technical advice or suggestions for next steps. Furthermore, if the user is excited, the feedback unit can suggest challenging tasks or new techniques. In this way, it is possible to provide appropriate feedback according to the user's emotions.

[0096] The reception unit can visualize the progress of a user's practice based on the user's past practice data. For example, it can display past practice time and a list of songs practiced in graphs and charts. It can also provide indicators showing the user's improvement in technical skills. Furthermore, it can analyze the frequency and patterns of the user's practice and suggest effective practice methods. This allows the user to understand the progress of their practice and maintain motivation.

[0097] The generation unit can estimate the user's emotions and adjust the difficulty of the musical score based on the estimated emotions. For example, if the user is relaxed, a slightly more difficult musical score can be generated. If the user is nervous, an easy musical score can be generated. Furthermore, if the user is excited, a challenging musical score can be generated. In this way, it is possible to provide musical scores with a level of difficulty that corresponds to the user's emotions.

[0098] The feedback unit can analyze recordings of the user's performance and suggest specific areas for improvement. For example, it can point out pitch or rhythm discrepancies and suggest specific practice methods. It can also point out areas for improvement in the expressiveness or dynamics of the performance and provide specific advice. Furthermore, it can analyze recordings of the user's performance and provide feedback to support the improvement of technical skills. This allows the user to identify specific areas for improvement and practice effectively.

[0099] The generation unit can suggest regional music and styles by taking into account the user's geographical location information. For example, if the user is in Japan, traditional Japanese music and pop music can be suggested. If the user is in America, jazz and blues can be suggested. Furthermore, if the user is in Brazil, samba and bossa nova can be suggested. This makes it possible to provide music and styles based on the user's geographical location information.

[0100] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the user is nervous, an interface with subdued colors can be provided to reduce visual stress. If the user is having fun, an interface with bright colors can be provided to make input work more enjoyable. Furthermore, if the user is tired, a simple, highly visible interface can be provided to make input work easier. In this way, an input interface can be provided that corresponds to the user's emotions.

[0101] The generation unit can analyze the user's social media activity and suggest related songs. For example, it can analyze performance videos shared by the user on social media and suggest songs in the same genre. It can also suggest related songs based on songs liked by the user's followers. It can also suggest new songs and artists based on the user's social media activity. This makes it possible to provide related songs based on the user's social media activity.

[0102] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is feeling stressed, the most important feedback can be provided with priority. If the user is relaxed, detailed feedback can be provided. Furthermore, if the user is in a hurry, feedback that quickly points out areas for improvement can be provided. In this way, it is possible to provide the priority of feedback according to the user's emotions.

[0103] The generation unit can adjust the generation algorithm by reflecting the user's past feedback. For example, the generation algorithm can be adjusted based on feedback provided by the user in the past. The expression method and layout of the score can also be improved based on the user's past feedback. Furthermore, the difficulty level of the score can also be adjusted based on the user's past feedback. In this way, it is possible to provide a score generation algorithm that reflects the user's past feedback.

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

[0105] Step 1: The reception unit inputs the user's instrument proficiency, which includes the number of years of playing experience, a list of songs they can play, and their technical skills. The user is required to specifically input their own playing experience and technical level. Step 2: The generator uses a generation AI to adjust the difficulty of the song based on the information entered by the receiver. The generator analyzes each part of the song and uses an algorithm to gradually adjust the difficulty. For example, it simplifies complex chord progressions and fast passages, converting them into a form that is easy for beginners to play. The generator generates sheet music that gradually increases in difficulty according to the user's level of proficiency. Step 3: The feedback unit tracks the user's progress based on the score generated by the generation unit and suggests next practice steps. The feedback unit analyzes the recording of the user's performance and suggests next practice steps.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] 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.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting a user's musical instrument proficiency; a generation unit that adjusts the difficulty level of a piece of music based on the information input by the reception unit; a feedback unit that tracks the user's progress based on the musical score generated by the generation unit and suggests next practice steps. A system characterized by:

2. The reception unit Enter the user's years of playing experience, a list of songs they can play, and their technical skills 2. The system of claim 1.

3. The generation unit It uses an algorithm that analyzes each part of the song and adjusts the difficulty level in stages.

2. The system of claim 1.

4. The generation unit Complex chord progressions and fast passages are simplified to make them easier to play even for beginners.

2. The system of claim 1.

5. The feedback unit Analyzes recordings of users' performances and suggests next practice steps 2. The system of claim 1.

6. The generation unit Generate sheet music with increasing difficulty according to the user's level of proficiency 2. The system of claim 1.

7. The reception unit Estimate user emotions and adjust the design of the input interface based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Automatically retrieves the user's past playing history, eliminating the need to enter information 2. The system of claim 1.

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