system

Abstract

The system according to the embodiment aims to generate musical scores based on music data without requiring specialized knowledge, and to apply for permission from the copyright holder. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and an application unit. The reception unit uploads music data. The analysis unit analyzes the music data uploaded by the reception unit. The generation unit generates a musical score based on the data analyzed by the analysis unit. The application unit submits a permission request to the author based on the musical score generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies had the challenge of a complex process for generating musical scores based on music data and applying for permission from copyright holders, making it difficult to perform without specialized knowledge.

[0005] The system according to the embodiment aims to generate musical scores based on music data without requiring specialized knowledge, and to apply for permission from the copyright holder. [Means for solving the problem]

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and an application unit. The reception unit uploads music data. The analysis unit analyzes the music data uploaded by the reception unit. The generation unit generates a musical score based on the data analyzed by the analysis unit. The application unit submits a permission request to the author based on the musical score generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user without specialized knowledge to generate a musical score based on music data and apply for permission from the copyright holder. [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 of form 1) The auto arranger system according to an embodiment of the present invention allows users to arrange and generate sheet music without specialized knowledge, simply by uploading music data. This auto arranger system solves the problem of people wanting to perform music for club activities, circles, and events, but sheet music is not available commercially. Specifically, users upload music data, and a generation AI analyzes the uploaded music data and arranges the sheet music. The generation AI analyzes elements such as melody, harmony, and rhythm of the music and generates sheet music based on the user's specifications. For example, it can generate sheet music for various arrangements, such as piano scores and band scores. Furthermore, the system also provides permission requests for arrangements from copyright holders. When using sheet music generated by users, the system automatically processes the procedures to obtain permission from the copyright holder. This ensures that arrangement rights are not infringed. This system is in demand among a wide range of users, including music enthusiasts, students, and musical organizations. For example, it can be used when sheet music is needed for performances in school club activities or for club concerts. This allows the auto arranger system to arrange and generate sheet music without specialized knowledge, simply by uploading music data. It can also automatically request permission from the copyright holder for the arrangement, preventing infringement of arrangement rights.

[0029] The auto arranger system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and an application unit. The reception unit allows a user to upload music data. For example, the reception unit provides an interface for users to drag and drop music data and a file selection dialog. The reception unit also checks the format of the uploaded music data and verifies that it is a supported format. The analysis unit analyzes the uploaded music data. The analysis unit uses a generation AI to analyze elements of the music data, such as melody, harmony, and rhythm. For example, the analysis unit uses acoustic analysis technology to extract the melody line of a song and analyze the chord structure. The analysis unit also analyzes rhythm patterns and determines the tempo and beat of the song. The generation unit generates sheet music based on the data analyzed by the analysis unit. The generation unit uses a generation AI to generate sheet music based on user specifications. For example, the generation unit generates sheet music for various arrangements, such as piano sheet music and band sheet music. The generation unit also adjusts the layout and format of the sheet music to provide an easy-to-read sheet music. The application unit is a component that applies for permission to the copyright holder based on the musical score generated by the generation unit. When a user uses the musical score generated by the generation unit, the application unit automatically performs the procedure for obtaining permission from the copyright holder. For example, the application unit inputs necessary information through an online application system and applies for permission. This allows the auto arranger system according to the embodiment to arrange and generate musical scores without specialized knowledge, simply by uploading music data. Furthermore, the system can automatically apply for permission to arrange music from the copyright holder, thereby preventing infringement of arrangement rights.

[0030] The analysis unit can analyze the melody, harmony, and rhythm elements of the music data. The analysis unit, for example, uses acoustic analysis technology to extract the melody line of the music. For example, the analysis unit can analyze pitch changes in the music and identify the melody line. The analysis unit can also use frequency analysis technology to analyze the chord structure. For example, the analysis unit can analyze the frequency spectrum of the music and identify the components of the chord. The analysis unit can also use rhythm analysis technology to analyze the rhythm pattern. For example, the analysis unit can analyze the tempo and beat of the music and identify the rhythm pattern. This enables the analysis unit to perform a detailed analysis of the music data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input music data into a generation AI, which can analyze the melody, harmony, and rhythm elements.

[0031] The generation unit can generate scores for piano or band, as well as scores for multiple ensembles, based on user specifications. The generation unit generates scores based on, for example, the instrumental ensemble specified by the user. For example, to generate scores for piano, the generation unit arranges the music to suit the piano's range and playing technique. To generate scores for band, the generation unit can divide the parts for each instrument and create a balanced arrangement. For example, the generation unit generates scores for bands that include parts for guitar, bass, drums, etc. To generate scores for orchestras, the generation unit can also arrange the parts for each instrument in detail and generate scores that take into account the overall harmony. For example, the generation unit generates scores for orchestras that include parts for string instruments, wind instruments, percussion instruments, etc. This enables the generation unit to generate scores for various ensembles based on user specifications. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the instrument arrangement specified by the user into the generation AI, which can then generate musical scores.

[0032] The application unit can automatically perform the procedure for obtaining permission from the author when using sheet music generated by the user. For example, the application unit can perform the procedure for obtaining permission from the author through an online application system. For example, the application unit can input information about the sheet music generated by the user and automatically generate the necessary documents. The application unit can also automatically obtain the author's contact information and send an email to request permission. For example, the application unit can obtain the author's email address and send an email containing the details of the permission request. The application unit also has a function to notify the user of the progress of the permission request. For example, the application unit will notify the user when the permission request is accepted or when permission is approved. In this way, the application unit can automatically perform the procedure for obtaining permission from the author. Some or all of the above processing in the application unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the application unit can input the details of the permission request into a generation AI, and the generation AI can perform the permission request procedure.

[0033] The reception desk can analyze the user's past upload history and select an appropriate upload method. For example, the reception desk can prioritize suggesting upload methods the user has used in the past (e.g., drag and drop, file selection). For instance, if the reception desk previously uploaded using drag and drop, it will suggest the same method next time. The reception desk can also analyze the time of day when the user previously uploaded and prompt them to upload at the optimal time. For example, if the reception desk previously uploaded at night, it will prompt them to upload at night next time as well. The reception desk can also automatically select a specific format and file size based on the user's past upload history. For example, it can analyze the file formats the user has previously uploaded (e.g., MP3, WAV) and suggest similar formats. This allows the reception desk to select the optimal upload method based on the user's past upload history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's past upload history into a generative AI, which can then select the optimal upload method.

[0034] When uploading music data, the reception unit can filter the music data based on the user's current musical activities and areas of interest. For example, the reception unit prioritizes uploading related music data based on the musical activities (band, orchestra, etc.) in which the user is currently participating. For example, if the user is in a band, the reception unit prioritizes uploading music data for the band. The reception unit can also filter appropriate music data based on the user's areas of interest (classical, jazz, etc.). For example, if the user is interested in classical music, the reception unit prioritizes uploading classical music music data. The reception unit can also analyze the genres of music data previously uploaded by the user and prioritize uploading data of a similar genre. For example, if the user previously uploaded jazz music data, the reception unit prioritizes uploading jazz music data next time. This allows the reception unit to filter music data based on the user's current musical activities and areas of interest. Some or all of the above-described processing by the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception desk can input data on the user's musical activities and areas of interest into a generating AI, which can then filter the data.

[0035] When uploading music data, the reception unit can prioritize uploading highly relevant data in consideration of the user's geographical location information. For example, if the user is active in a specific area, the reception unit prioritizes uploading music data related to that area. For example, if the user is active in a specific city, the reception unit prioritizes uploading music data related to that city. Furthermore, if the user is traveling, the reception unit can prioritize uploading music data related to the user's current location. For example, the reception unit prioritizes uploading music data related to the area the user is traveling to. Furthermore, if the user is participating in a specific event, the reception unit can prioritize uploading music data related to the event. For example, if the user is participating in a music festival, the reception unit prioritizes uploading music data related to the festival. This allows the reception unit to prioritize uploading highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information into the generation AI, which can then prioritize uploading highly relevant data.

[0036] The reception unit can analyze the user's social media activity and upload related data when uploading music data. The reception unit, for example, prioritizes uploading related music data based on music shared by the user on social media. For example, the reception unit uploads related music data based on the genre and artist of the music shared by the user on social media. The reception unit can also prioritize uploading related music data based on artists the user follows on social media. For example, the reception unit prioritizes uploading music data of artists the user follows. The reception unit can also prioritize uploading related music data based on music events the user is participating in on social media. For example, the reception unit uploads music data related to music events the user is participating in. This allows the reception unit to upload related data based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into a generation AI, which then uploads the related data.

[0037] When analyzing music data, the analysis unit can adjust the level of detail of the analysis based on the importance of the music. For example, the analysis unit performs a detailed analysis on music data with high importance to generate a highly accurate score. For example, the analysis unit performs a detailed analysis on music data with a high number of plays or music with high user ratings. The analysis unit can also perform a simplified analysis on music data with low importance to quickly generate a score. For example, the analysis unit performs a simplified analysis on music data with a low number of plays or music with low user ratings. The analysis unit can also perform a balanced analysis on music data with medium importance to generate a score with moderate accuracy. For example, the analysis unit performs a balanced analysis on music data with a medium number of plays or medium user ratings. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the music. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input music importance data to a generation AI, which can adjust the level of detail of the analysis.

[0038] When analyzing music data, the analysis unit can apply different analysis algorithms depending on the genre of the music. For example, the analysis unit applies a classical analysis algorithm to classical music. For example, the analysis unit analyzes chord structures and melody lines in detail based on the characteristics of classical music. The analysis unit can also apply a jazz analysis algorithm to jazz music. For example, the analysis unit analyzes improvisational elements and rhythm patterns based on the characteristics of jazz music. The analysis unit can also apply a pop analysis algorithm to pop music. For example, the analysis unit analyzes catchy melodies and simple chord structures based on the characteristics of pop music. This allows the analysis unit to apply different analysis algorithms depending on the genre of the music. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input music genre data to a generation AI, which then applies an analysis algorithm according to the genre.

[0039] The analysis unit can determine the priority of analysis based on the creation date of the songs when analyzing song data. For example, the analysis unit prioritizes the analysis of the latest song data and quickly generates musical scores. For example, the analysis unit prioritizes the analysis of the latest hit songs and newly released songs. The analysis unit can also postpone the analysis of older song data. For example, the analysis unit postpones the analysis of past hit songs and classic songs. Furthermore, the analysis unit can analyze song data created during a specific period while considering the trends of that period. For example, the analysis unit analyzes songs related to a specific decade or season while considering the trends of that period. This allows the analysis unit to determine the priority of analysis based on the creation date of the songs. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input song creation date data into a generation AI, and the generation AI can determine the priority of analysis.

[0040] The analysis unit can adjust the order of analysis based on the relevance of songs when analyzing music data. For example, the analysis unit analyzes the relevance of music data uploaded by the user and prioritizes the analysis of highly relevant data. For example, the analysis unit analyzes the relevance of genres and artists of songs uploaded by the user and prioritizes the analysis of highly relevant data. The analysis unit can also determine the order of analysis by considering the relevance with music data previously uploaded by the user. For example, the analysis unit determines the order of analysis by considering the relevance of genres and artists of songs previously uploaded by the user. The analysis unit can also adjust the order of analysis based on the relevance of music data specified by the user. For example, the analysis unit adjusts the order of analysis based on the relevance of genres and artists of songs specified by the user. In this way, the analysis unit can adjust the order of analysis based on the relevance of songs. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input music relevance data into a generative AI, and the generative AI can adjust the order of analysis.

[0041] When generating a musical score, the generation unit can adjust the level of detail of the generated score based on the importance of the musical piece. For example, the generation unit generates a detailed musical score for a musical piece with a high importance. For example, the generation unit generates a detailed musical score for a musical piece with a high number of plays or a musical piece with a high user rating. The generation unit can also generate a simplified musical score for a musical piece with a low importance. For example, the generation unit generates a simplified musical score for a musical piece with a low number of plays or a musical piece with a low user rating. The generation unit can also generate a balanced musical score for a musical piece with a medium importance. For example, the generation unit generates a balanced musical score for a musical piece with a medium number of plays or a medium rating. This allows the generation unit to adjust the level of detail of the generated score based on the importance of the musical piece. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input music importance data to the generation AI, which can adjust the level of detail of the generated score.

[0042] When generating a musical score, the generation unit can apply different generation algorithms depending on the genre of the music. For example, the generation unit applies a generation algorithm dedicated to classical music to classical music. For example, the generation unit generates detailed chord structures and melody lines based on the characteristics of classical music. The generation unit can also apply a generation algorithm dedicated to jazz music to jazz music. For example, the generation unit generates improvisation elements and rhythm patterns based on the characteristics of jazz music. The generation unit can also apply a generation algorithm dedicated to pop music to pop music. For example, the generation unit generates catchy melodies and simple chord structures based on the characteristics of pop music. This allows the generation unit to apply different generation algorithms depending on the genre of the music. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input music genre data into the generation AI, which then applies a generation algorithm according to the genre.

[0043] When generating musical scores, the generation unit can determine the priority of generation based on the time when the music was created. For example, the generation unit prioritizes generating musical scores for the latest music. For example, the generation unit prioritizes generating musical scores for the latest hit music or newly released music. The generation unit can also postpone generating musical scores for older music. For example, the generation unit postpones generating musical scores for past hit music or classical music. The generation unit can also generate musical scores for music created during a specific period, taking into account trends of that period. For example, the generation unit generates musical scores for music related to a specific decade or season, taking into account trends of that period. This allows the generation unit to determine the priority of generation based on the time when the music was created. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the time when the music was created into the generation AI, and the generation AI can determine the priority of generation.

[0044] The generation unit can adjust the order of sheet music generation based on the relevance of the songs. For example, the generation unit can analyze the relevance of songs uploaded by the user and prioritize creating sheet music for songs with high relevance. For example, the generation unit can analyze the relevance of genres and artists of songs uploaded by the user and prioritize creating sheet music for songs with high relevance. The generation unit can also determine the order of sheet music generation by considering the relevance of songs previously uploaded by the user. For example, the generation unit can determine the order of sheet music generation by considering the relevance of genres and artists of songs previously uploaded by the user. The generation unit can also adjust the order of sheet music generation based on the relevance of songs specified by the user. For example, the generation unit can adjust the order of sheet music generation based on the relevance of genres and artists of songs specified by the user. In this way, the generation unit can adjust the order of generation based on the relevance of songs. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input song relevance data into a generation AI, and the generation AI can adjust the order of generation.

[0045] When making a license application, the application unit can adjust the level of detail in the application based on the importance of the song. For example, the application unit performs a detailed license application procedure for a song with a high level of importance. For example, the application unit performs a detailed license application procedure for a song with a high number of plays or a song with a high user rating. The application unit can also perform a simplified license application procedure for a song with a low level of importance. For example, the application unit performs a simplified license application procedure for a song with a low number of plays or a song with a low user rating. The application unit can also perform a balanced license application procedure for a song with a medium level of importance. For example, the application unit performs a balanced license application procedure for a song with a medium number of plays or a medium rating. This allows the application unit to adjust the level of detail in the application based on the importance of the song. Some or all of the above-mentioned processing in the application unit may be performed using, or without, a generation AI. For example, the application unit can input song importance data into a generation AI, which can adjust the level of detail in the application.

[0046] When applying for a license, the application unit can apply different application algorithms depending on the genre of the music. For example, the application unit applies a classical-specific application algorithm to classical music. For example, the application unit performs a detailed license application procedure based on the characteristics of classical music. The application unit can also apply a jazz-specific application algorithm to jazz music. For example, the application unit performs a simplified license application procedure based on the characteristics of jazz music. The application unit can also apply a pop-specific application algorithm to pop music. For example, the application unit performs a balanced license application procedure based on the characteristics of pop music. This allows the application unit to apply different application algorithms depending on the genre of the music. Some or all of the above-mentioned processing in the application unit may be performed using, or without, a generation AI. For example, the application unit can input music genre data into the generation AI, which then applies an application algorithm according to the genre.

[0047] When applying for a license, the application unit can determine the priority of the application based on the creation date of the song. For example, the application unit can prioritize the license application procedure for the latest song. For example, the application unit can prioritize the license application procedure for the latest hit song or a newly released song. The application unit can also postpone the license application procedure for older songs. For example, the application unit can postpone the license application procedure for past hit songs or classic songs. The application unit can also perform the license application procedure for songs created during a specific period, taking into account the trends of that period. For example, the application unit can perform the license application procedure for songs related to a specific decade or season, taking into account the trends of that period. This allows the application unit to determine the priority of the application based on the creation date of the song. Some or all of the above-mentioned processing in the application unit may be performed using, or without, a generation AI. For example, the application unit can input song creation date data into the generation AI, which can then determine the priority of the application.

[0048] The request unit can adjust the order of license requests based on the relevance of the songs when making license requests. For example, the request unit analyzes the relevance of songs uploaded by the user and prioritizes license requests for songs with high relevance. For example, the request unit analyzes the relevance of the genres and artists of songs uploaded by the user and prioritizes license requests for songs with high relevance. The request unit can also determine the order of license requests taking into account the relevance of songs previously uploaded by the user. For example, the request unit determines the order of license requests taking into account the relevance of the genres and artists of songs previously uploaded by the user. The request unit can also adjust the order of license requests based on the relevance of songs specified by the user. For example, the request unit adjusts the order of license requests based on the relevance of the genres and artists of songs specified by the user. This allows the request unit to adjust the order of requests based on the relevance of songs. Some or all of the above-described processing in the request unit may be performed using, or without, a generation AI. For example, the request unit can input song relevance data into a generation AI, which can then adjust the order of requests.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The reception unit can learn the user's musical preferences and suggest the most suitable music data to the user based on their past upload and playback history. For example, the reception unit can analyze the genre and artist of songs the user has previously uploaded and suggest music data of similar genres and artists. The reception unit can also analyze the user's playback history, extract the characteristics of songs with a high number of plays, and suggest similar music data. Furthermore, if the reception unit uploads music related to a specific event or season, it can suggest music data related to that event or season. In this way, the reception unit can suggest the most suitable music data based on the user's musical preferences. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's musical preference data into a generative AI, which can then suggest the most suitable music data.

[0051] The analysis unit can adjust the level of detail in its analysis of musical data, taking into account the user's musical skill level. For example, if the user is a beginner, the analysis unit can perform a simplified analysis and generate a basic musical score. If the user is an intermediate player, the analysis unit can perform a detailed analysis and generate a more complex musical score. Furthermore, if the user is an advanced player, the analysis unit can perform a very detailed analysis and generate a professional musical score. In this way, the analysis unit can adjust the level of detail in its analysis according to the user's musical skill level. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the user's skill level data into the generation AI, which can then adjust the level of detail in its analysis.

[0052] The analysis unit can adjust the level of detail of its analysis when analyzing music data, taking into account the cultural background of the music. For example, the analysis unit can perform a detailed analysis of traditional music from a specific region or country, taking its cultural background into consideration. Furthermore, the analysis unit can apply a general analysis algorithm to popular music. In addition, for music related to a specific era or movement, the analysis unit can perform the analysis considering the characteristics of that era or movement. This allows the analysis unit to adjust the level of detail of its analysis based on the cultural background of the music. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input cultural background data of the music into a generative AI, which can then adjust the level of detail of the analysis.

[0053] The generation unit can learn the user's playing style and generate the optimal score during the score generation process. For example, the generation unit can analyze data from songs the user has played in the past and extract the user's playing style (tempo, dynamics, articulation, etc.). Furthermore, if the user plays a specific instrument, the generation unit can generate a score suitable for that instrument. Additionally, if the user plays a specific genre, the generation unit can generate a score suitable for that genre. This allows the generation unit to generate the optimal score based on the user's playing style. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's playing style data into a generation AI, which can then generate the optimal score.

[0054] When applying for a license, the application unit can analyze the user's past application history and suggest the optimal application method. For example, the application unit can prioritize and suggest application methods that the user has used in the past (online application, mail application, etc.). The application unit can also analyze the genres and artists of songs for which the user has previously applied and suggest application methods for similar genres and artists. Furthermore, the application unit can analyze the success rate of obtaining licenses for songs for which the user has previously applied and suggest application methods with a high success rate. This allows the application unit to suggest the optimal application method based on the user's past application history. Some or all of the above-mentioned processing in the application unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the application unit can input the user's application history data into a generation AI, which then suggests the optimal application method.

[0055] When analyzing music data, the analysis unit can adjust the level of detail of the analysis taking into account the commercial value of the music. For example, the analysis unit can perform a detailed analysis on music with high commercial value and generate a highly accurate score. The analysis unit can also perform a simplified analysis on music with low commercial value and generate a score quickly. Furthermore, the analysis unit can perform a balanced analysis on music with medium commercial value and generate a score with moderate accuracy. This allows the analysis unit to adjust the level of detail of the analysis based on the commercial value of the music. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the commercial value data of the music into the generation AI, which can adjust the level of detail of the analysis.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The reception unit is a component through which users upload music data. The reception unit provides, for example, an interface that allows users to drag and drop music data, or a file selection dialog. The reception unit also checks the format of the uploaded music data and verifies that it is a supported format. Step 2: The analysis unit analyzes the uploaded music data. Using the generation AI, the analysis unit analyzes elements of the music data, such as melody, harmony, and rhythm. For example, the analysis unit uses acoustic analysis technology to extract the melody line of the music and analyze the chord structure. The analysis unit also analyzes the rhythm pattern and identifies the tempo and beat of the music. Step 3: The generation unit generates the score based on the data analyzed by the analysis unit. The generation unit uses a generation AI to generate the score based on the user's specifications. For example, the generation unit generates scores for various arrangements, such as scores for piano or band. The generation unit also adjusts the layout and format of the score to provide an easy-to-read score. Step 4: The application unit is a part that applies for permission to the author based on the musical score generated by the generation unit. The application unit automatically performs the procedure to obtain permission from the author when a user uses the musical score generated by the user. For example, the application unit inputs the necessary information through an online application system and applies for permission.

[0058] (Example 2) The auto arranger system according to an embodiment of the present invention allows users to arrange and generate sheet music without specialized knowledge, simply by uploading music data. This auto arranger system solves the problem of people wanting to perform music for club activities, circles, and events, but sheet music is not available commercially. Specifically, users upload music data, and a generation AI analyzes the uploaded music data and arranges the sheet music. The generation AI analyzes elements such as melody, harmony, and rhythm of the music and generates sheet music based on the user's specifications. For example, it can generate sheet music for various arrangements, such as piano scores and band scores. Furthermore, the system also provides permission requests for arrangements from copyright holders. When using sheet music generated by users, the system automatically processes the procedures to obtain permission from the copyright holder. This ensures that arrangement rights are not infringed. This system is in demand among a wide range of users, including music enthusiasts, students, and musical organizations. For example, it can be used when sheet music is needed for performances in school club activities or for club concerts. This allows the auto arranger system to arrange and generate sheet music without specialized knowledge, simply by uploading music data. It can also automatically request permission from the copyright holder for the arrangement, preventing infringement of arrangement rights.

[0059] The auto arranger system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and an application unit. The reception unit allows a user to upload music data. For example, the reception unit provides an interface for users to drag and drop music data and a file selection dialog. The reception unit also checks the format of the uploaded music data and verifies that it is a supported format. The analysis unit analyzes the uploaded music data. The analysis unit uses a generation AI to analyze elements of the music data, such as melody, harmony, and rhythm. For example, the analysis unit uses acoustic analysis technology to extract the melody line of a song and analyze the chord structure. The analysis unit also analyzes rhythm patterns and determines the tempo and beat of the song. The generation unit generates sheet music based on the data analyzed by the analysis unit. The generation unit uses a generation AI to generate sheet music based on user specifications. For example, the generation unit generates sheet music for various arrangements, such as piano sheet music and band sheet music. The generation unit also adjusts the layout and format of the sheet music to provide an easy-to-read sheet music. The application unit is a component that applies for permission to the copyright holder based on the musical score generated by the generation unit. When a user uses the musical score generated by the generation unit, the application unit automatically performs the procedure for obtaining permission from the copyright holder. For example, the application unit inputs necessary information through an online application system and applies for permission. This allows the auto arranger system according to the embodiment to arrange and generate musical scores without specialized knowledge, simply by uploading music data. Furthermore, the system can automatically apply for permission to arrange music from the copyright holder, thereby preventing infringement of arrangement rights.

[0060] The analysis unit can analyze the melody, harmony, and rhythm elements of the music data. The analysis unit, for example, uses acoustic analysis technology to extract the melody line of the music. For example, the analysis unit can analyze pitch changes in the music and identify the melody line. The analysis unit can also use frequency analysis technology to analyze the chord structure. For example, the analysis unit can analyze the frequency spectrum of the music and identify the components of the chord. The analysis unit can also use rhythm analysis technology to analyze the rhythm pattern. For example, the analysis unit can analyze the tempo and beat of the music and identify the rhythm pattern. This enables the analysis unit to perform a detailed analysis of the music data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input music data into a generation AI, which can analyze the melody, harmony, and rhythm elements.

[0061] The generation unit can generate scores for piano or band, as well as scores for multiple ensembles, based on user specifications. The generation unit generates scores based on, for example, the instrumental ensemble specified by the user. For example, to generate scores for piano, the generation unit arranges the music to suit the piano's range and playing technique. To generate scores for band, the generation unit can divide the parts for each instrument and create a balanced arrangement. For example, the generation unit generates scores for bands that include parts for guitar, bass, drums, etc. To generate scores for orchestras, the generation unit can also arrange the parts for each instrument in detail and generate scores that take into account the overall harmony. For example, the generation unit generates scores for orchestras that include parts for string instruments, wind instruments, percussion instruments, etc. This enables the generation unit to generate scores for various ensembles based on user specifications. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the instrument arrangement specified by the user into the generation AI, which can then generate musical scores.

[0062] The application unit can automatically perform the procedure for obtaining permission from the author when using sheet music generated by the user. For example, the application unit can perform the procedure for obtaining permission from the author through an online application system. For example, the application unit can input information about the sheet music generated by the user and automatically generate the necessary documents. The application unit can also automatically obtain the author's contact information and send an email to request permission. For example, the application unit can obtain the author's email address and send an email containing the details of the permission request. The application unit also has a function to notify the user of the progress of the permission request. For example, the application unit will notify the user when the permission request is accepted or when permission is approved. In this way, the application unit can automatically perform the procedure for obtaining permission from the author. Some or all of the above processing in the application unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the application unit can input the details of the permission request into a generation AI, and the generation AI can perform the permission request procedure.

[0063] The reception unit can estimate the user's emotions and adjust the timing of uploading music data based on the estimated user emotions. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expressions and immediately uploads music data if the user is relaxed. The reception unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and, if the user is feeling stressed, temporarily delays the upload until the user calms down. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on fluctuations in heart rate and, if the user is in a hurry, speeds up the upload process and completes it in the shortest time possible. This allows the reception unit to adjust the timing of uploading music data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 the generation AI, or may be performed without using the generation AI. For example, the reception unit may input the user's facial expression data into the generation AI, which may then estimate the emotion and adjust the upload timing.

[0064] The reception desk can analyze the user's past upload history and select an appropriate upload method. For example, the reception desk can prioritize suggesting upload methods the user has used in the past (e.g., drag and drop, file selection). For instance, if the reception desk previously uploaded using drag and drop, it will suggest the same method next time. The reception desk can also analyze the time of day when the user previously uploaded and prompt them to upload at the optimal time. For example, if the reception desk previously uploaded at night, it will prompt them to upload at night next time as well. The reception desk can also automatically select a specific format and file size based on the user's past upload history. For example, it can analyze the file formats the user has previously uploaded (e.g., MP3, WAV) and suggest similar formats. This allows the reception desk to select the optimal upload method based on the user's past upload history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's past upload history into a generative AI, which can then select the optimal upload method.

[0065] When uploading music data, the reception unit can filter the music data based on the user's current musical activities and areas of interest. For example, the reception unit prioritizes uploading related music data based on the musical activities (band, orchestra, etc.) in which the user is currently participating. For example, if the user is in a band, the reception unit prioritizes uploading music data for the band. The reception unit can also filter appropriate music data based on the user's areas of interest (classical, jazz, etc.). For example, if the user is interested in classical music, the reception unit prioritizes uploading classical music music data. The reception unit can also analyze the genres of music data previously uploaded by the user and prioritize uploading data of a similar genre. For example, if the user previously uploaded jazz music data, the reception unit prioritizes uploading jazz music data next time. This allows the reception unit to filter music data based on the user's current musical activities and areas of interest. Some or all of the above-described processing by the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception desk can input data on the user's musical activities and areas of interest into a generating AI, which can then filter the data.

[0066] The reception unit can estimate the user's emotions and prioritize music data to be uploaded based on the estimated user emotions. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression and immediately uploads music data if the user is relaxed. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and, if the user is feeling stressed, temporarily delays the upload until the user calms down. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations and, if the user is in a hurry, speeds up the upload process and completes it in the shortest time possible. This allows the reception unit to prioritize music data to be uploaded based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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, or without, the generation AI. For example, the reception unit may input the user's facial expression data into the generation AI, which may then estimate the emotion and determine the priority of the music data to be uploaded.

[0067] When uploading music data, the reception unit can prioritize uploading highly relevant data in consideration of the user's geographical location information. For example, if the user is active in a specific area, the reception unit prioritizes uploading music data related to that area. For example, if the user is active in a specific city, the reception unit prioritizes uploading music data related to that city. Furthermore, if the user is traveling, the reception unit can prioritize uploading music data related to the user's current location. For example, the reception unit prioritizes uploading music data related to the area the user is traveling to. Furthermore, if the user is participating in a specific event, the reception unit can prioritize uploading music data related to the event. For example, if the user is participating in a music festival, the reception unit prioritizes uploading music data related to the festival. This allows the reception unit to prioritize uploading highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information into the generation AI, which can then prioritize uploading highly relevant data.

[0068] The reception unit can analyze the user's social media activity and upload related data when uploading music data. The reception unit, for example, prioritizes uploading related music data based on music shared by the user on social media. For example, the reception unit uploads related music data based on the genre and artist of the music shared by the user on social media. The reception unit can also prioritize uploading related music data based on artists the user follows on social media. For example, the reception unit prioritizes uploading music data of artists the user follows. The reception unit can also prioritize uploading related music data based on music events the user is participating in on social media. For example, the reception unit uploads music data related to music events the user is participating in. This allows the reception unit to upload related data based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into a generation AI, which then uploads the related data.

[0069] The analysis unit can estimate the user's emotions and adjust the analysis method for music data based on the estimated user emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expressions, and if the user is relaxed, performs a detailed analysis and generates a highly accurate musical score. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice, and if the user is in a hurry, performs a simplified analysis and quickly generates a musical score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on fluctuations in heart rate, and if the user is excited, provides analysis results with visually stimulating effects. This allows the analysis unit to adjust the analysis method for music data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input the user's facial expression data into the generation AI, which may infer the emotion and adjust the analysis method.

[0070] When analyzing music data, the analysis unit can adjust the level of detail of the analysis based on the importance of the music. For example, the analysis unit performs a detailed analysis on music data with high importance to generate a highly accurate score. For example, the analysis unit performs a detailed analysis on music data with a high number of plays or music with high user ratings. The analysis unit can also perform a simplified analysis on music data with low importance to quickly generate a score. For example, the analysis unit performs a simplified analysis on music data with a low number of plays or music with low user ratings. The analysis unit can also perform a balanced analysis on music data with medium importance to generate a score with moderate accuracy. For example, the analysis unit performs a balanced analysis on music data with a medium number of plays or medium user ratings. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the music. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input music importance data to a generation AI, which can adjust the level of detail of the analysis.

[0071] When analyzing music data, the analysis unit can apply different analysis algorithms depending on the genre of the music. For example, the analysis unit applies a classical analysis algorithm to classical music. For example, the analysis unit analyzes chord structures and melody lines in detail based on the characteristics of classical music. The analysis unit can also apply a jazz analysis algorithm to jazz music. For example, the analysis unit analyzes improvisational elements and rhythm patterns based on the characteristics of jazz music. The analysis unit can also apply a pop analysis algorithm to pop music. For example, the analysis unit analyzes catchy melodies and simple chord structures based on the characteristics of pop music. This allows the analysis unit to apply different analysis algorithms depending on the genre of the music. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input music genre data to a generation AI, which then applies an analysis algorithm according to the genre.

[0072] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expressions, and if the user is relaxed, performs a detailed analysis and generates a highly accurate musical score. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice, and if the user is in a hurry, performs a simplified analysis and quickly generates a musical score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations, and if the user is excited, provides analysis results with visually stimulating effects. This allows the analysis unit to determine the priority of analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input the user's facial expression data into the generation AI, which may infer emotions and determine analysis priorities.

[0073] The analysis unit can determine the priority of analysis based on the creation date of the songs when analyzing song data. For example, the analysis unit prioritizes the analysis of the latest song data and quickly generates musical scores. For example, the analysis unit prioritizes the analysis of the latest hit songs and newly released songs. The analysis unit can also postpone the analysis of older song data. For example, the analysis unit postpones the analysis of past hit songs and classic songs. Furthermore, the analysis unit can analyze song data created during a specific period while considering the trends of that period. For example, the analysis unit analyzes songs related to a specific decade or season while considering the trends of that period. This allows the analysis unit to determine the priority of analysis based on the creation date of the songs. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input song creation date data into a generation AI, and the generation AI can determine the priority of analysis.

[0074] The analysis unit can adjust the order of analysis based on the relevance of songs when analyzing music data. For example, the analysis unit analyzes the relevance of music data uploaded by the user and prioritizes the analysis of highly relevant data. For example, the analysis unit analyzes the relevance of genres and artists of songs uploaded by the user and prioritizes the analysis of highly relevant data. The analysis unit can also determine the order of analysis by considering the relevance with music data previously uploaded by the user. For example, the analysis unit determines the order of analysis by considering the relevance of genres and artists of songs previously uploaded by the user. The analysis unit can also adjust the order of analysis based on the relevance of music data specified by the user. For example, the analysis unit adjusts the order of analysis based on the relevance of genres and artists of songs specified by the user. In this way, the analysis unit can adjust the order of analysis based on the relevance of songs. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input music relevance data into a generative AI, and the generative AI can adjust the order of analysis.

[0075] The generation unit can estimate the user's emotions and adjust the method for generating the score based on the estimated user emotions. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expressions and generates a detailed score if the user is relaxed. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and generates a simplified score if the user is in a hurry. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on fluctuations in heart rate and generates a score with visually stimulating effects if the user is excited. This allows the generation unit to adjust the method for generating the score according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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, or without, the generation AI. For example, the generation unit may input the user's facial expression data into the generation AI, which may then estimate the emotion and adjust the generation method.

[0076] When generating a musical score, the generation unit can adjust the level of detail of the generated score based on the importance of the musical piece. For example, the generation unit generates a detailed musical score for a musical piece with a high importance. For example, the generation unit generates a detailed musical score for a musical piece with a high number of plays or a musical piece with a high user rating. The generation unit can also generate a simplified musical score for a musical piece with a low importance. For example, the generation unit generates a simplified musical score for a musical piece with a low number of plays or a musical piece with a low user rating. The generation unit can also generate a balanced musical score for a musical piece with a medium importance. For example, the generation unit generates a balanced musical score for a musical piece with a medium number of plays or a medium rating. This allows the generation unit to adjust the level of detail of the generated score based on the importance of the musical piece. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input music importance data to the generation AI, which can adjust the level of detail of the generated score.

[0077] When generating a musical score, the generation unit can apply different generation algorithms depending on the genre of the music. For example, the generation unit applies a generation algorithm dedicated to classical music to classical music. For example, the generation unit generates detailed chord structures and melody lines based on the characteristics of classical music. The generation unit can also apply a generation algorithm dedicated to jazz music to jazz music. For example, the generation unit generates improvisation elements and rhythm patterns based on the characteristics of jazz music. The generation unit can also apply a generation algorithm dedicated to pop music to pop music. For example, the generation unit generates catchy melodies and simple chord structures based on the characteristics of pop music. This allows the generation unit to apply different generation algorithms depending on the genre of the music. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input music genre data into the generation AI, which then applies a generation algorithm according to the genre.

[0078] The generation unit can estimate the user's emotions and prioritize the scores to be generated based on the estimated user emotions. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expressions and generates a detailed score if the user is relaxed. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and generates a simplified score if the user is in a hurry. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations and generates a score with visually stimulating effects if the user is excited. This allows the generation unit to prioritize the scores to be generated based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-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, which may infer the emotion and determine the priority of the musical score to be generated.

[0079] When generating musical scores, the generation unit can determine the priority of generation based on the time when the music was created. For example, the generation unit prioritizes generating musical scores for the latest music. For example, the generation unit prioritizes generating musical scores for the latest hit music or newly released music. The generation unit can also postpone generating musical scores for older music. For example, the generation unit postpones generating musical scores for past hit music or classical music. The generation unit can also generate musical scores for music created during a specific period, taking into account trends of that period. For example, the generation unit generates musical scores for music related to a specific decade or season, taking into account trends of that period. This allows the generation unit to determine the priority of generation based on the time when the music was created. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the time when the music was created into the generation AI, and the generation AI can determine the priority of generation.

[0080] The generation unit can adjust the order of sheet music generation based on the relevance of the songs. For example, the generation unit can analyze the relevance of songs uploaded by the user and prioritize creating sheet music for songs with high relevance. For example, the generation unit can analyze the relevance of genres and artists of songs uploaded by the user and prioritize creating sheet music for songs with high relevance. The generation unit can also determine the order of sheet music generation by considering the relevance of songs previously uploaded by the user. For example, the generation unit can determine the order of sheet music generation by considering the relevance of genres and artists of songs previously uploaded by the user. The generation unit can also adjust the order of sheet music generation based on the relevance of songs specified by the user. For example, the generation unit can adjust the order of sheet music generation based on the relevance of genres and artists of songs specified by the user. In this way, the generation unit can adjust the order of generation based on the relevance of songs. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input song relevance data into a generation AI, and the generation AI can adjust the order of generation.

[0081] The application unit can estimate the user's emotions and adjust the permission request method based on the estimated emotions. For example, the application unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the application unit can calculate an emotion score based on changes in facial expressions and provide a detailed permission request procedure if the user is relaxed. The application unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the application unit can analyze the tone and speed of the voice and provide a simplified permission request procedure if the user is in a hurry. The application unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the application unit can calculate an emotion score based on fluctuations in heart rate and provide a permission request procedure with visually stimulating effects if the user is excited. This allows the application unit to adjust the permission request method according 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the application section may be performed using a generation AI, or not using a generation AI. For example, the application section may input user facial expression data into a generation AI, which may estimate emotions and adjust the method of the permission application.

[0082] When making a license application, the application unit can adjust the level of detail in the application based on the importance of the song. For example, the application unit performs a detailed license application procedure for a song with a high level of importance. For example, the application unit performs a detailed license application procedure for a song with a high number of plays or a song with a high user rating. The application unit can also perform a simplified license application procedure for a song with a low level of importance. For example, the application unit performs a simplified license application procedure for a song with a low number of plays or a song with a low user rating. The application unit can also perform a balanced license application procedure for a song with a medium level of importance. For example, the application unit performs a balanced license application procedure for a song with a medium number of plays or a medium rating. This allows the application unit to adjust the level of detail in the application based on the importance of the song. Some or all of the above-mentioned processing in the application unit may be performed using, or without, a generation AI. For example, the application unit can input song importance data into a generation AI, which can adjust the level of detail in the application.

[0083] When applying for a license, the application unit can apply different application algorithms depending on the genre of the music. For example, the application unit applies a classical-specific application algorithm to classical music. For example, the application unit performs a detailed license application procedure based on the characteristics of classical music. The application unit can also apply a jazz-specific application algorithm to jazz music. For example, the application unit performs a simplified license application procedure based on the characteristics of jazz music. The application unit can also apply a pop-specific application algorithm to pop music. For example, the application unit performs a balanced license application procedure based on the characteristics of pop music. This allows the application unit to apply different application algorithms depending on the genre of the music. Some or all of the above-mentioned processing in the application unit may be performed using, or without, a generation AI. For example, the application unit can input music genre data into the generation AI, which then applies an application algorithm according to the genre.

[0084] The application unit can estimate the user's emotions and determine the priority of permission requests based on the estimated emotions. For example, the application unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the application unit can calculate an emotion score based on changes in facial expressions and provide a detailed permission request procedure if the user is relaxed. The application unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the application unit can analyze the tone and speed of the voice and provide a simplified permission request procedure if the user is in a hurry. The application unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the application unit can calculate an emotion score based on fluctuations in heart rate and provide a permission request procedure with visually stimulating effects if the user is excited. This allows the application unit to determine the priority of permission requests according 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the application unit may be performed using a generation AI, or not using a generation AI. For example, the application unit can input user facial expression data into a generation AI, which can then estimate emotions and determine the priority of the permission application.

[0085] When applying for a license, the application unit can determine the priority of the application based on the creation date of the song. For example, the application unit can prioritize the license application procedure for the latest song. For example, the application unit can prioritize the license application procedure for the latest hit song or a newly released song. The application unit can also postpone the license application procedure for older songs. For example, the application unit can postpone the license application procedure for past hit songs or classic songs. The application unit can also perform the license application procedure for songs created during a specific period, taking into account the trends of that period. For example, the application unit can perform the license application procedure for songs related to a specific decade or season, taking into account the trends of that period. This allows the application unit to determine the priority of the application based on the creation date of the song. Some or all of the above-mentioned processing in the application unit may be performed using, or without, a generation AI. For example, the application unit can input song creation date data into the generation AI, which can then determine the priority of the application.

[0086] The request unit can adjust the order of license requests based on the relevance of the songs when making license requests. For example, the request unit analyzes the relevance of songs uploaded by the user and prioritizes license requests for songs with high relevance. For example, the request unit analyzes the relevance of the genres and artists of songs uploaded by the user and prioritizes license requests for songs with high relevance. The request unit can also determine the order of license requests taking into account the relevance of songs previously uploaded by the user. For example, the request unit determines the order of license requests taking into account the relevance of the genres and artists of songs previously uploaded by the user. The request unit can also adjust the order of license requests based on the relevance of songs specified by the user. For example, the request unit adjusts the order of license requests based on the relevance of the genres and artists of songs specified by the user. This allows the request unit to adjust the order of requests based on the relevance of songs. Some or all of the above-described processing in the request unit may be performed using, or without, a generation AI. For example, the request unit can input song relevance data into a generation AI, which can then adjust the order of requests. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and application unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the user to upload music data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses generation AI to analyze elements such as melody, harmony, and rhythm of the music data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a musical score based on the user's specifications. The application unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically submits a permission request to the author. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, generation unit, and application 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 control unit 46A of the smart glasses 214 and provides an interface for a user to upload music data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes elements of the music data, such as melody, harmony, and rhythm, using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates musical scores based on user specifications. The application unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically requests permission from the copyright holder. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and application 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 control unit 46A of the headset-type terminal 314 and provides an interface for a user to upload music data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes elements of the music data, such as melody, harmony, and rhythm, using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates musical scores based on user specifications. The application unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically requests permission from the copyright holder. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and application 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 control unit 46A of the robot 414 and provides an interface for users to upload music data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes elements of the music data, such as melody, harmony, and rhythm, using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates musical scores based on user specifications. The application unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically requests permission from the copyright holder.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The reception unit can learn the user's musical preferences and suggest the most suitable music data to the user based on their past upload and playback history. For example, the reception unit can analyze the genre and artist of songs the user has previously uploaded and suggest music data of similar genres and artists. The reception unit can also analyze the user's playback history, extract the characteristics of songs with a high number of plays, and suggest similar music data. Furthermore, if the reception unit uploads music related to a specific event or season, it can suggest music data related to that event or season. In this way, the reception unit can suggest the most suitable music data based on the user's musical preferences. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's musical preference data into a generative AI, which can then suggest the most suitable music data.

[0089] The analysis unit can adjust the level of detail in its analysis of musical data, taking into account the user's musical skill level. For example, if the user is a beginner, the analysis unit can perform a simplified analysis and generate a basic musical score. If the user is an intermediate player, the analysis unit can perform a detailed analysis and generate a more complex musical score. Furthermore, if the user is an advanced player, the analysis unit can perform a very detailed analysis and generate a professional musical score. In this way, the analysis unit can adjust the level of detail in its analysis according to the user's musical skill level. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the user's skill level data into the generation AI, which can then adjust the level of detail in its analysis.

[0090] The generation unit can estimate the user's emotions and adjust the layout and design of the musical score based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a visually calming musical score. If the user is excited, the generation unit can generate a visually stimulating musical score. Furthermore, if the user is sad, the generation unit can generate a visually soothing musical score. In this way, the generation unit can adjust the layout and design of the musical score according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI, which can estimate the emotions and adjust the layout and design of the musical score.

[0091] The application unit can estimate the user's emotions and adjust the method of notifying the user of the progress of the permission application based on the estimated user emotions. For example, if the user is relaxed, the application unit can notify the user of detailed progress. If the user is in a hurry, the application unit can notify the user of simplified progress. Furthermore, if the user is excited, the application unit can notify the user of progress with visually stimulating effects. In this way, the application unit can adjust the method of notifying the user of the progress of the permission application according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the application unit may be performed using a generative AI, or not using a generative AI. For example, the application unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the method of notifying the user of the progress.

[0092] The reception unit can estimate the user's emotions and customize the music data upload interface based on the estimated user emotions. For example, the reception unit can provide a simple and intuitive interface when the user is relaxed. Furthermore, the reception unit can provide a visually stimulating interface when the user is excited. Furthermore, the reception unit can provide a calming interface when the user is stressed. This allows the reception unit to customize the music data upload interface according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can 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 can be performed using, for example, the generation AI, or without the generation AI. For example, the reception unit can input the user's emotion data into the generation AI, which can then estimate the emotion and customize the upload interface.

[0093] The analysis unit can adjust the level of detail of its analysis when analyzing music data, taking into account the cultural background of the music. For example, the analysis unit can perform a detailed analysis of traditional music from a specific region or country, taking its cultural background into consideration. Furthermore, the analysis unit can apply a general analysis algorithm to popular music. In addition, for music related to a specific era or movement, the analysis unit can perform the analysis considering the characteristics of that era or movement. This allows the analysis unit to adjust the level of detail of its analysis based on the cultural background of the music. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input cultural background data of the music into a generative AI, which can then adjust the level of detail of the analysis.

[0094] The generation unit can learn the user's playing style and generate the optimal score during the score generation process. For example, the generation unit can analyze data from songs the user has played in the past and extract the user's playing style (tempo, dynamics, articulation, etc.). Furthermore, if the user plays a specific instrument, the generation unit can generate a score suitable for that instrument. Additionally, if the user plays a specific genre, the generation unit can generate a score suitable for that genre. This allows the generation unit to generate the optimal score based on the user's playing style. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's playing style data into a generation AI, which can then generate the optimal score.

[0095] When applying for a license, the application unit can analyze the user's past application history and suggest the optimal application method. For example, the application unit can prioritize and suggest application methods that the user has used in the past (online application, mail application, etc.). The application unit can also analyze the genres and artists of songs for which the user has previously applied and suggest application methods for similar genres and artists. Furthermore, the application unit can analyze the success rate of obtaining licenses for songs for which the user has previously applied and suggest application methods with a high success rate. This allows the application unit to suggest the optimal application method based on the user's past application history. Some or all of the above-mentioned processing in the application unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the application unit can input the user's application history data into a generation AI, which then suggests the optimal application method.

[0096] The reception desk can estimate the user's emotions and guide the music data upload process based on the estimated emotions. For example, if the user is relaxed, the reception desk can provide step-by-step guidance. If the user is in a hurry, the reception desk can provide simplified guidance. Furthermore, if the user is stressed, the reception desk can provide relaxing guidance. In this way, the reception desk can guide the music data upload process according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using a generative AI, or not using a generative AI. For example, the reception desk can input the user's emotion data into a generative AI, which can estimate the emotions and guide the upload process.

[0097] When analyzing music data, the analysis unit can adjust the level of detail of the analysis taking into account the commercial value of the music. For example, the analysis unit can perform a detailed analysis on music with high commercial value and generate a highly accurate score. The analysis unit can also perform a simplified analysis on music with low commercial value and generate a score quickly. Furthermore, the analysis unit can perform a balanced analysis on music with medium commercial value and generate a score with moderate accuracy. This allows the analysis unit to adjust the level of detail of the analysis based on the commercial value of the music. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the commercial value data of the music into the generation AI, which can adjust the level of detail of the analysis.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The reception unit is a component through which users upload music data. The reception unit provides, for example, an interface that allows users to drag and drop music data, or a file selection dialog. The reception unit also checks the format of the uploaded music data and verifies that it is a supported format. Step 2: The analysis unit analyzes the uploaded music data. Using the generation AI, the analysis unit analyzes elements of the music data, such as melody, harmony, and rhythm. For example, the analysis unit uses acoustic analysis technology to extract the melody line of the music and analyze the chord structure. The analysis unit also analyzes the rhythm pattern and identifies the tempo and beat of the music. Step 3: The generation unit generates the score based on the data analyzed by the analysis unit. The generation unit uses a generation AI to generate the score based on the user's specifications. For example, the generation unit generates scores for various arrangements, such as scores for piano or band. The generation unit also adjusts the layout and format of the score to provide an easy-to-read score. Step 4: The application unit is a part that applies for permission to the author based on the musical score generated by the generation unit. The application unit automatically performs the procedure to obtain permission from the author when a user uses the musical score generated by the user. For example, the application unit inputs the necessary information through an online application system and applies for permission.

[0100] 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.

[0101] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0102] 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.

[0103] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] [Explanation of symbols]

[0172] 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 uploading music data; an analysis unit that analyzes the music data uploaded by the reception unit; a generation unit that generates a musical score based on the data analyzed by the analysis unit; an application unit that applies for permission to the author based on the musical score generated by the generation unit; A system characterized by:

2. The analysis unit Analyzes the melody, harmony, and rhythm elements of music data 2. The system of claim 1.

3. The generation unit Generate scores for piano or band based on user specifications, and for multiple ensembles 2. The system of claim 1.

4. The application department Automatically obtain permission from the copyright holder when using music scores generated by users 2. The system of claim 1.

5. The reception unit Estimates the user's emotions and adjusts the timing of uploading music data based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit Analyze the user's upload history and select the appropriate upload method 2. The system of claim 1.

7. The reception unit When uploading music data, filtering is performed based on the user's current musical activity and interests.

2. The system of claim 1.

8. The reception unit Estimates the user's emotions and prioritizes the music data to be uploaded based on the estimated user emotions.

2. The system of claim 1.

9. The reception unit When uploading music data, the system prioritizes uploading relevant data based on the user's geographic location.

2. The system of claim 1.

10. The reception unit When uploading song data, analyze users' social media activity and upload related data.

2. The system of claim 1.

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