System

Abstract

An object of the system according to the embodiment is to create and release high-quality music without expert knowledge.SOLUTION: A system includes a collection unit, a generation unit, an editing unit, and a release unit. A collection part inputs information on the genre and atmosphere of a musical piece and a musical instrument that a user wants to use. The generation unit generates a melody, a rhythm, and a harmony of the musical piece based on the information collected by the collection unit. The editing unit edits and adjusts the music generated by the generation unit. The release unit releases the music edited and adjusted by the editing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the music production process was complex, making it difficult to create high-quality music without specialized knowledge.

[0005] The system according to the embodiment aims to enable users to create and release high-quality music even without specialized knowledge. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, an editing unit, and a release unit. The collection unit receives input from the user about the genre, atmosphere, and desired instrument information of the music. The generation unit generates the melody, rhythm, and harmony of the music based on the information collected by the collection unit. The editing unit edits and adjusts the music generated by the generation unit. The release unit releases the music edited and adjusted by the editing unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to create and release high-quality music even without specialized knowledge. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) In a musical activity system according to an embodiment of the present invention, a user inputs information about the genre, mood, and instruments of a song, and a generation AI generates, edits, and releases the song. The user inputs information about the genre, mood, and instruments of the song, and the generation AI analyzes that information to generate the song's melody, rhythm, and harmony. The generated song can then be edited and adjusted by the user and finally released. For example, the music activity system allows the user to specify a genre such as pop, rock, or classical, or a cheerful, sad, or relaxed mood. It is also possible to select instruments such as piano, guitar, or drums. This information is input into the generation AI, which then analyzes the input information and generates the song's melody, rhythm, and harmony. The generation AI generates a song that meets the user's specifications based on classic songs and music theory. For example, if a user selects a song in the pop genre with a cheerful mood, the generation AI will generate a melody and rhythm that meets those conditions. The generated song can then be edited and adjusted by the user. For example, it is possible to change parts of the melody or adjust the rhythm. Effects can also be added to change the song's atmosphere. Finally, the completed song can be released worldwide. Users can upload their completed song to an online music distribution service and have it listened to by people all over the world. This allows anyone to easily share their own music and create masterpieces. The Music Activity System utilizes the power of AI to enable anyone to create professional-quality music and share it with the world. Even without knowledge of composition, the AI ​​can generate music, and users can edit and adjust it to create original songs. Furthermore, by releasing the completed song worldwide, it can be created into a masterpiece.

[0029] A musical activity system according to an embodiment includes a collection unit, a generation unit, an editing unit, and a release unit. The collection unit receives input from a user about the genre and atmosphere of a song and the desired musical instruments. The user's input information includes, for example, genres such as pop, rock, and classical, as well as moods such as cheerful, sad, and relaxed. The user can also select musical instruments such as piano, guitar, and drums. The collection unit, for example, analyzes the information received from the user and provides it to a generation AI. The generation unit uses the generation AI to generate a melody, rhythm, and harmony for a song based on the information collected by the collection unit. The generation AI generates a song that meets the user's specifications, for example, based on classic songs and music theory. For example, if a user selects a song in the pop genre with a cheerful atmosphere, the generation unit generates a melody and rhythm that meets those conditions. The editing unit edits and adjusts the song generated by the generation unit. For example, the editing unit can change parts of the melody or adjust the rhythm. It can also add effects to change the atmosphere of the song. The editing unit, for example, allows the user to customize the generated song to their own preferences. The release unit releases the music edited and adjusted by the editing unit. The release unit, for example, uploads the completed music to an online music distribution service so that people all over the world can listen to it. The release unit, for example, enables users to easily distribute their own music and create masterpieces. In this way, the music activity system according to the embodiment allows users to input information such as the genre and atmosphere of the music and the instruments they want to use, and the generation AI generates, edits, and releases the music, allowing anyone to create and distribute professional music.

[0030] The collection unit can analyze the user's past music production history and suggest an appropriate instrument or genre. For example, the collection unit can suggest an optimal instrument based on the instrument the user has frequently used in the past. For example, the collection unit can analyze the genres of music the user has previously produced and suggest an optimal genre. For example, the collection unit can suggest a combination of a specific instrument and genre based on the user's past music production history. In this way, the optimal instrument and genre can be suggested by analyzing the user's past music production history. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's past music data into a generation AI and cause the generation AI to suggest an optimal instrument and genre.

[0031] When collecting songs, the collection unit can filter them based on the user's current mood and activity status. For example, if the user is exercising, the collection unit can suggest energetic songs. For example, if the user is relaxing, the collection unit can also suggest calming songs. For example, if the user is concentrating, the collection unit can also suggest songs that improve concentration. In this way, by filtering songs based on the user's current mood and activity status, more appropriate songs can be suggested. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's activity data into a generation AI and have the generation AI perform filtering.

[0032] When collecting music, the collection unit can select an appropriate collection means depending on the user's input method. For example, when the user inputs the genre and atmosphere of the music by voice, the collection unit collects information using voice recognition technology. For example, when the user inputs the genre and atmosphere of the music by text, the collection unit can also collect information using text analysis technology. For example, when the user inputs the atmosphere of the music by image, the collection unit can also collect information using image analysis technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input voice data to a generation AI and have the generation AI perform voice recognition.

[0033] When collecting songs, the collection unit can prioritize collecting highly relevant song information by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting song information related to that area. For example, if the user is traveling, the collection unit can also prioritize collecting song information related to the travel destination. For example, if the user is participating in a specific event, the collection unit can also prioritize collecting song information related to the event. In this way, highly relevant song information can be collected by taking the user's geographical location information into account. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's location data into the generation AI and cause the generation AI to collect highly relevant song information.

[0034] When collecting songs, the collection unit can analyze the user's social media activities and collect related song information. The collection unit, for example, collects related song information based on song information shared by the user on social media. The collection unit can also, for example, analyze the content of the user's social media posts to collect related song information. The collection unit can also, for example, refer to the activities of the user's friends on social media to collect related song information. In this way, related song information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media data into a generation AI and cause the generation AI to collect related song information.

[0035] When collecting songs, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit preferentially collects information about songs that the user has previously rated highly. For example, the collection unit can also collect information about songs that the user has previously rated poorly, excluding that information. For example, the collection unit can customize the collection method based on the user's past feedback to collect optimal song information. In this way, the collection method can be customized and optimal song information can be collected by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's feedback data into a generation AI and cause the generation AI to customize the collection method.

[0036] The generation unit can adjust the level of detail of the melody and rhythm based on the importance of the music piece during generation. For example, in the case of an important music piece, the generation unit generates a detailed melody and rhythm. For example, in the case of an ordinary music piece, the generation unit can also generate a standard melody and rhythm. For example, in the case of an easy music piece, the generation unit can also generate a simple melody and rhythm. In this way, by adjusting the level of detail of the melody and rhythm based on the importance of the music piece, an appropriate music piece can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input music piece importance data to the generation AI and cause the generation AI to adjust the level of detail of the melody and rhythm.

[0037] During generation, the generation unit can apply different generation algorithms depending on the category of the music. For example, in the case of a pop music piece, the generation unit can apply a generation algorithm specialized for pop music. For example, in the case of a rock music piece, the generation unit can also apply a generation algorithm specialized for rock music. For example, in the case of a classical music piece, the generation unit can also apply a generation algorithm specialized for classical music. In this way, by applying different generation algorithms depending on the category of the music piece, appropriate music can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input music category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0038] During generation, the generation unit can improve the accuracy of generation by referring to the user's past music generation results. The generation unit improves the accuracy of generation, for example, based on data of music generated by the user in the past. The generation unit can also analyze the user's past music generation results and optimize the generation algorithm, for example. The generation unit can also improve the accuracy of generation by referring to the user's past music generation history, for example. In this way, the accuracy of generation can be improved by referring to the user's past music generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past music data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0039] The generation unit can determine the generation priority based on the submission date of the music piece at the time of generation. For example, the generation unit prioritizes generation for music pieces with an approaching deadline. For example, the generation unit can postpone generation for music pieces with a distant submission date. The generation unit can also dynamically adjust the generation priority based on the submission date, for example. This allows for efficient music generation by determining the generation priority based on the submission date of the music piece. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input submission date data into the generation AI and have the generation AI determine the generation priority.

[0040] The generation unit can adjust the order of generation based on the relevance of the songs during generation. For example, the generation unit prioritizes generation of highly relevant songs. For example, the generation unit can also postpone generation of less relevant songs. For example, the generation unit can dynamically adjust the order of generation based on the relevance of the songs. In this way, songs can be generated efficiently by adjusting the order of generation based on the relevance of the songs. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input relevance data of songs to the generation AI and cause the generation AI to adjust the order of generation.

[0041] During generation, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user is a beginner, the generation unit generates music using less technical terminology. For example, if the user is an intermediate user, the generation unit can generate music using appropriate technical terminology. For example, if the user is an advanced user, the generation unit can generate music using more technical terminology. In this way, by adjusting the use of technical terminology in the generation according to the user's level of expertise, an appropriate piece of music can be generated. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's technical knowledge data into the generation AI and cause the generation AI to use technical terminology.

[0042] During editing, the editing unit can adjust the level of detail of the editing based on the importance of the song. For example, in the case of an important song, the editing unit performs detailed editing. For example, in the case of a general song, the editing unit can also perform standard editing. For example, in the case of a simple song, the editing unit can also perform simple editing. In this way, by adjusting the level of detail of the editing based on the importance of the song, appropriate editing becomes possible. Some or all of the above-mentioned processing in the editing unit may be performed using, for example, AI, or may be performed without using AI. For example, the editing unit can input song importance data into the generation AI and have the generation AI adjust the level of detail of the editing.

[0043] During editing, the editing department can apply different editing algorithms depending on the category of the music. For example, in the case of a pop music piece, the editing department can apply an editing algorithm specialized for pop music. For example, in the case of a rock music piece, the editing department can also apply an editing algorithm specialized for rock music. For example, in the case of a classical music piece, the editing department can also apply an editing algorithm specialized for classical music. In this way, by applying different editing algorithms depending on the category of the music piece, appropriate editing becomes possible. Some or all of the above-mentioned processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input music category data into the generation AI and have the generation AI apply the editing algorithm.

[0044] During editing, the editing department can improve the accuracy of editing by referring to the user's past editing results. The editing department improves the accuracy of editing, for example, based on data of edits made by the user in the past. The editing department can also analyze the user's past editing results and optimize the editing algorithm, for example. The editing department can also improve the accuracy of editing by referring to the user's past editing history, for example. In this way, the accuracy of editing can be improved by referring to the user's past editing results. Some or all of the above-mentioned processing in the editing department may be performed using AI, for example, or may be performed without using AI. For example, the editing department can input the user's past editing data into the generation AI and have the generation AI improve the accuracy of editing.

[0045] During editing, the editing department can determine the priority of editing based on the submission date of the song. For example, the editing department prioritizes editing for songs with an approaching deadline. For example, the editing department can postpone editing for songs with a distant submission date. The editing department can also dynamically adjust the priority of editing based on the submission date, for example. This enables efficient editing by determining the priority of editing based on the submission date of the song. Some or all of the above-described processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input submission date data into the generation AI and have the generation AI determine the priority of editing.

[0046] During editing, the editing unit can adjust the editing order based on the relevance of the songs. For example, the editing unit prioritizes editing of highly relevant songs. For example, the editing unit can also postpone editing of less relevant songs. For example, the editing unit can dynamically adjust the editing order based on the relevance of the songs. This allows for efficient editing by adjusting the editing order based on the relevance of the songs. Some or all of the above-described processing in the editing unit may be performed using AI, for example, or may be performed without using AI. For example, the editing unit can input song relevance data into a generation AI and have the generation AI adjust the editing order.

[0047] During editing, the editing department can adjust the use of technical terms in the editing depending on the user's level of expertise. For example, if the user is a beginner, the editing department can edit using less technical terms. For example, if the user is an intermediate user, the editing department can also edit using appropriate technical terms. For example, if the user is an advanced user, the editing department can also edit using more technical terms. This enables appropriate editing by adjusting the use of technical terms in the editing depending on the user's level of expertise. Some or all of the above-mentioned processing in the editing department can be performed using AI, for example, or without AI. For example, the editing department can input the user's technical knowledge data into a generation AI and have the generation AI execute the use of technical terms.

[0048] The release unit can adjust the level of detail of the release based on the importance of the song at the time of release. For example, for an important song, the release unit can provide detailed release procedures. For example, for a general song, the release unit can also provide standard release procedures. For example, for a simple song, the release unit can also provide simple release procedures. This enables appropriate release by adjusting the level of detail of the release based on the importance of the song. Some or all of the above-mentioned processing in the release unit may be performed using AI, for example, or may be performed without using AI. For example, the release unit can input song importance data into a generation AI and have the generation AI adjust the level of detail of the release.

[0049] The release unit can apply different release algorithms depending on the category of the song at the time of release. For example, in the case of a pop song, the release unit can apply a release algorithm specialized for pop. For example, in the case of a rock song, the release unit can also apply a release algorithm specialized for rock. For example, in the case of a classical song, the release unit can also apply a release algorithm specialized for classical. In this way, by applying different release algorithms depending on the category of the song, appropriate release is possible. Some or all of the above-mentioned processing in the release unit may be performed using, for example, AI, or may be performed without using AI. For example, the release unit can input song category data into the generation AI and have the generation AI apply the release algorithm.

[0050] At the time of release, the release unit can improve the accuracy of the release by referring to the user's past release results. The release unit can improve the accuracy of the release, for example, based on data of releases made by the user in the past. The release unit can also analyze the user's past release results and optimize the release algorithm, for example. The release unit can also improve the accuracy of the release by referring to the user's past release history, for example. In this way, the accuracy of the release can be improved by referring to the user's past release results. Some or all of the above-described processing in the release unit can be performed, for example, using AI, or can be performed without using AI. For example, the release unit can input the user's past release data into the generation AI and have the generation AI improve the accuracy of the release.

[0051] At the time of release, the release department can determine the release priority based on the submission date of the song. For example, the release department can prioritize the release of songs with an approaching deadline. For example, the release department can postpone the release of songs with a more distant submission date. The release department can also dynamically adjust the release priority based on the submission date, for example. This enables efficient release by determining the release priority based on the submission date of the song. Some or all of the above-described processing in the release department can be performed using, or without, AI. For example, the release department can input submission date data into a generation AI and have the generation AI determine the release priority.

[0052] The release unit can adjust the release order based on the relevance of the songs at the time of release. For example, the release unit prioritizes the release of highly relevant songs. For example, the release unit can also release less relevant songs later. For example, the release unit can dynamically adjust the release order based on the relevance of the songs. This allows for efficient release by adjusting the release order based on the relevance of the songs. Some or all of the above-described processing in the release unit may be performed using, or without, AI. For example, the release unit can input song relevance data into a generation AI and have the generation AI adjust the release order.

[0053] At the time of release, the release unit can adjust the use of technical terminology in the release according to the user's level of expertise. For example, if the user is a beginner, the release unit can release the release using less technical terminology. For example, if the user is an intermediate user, the release unit can also release the release using appropriate technical terminology. For example, if the user is an advanced user, the release unit can release the release using more technical terminology. This enables an appropriate release by adjusting the use of technical terminology in the release according to the user's level of expertise. Some or all of the above-described processing in the release unit can be performed using AI, for example, or without AI. For example, the release unit can input the user's technical knowledge data into a generation AI and have the generation AI execute the use of technical terminology.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The collection unit can learn the user's musical preferences and automatically extract the characteristics of songs that the user likes. For example, the collection unit can analyze the tempo, key, and instrument combination of songs that the user has previously rated highly, and suggest new songs based on these characteristics. Also, if the user likes a particular artist or band, the collection unit can learn the characteristics of the artist or band's songs and suggest similar songs. Furthermore, the collection unit can learn the musical preferences of the user in specific time periods or situations, and suggest songs that suit those time periods and situations. This enables more personalized song suggestions based on the user's musical preferences.

[0056] The collection unit can analyze the user's music streaming service history and suggest appropriate songs and artists. For example, the collection unit can suggest similar songs and artists based on the user's history of songs played in the past. Furthermore, if the user creates a specific playlist, the collection unit can analyze the characteristics of the songs in the playlist and suggest songs suitable for adding to the playlist. Furthermore, if the user frequently plays a specific genre or artist, the collection unit can suggest new songs related to that genre or artist. This makes it possible to make more appropriate song suggestions by utilizing the user's music streaming service history.

[0057] The collection unit can suggest music effects and arrangements based on the user's current activity status. For example, if the user is exercising, the collection unit can suggest energetic effects and arrangements. If the user is relaxing, the collection unit can suggest calming effects and arrangements. Furthermore, if the user is concentrating, the collection unit can suggest effects and arrangements that enhance concentration. In this way, more appropriate music can be generated by suggesting music effects and arrangements based on the user's current activity status. The collection unit can input the user's activity data into the generation AI and have the generation AI execute the suggested effects and arrangements.

[0058] The collection unit can automatically classify the elements of a song according to the user's input method. For example, when the user inputs the genre and atmosphere of a song by voice, the collection unit can collect information using voice recognition technology and classify the elements of the song. When the user inputs the genre and atmosphere of a song by text, the collection unit can collect information using text analysis technology and classify the elements of the song. Furthermore, when the user inputs the atmosphere of a song by image, the collection unit can collect information using image analysis technology and classify the elements of the song. In this way, by automatically classifying the elements of a song according to the user's input method, information can be efficiently collected and used to create a song.

[0059] The collection unit can suggest regionally specific musical styles and instruments based on the user's geographical location information. For example, if the user is in a specific region, the collection unit can suggest traditional musical styles and instruments from that region. Also, if the user is traveling, the collection unit can suggest musical styles and instruments from the region where the user is traveling. Furthermore, if the user is participating in a specific event, the collection unit can suggest musical styles and instruments related to that event. This makes it possible to generate a wider variety of songs by suggesting regionally specific musical styles and instruments based on the user's geographical location information. The collection unit can input the user's location data into the generation AI and cause the generation AI to suggest regionally specific musical styles and instruments.

[0060] The collection unit can analyze the user's social media activity and suggest songs based on song information shared by the user's friends and followers. For example, the collection unit can collect song information shared by the user's friends and followers on social media and suggest similar songs. It can also analyze the content of the user's social media posts and suggest related songs. Furthermore, if the user follows a specific artist or band, it can suggest new songs related to that artist or band. This enables more personalized song suggestions by analyzing the user's social media activity. The collection unit can input social media data into the generation AI and have the generation AI perform song suggestions.

[0061] The collection unit can dynamically adjust the song suggestion method based on the user's past feedback. For example, the collection unit can analyze the characteristics of songs that the user has previously rated highly and prioritize suggesting similar songs. The collection unit can also analyze the characteristics of songs that the user has previously rated poorly and suggest songs that exclude such songs. Furthermore, the collection unit can customize the song suggestion method based on the user's feedback and suggest songs that match the user's preferences. This enables more appropriate song suggestions by reflecting the user's past feedback. The collection unit can input the user's feedback data into the generation AI and cause the generation AI to adjust the song suggestion method.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The collection unit receives input from the user about the genre and atmosphere of the music, as well as the instruments they wish to use. The information input by the user includes genres such as pop, rock, and classical, as well as moods such as cheerful, sad, and relaxed. It is also possible to select instruments such as piano, guitar, and drums. The collection unit analyzes the information input by the user and provides it to the generation AI. Step 2: The generation unit uses the generation AI to generate the melody, rhythm, and harmony of the song based on the information collected by the collection unit. The generation AI generates a song that meets the user's specifications based on classic songs and music theory. For example, if a song with a bright atmosphere in the pop genre is specified, the generation AI will generate a melody and rhythm that meets those conditions. Step 3: The editor edits and adjusts the music generated by the generator. The editor can change parts of the melody or adjust the rhythm. They can also add effects to change the atmosphere of the music. This allows users to customize the generated music to their own tastes. Step 4: The Releasing Department releases the song that has been edited and adjusted by the Editing Department. The Releasing Department then uploads the completed song to an online music distribution service, allowing it to be listened to by people all over the world. This allows users to easily distribute their own songs and create masterpieces.

[0064] (Example 2) In a musical activity system according to an embodiment of the present invention, a user inputs information about the genre, mood, and instruments of a song, and a generation AI generates, edits, and releases the song. The user inputs information about the genre, mood, and instruments of the song, and the generation AI analyzes that information to generate the song's melody, rhythm, and harmony. The generated song can then be edited and adjusted by the user and finally released. For example, the music activity system allows the user to specify a genre such as pop, rock, or classical, or a cheerful, sad, or relaxed mood. It is also possible to select instruments such as piano, guitar, or drums. This information is input into the generation AI, which then analyzes the input information and generates the song's melody, rhythm, and harmony. The generation AI generates a song that meets the user's specifications based on classic songs and music theory. For example, if a user selects a song in the pop genre with a cheerful mood, the generation AI will generate a melody and rhythm that meets those conditions. The generated song can then be edited and adjusted by the user. For example, it is possible to change parts of the melody or adjust the rhythm. Effects can also be added to change the song's atmosphere. Finally, the completed song can be released worldwide. Users can upload their completed song to an online music distribution service and have it listened to by people all over the world. This allows anyone to easily share their own music and create masterpieces. The Music Activity System utilizes the power of AI to enable anyone to create professional-quality music and share it with the world. Even without knowledge of composition, the AI ​​can generate music, and users can edit and adjust it to create original songs. Furthermore, by releasing the completed song worldwide, it can be created into a masterpiece.

[0065] A musical activity system according to an embodiment includes a collection unit, a generation unit, an editing unit, and a release unit. The collection unit receives input from a user about the genre and atmosphere of a song and the desired musical instruments. The user's input information includes, for example, genres such as pop, rock, and classical, as well as moods such as cheerful, sad, and relaxed. The user can also select musical instruments such as piano, guitar, and drums. The collection unit, for example, analyzes the information received from the user and provides it to a generation AI. The generation unit uses the generation AI to generate a melody, rhythm, and harmony for a song based on the information collected by the collection unit. The generation AI generates a song that meets the user's specifications, for example, based on classic songs and music theory. For example, if a user selects a song in the pop genre with a cheerful atmosphere, the generation unit generates a melody and rhythm that meets those conditions. The editing unit edits and adjusts the song generated by the generation unit. For example, the editing unit can change parts of the melody or adjust the rhythm. It can also add effects to change the atmosphere of the song. The editing unit, for example, allows the user to customize the generated song to their own preferences. The release unit releases the music edited and adjusted by the editing unit. The release unit, for example, uploads the completed music to an online music distribution service so that people all over the world can listen to it. The release unit, for example, enables users to easily distribute their own music and create masterpieces. In this way, the music activity system according to the embodiment allows users to input information such as the genre and atmosphere of the music and the instruments they want to use, and the generation AI generates, edits, and releases the music, allowing anyone to create and distribute professional music.

[0066] The collection unit estimates the user's emotions and suggests music genres and atmospheres based on the estimated user emotions. For example, if the user is relaxed, the collection unit suggests music genres with a relaxed atmosphere. For example, if the user is sad, the collection unit can also suggest music genres with a sad atmosphere. For example, if the user is excited, the collection unit can also suggest music genres with an energetic atmosphere. This allows for the generation of more appropriate music by suggesting music genres and atmospheres 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-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0067] The collection unit can analyze the user's past music production history and suggest an appropriate instrument or genre. For example, the collection unit can suggest an optimal instrument based on the instrument the user has frequently used in the past. For example, the collection unit can analyze the genres of music the user has previously produced and suggest an optimal genre. For example, the collection unit can suggest a combination of a specific instrument and genre based on the user's past music production history. In this way, the optimal instrument and genre can be suggested by analyzing the user's past music production history. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's past music data into a generation AI and cause the generation AI to suggest an optimal instrument and genre.

[0068] When collecting songs, the collection unit can filter them based on the user's current mood and activity status. For example, if the user is exercising, the collection unit can suggest energetic songs. For example, if the user is relaxing, the collection unit can also suggest calming songs. For example, if the user is concentrating, the collection unit can also suggest songs that improve concentration. In this way, by filtering songs based on the user's current mood and activity status, more appropriate songs can be suggested. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's activity data into a generation AI and have the generation AI perform filtering.

[0069] When collecting music, the collection unit can select an appropriate collection means depending on the user's input method. For example, when the user inputs the genre and atmosphere of the music by voice, the collection unit collects information using voice recognition technology. For example, when the user inputs the genre and atmosphere of the music by text, the collection unit can also collect information using text analysis technology. For example, when the user inputs the atmosphere of the music by image, the collection unit can also collect information using image analysis technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input voice data to a generation AI and have the generation AI perform voice recognition.

[0070] The collection unit can estimate the user's emotions and determine the priority of music information to be collected based on the estimated user emotions. For example, when the user is relaxed, the collection unit can prioritize collecting relaxing music information. For example, when the user is sad, the collection unit can also prioritize collecting sad music information. For example, when the user is excited, the collection unit can also prioritize collecting energetic music information. This allows for more appropriate music information to be collected by prioritizing music information 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 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 collection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the collection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0071] When collecting songs, the collection unit can prioritize collecting highly relevant song information by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting song information related to that area. For example, if the user is traveling, the collection unit can also prioritize collecting song information related to the travel destination. For example, if the user is participating in a specific event, the collection unit can also prioritize collecting song information related to the event. In this way, highly relevant song information can be collected by taking the user's geographical location information into account. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's location data into the generation AI and cause the generation AI to collect highly relevant song information.

[0072] When collecting songs, the collection unit can analyze the user's social media activities and collect related song information. The collection unit, for example, collects related song information based on song information shared by the user on social media. The collection unit can also, for example, analyze the content of the user's social media posts to collect related song information. The collection unit can also, for example, refer to the activities of the user's friends on social media to collect related song information. In this way, related song information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media data into a generation AI and cause the generation AI to collect related song information.

[0073] When collecting songs, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit preferentially collects information about songs that the user has previously rated highly. For example, the collection unit can also collect information about songs that the user has previously rated poorly, excluding that information. For example, the collection unit can customize the collection method based on the user's past feedback to collect optimal song information. In this way, the collection method can be customized and optimal song information can be collected by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's feedback data into a generation AI and cause the generation AI to customize the collection method.

[0074] The generation unit can estimate the user's emotions and adjust the method for generating the melody and rhythm of the music based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a relaxed melody and rhythm. For example, if the user is sad, the generation unit can generate a melancholic melody and rhythm. For example, if the user is excited, the generation unit can generate an energetic melody and rhythm. This allows for the generation of a more appropriate music piece by adjusting the method for generating the melody and rhythm of the music 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 can 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 can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the method for generating the melody and rhythm.

[0075] The generation unit can adjust the level of detail of the melody and rhythm based on the importance of the music piece during generation. For example, in the case of an important music piece, the generation unit generates a detailed melody and rhythm. For example, in the case of an ordinary music piece, the generation unit can also generate a standard melody and rhythm. For example, in the case of an easy music piece, the generation unit can also generate a simple melody and rhythm. In this way, by adjusting the level of detail of the melody and rhythm based on the importance of the music piece, an appropriate music piece can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input music piece importance data to the generation AI and cause the generation AI to adjust the level of detail of the melody and rhythm.

[0076] During generation, the generation unit can apply different generation algorithms depending on the category of the music. For example, in the case of a pop music piece, the generation unit can apply a generation algorithm specialized for pop music. For example, in the case of a rock music piece, the generation unit can also apply a generation algorithm specialized for rock music. For example, in the case of a classical music piece, the generation unit can also apply a generation algorithm specialized for classical music. In this way, by applying different generation algorithms depending on the category of the music piece, appropriate music can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input music category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0077] During generation, the generation unit can improve the accuracy of generation by referring to the user's past music generation results. The generation unit improves the accuracy of generation, for example, based on data of music generated by the user in the past. The generation unit can also analyze the user's past music generation results and optimize the generation algorithm, for example. The generation unit can also improve the accuracy of generation by referring to the user's past music generation history, for example. In this way, the accuracy of generation can be improved by referring to the user's past music generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past music data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0078] The generation unit can estimate the user's emotions and adjust the length of the music to be generated based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a longer piece of music. For example, if the user is in a hurry, the generation unit can also generate a shorter piece of music. For example, if the user is concentrating, the generation unit can also generate a piece of music with an appropriate length. This allows for the generation of music of an appropriate length by adjusting the length of the music 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 can 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-mentioned processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the music.

[0079] The generation unit can determine the generation priority based on the submission date of the music piece at the time of generation. For example, the generation unit prioritizes generation for music pieces with an approaching deadline. For example, the generation unit can postpone generation for music pieces with a distant submission date. The generation unit can also dynamically adjust the generation priority based on the submission date, for example. This allows for efficient music generation by determining the generation priority based on the submission date of the music piece. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input submission date data into the generation AI and have the generation AI determine the generation priority.

[0080] The generation unit can adjust the order of generation based on the relevance of the songs during generation. For example, the generation unit prioritizes generation of highly relevant songs. For example, the generation unit can also postpone generation of less relevant songs. For example, the generation unit can dynamically adjust the order of generation based on the relevance of the songs. In this way, songs can be generated efficiently by adjusting the order of generation based on the relevance of the songs. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input relevance data of songs to the generation AI and cause the generation AI to adjust the order of generation.

[0081] During generation, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user is a beginner, the generation unit generates music using less technical terminology. For example, if the user is an intermediate user, the generation unit can generate music using appropriate technical terminology. For example, if the user is an advanced user, the generation unit can generate music using more technical terminology. In this way, by adjusting the use of technical terminology in the generation according to the user's level of expertise, an appropriate piece of music can be generated. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's technical knowledge data into the generation AI and cause the generation AI to use technical terminology.

[0082] The editing department can estimate the user's emotions and adjust the editing expression style based on the estimated user's emotions. For example, if the user is relaxed, the editing department can suggest a relaxed editing style. For example, if the user is sad, the editing department can suggest a melancholic editing style. For example, if the user is excited, the editing department can suggest an energetic editing style. This enables more appropriate editing by adjusting the editing expression style based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can 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-mentioned processing in the editing department can be performed using, for example, an AI, or can be performed without using an AI. For example, the editing department can input the user's emotion data into the generation AI and have the generation AI adjust the editing expression style.

[0083] During editing, the editing unit can adjust the level of detail of the editing based on the importance of the song. For example, in the case of an important song, the editing unit performs detailed editing. For example, in the case of a general song, the editing unit can also perform standard editing. For example, in the case of a simple song, the editing unit can also perform simple editing. In this way, by adjusting the level of detail of the editing based on the importance of the song, appropriate editing becomes possible. Some or all of the above-mentioned processing in the editing unit may be performed using, for example, AI, or may be performed without using AI. For example, the editing unit can input song importance data into the generation AI and have the generation AI adjust the level of detail of the editing.

[0084] During editing, the editing department can apply different editing algorithms depending on the category of the music. For example, in the case of a pop music piece, the editing department can apply an editing algorithm specialized for pop music. For example, in the case of a rock music piece, the editing department can also apply an editing algorithm specialized for rock music. For example, in the case of a classical music piece, the editing department can also apply an editing algorithm specialized for classical music. In this way, by applying different editing algorithms depending on the category of the music piece, appropriate editing becomes possible. Some or all of the above-mentioned processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input music category data into the generation AI and have the generation AI apply the editing algorithm.

[0085] During editing, the editing department can improve the accuracy of editing by referring to the user's past editing results. The editing department improves the accuracy of editing, for example, based on data of edits made by the user in the past. The editing department can also analyze the user's past editing results and optimize the editing algorithm, for example. The editing department can also improve the accuracy of editing by referring to the user's past editing history, for example. In this way, the accuracy of editing can be improved by referring to the user's past editing results. Some or all of the above-mentioned processing in the editing department may be performed using AI, for example, or may be performed without using AI. For example, the editing department can input the user's past editing data into the generation AI and have the generation AI improve the accuracy of editing.

[0086] The editing unit can estimate the user's emotions and adjust the length of the edits based on the estimated user emotions. For example, if the user is relaxed, the editing unit can suggest a longer edit. For example, if the user is in a hurry, the editing unit can also suggest a shorter edit. For example, if the user is concentrating, the editing unit can also suggest an edit of an appropriate length. This enables editing of an appropriate length by adjusting the length of the edits 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 can 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-mentioned processing in the editing unit can be performed using, for example, AI, or without AI. For example, the editing unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the edits.

[0087] During editing, the editing department can determine the priority of editing based on the submission date of the song. For example, the editing department prioritizes editing for songs with an approaching deadline. For example, the editing department can postpone editing for songs with a distant submission date. The editing department can also dynamically adjust the priority of editing based on the submission date, for example. This enables efficient editing by determining the priority of editing based on the submission date of the song. Some or all of the above-described processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input submission date data into the generation AI and have the generation AI determine the priority of editing.

[0088] During editing, the editing unit can adjust the editing order based on the relevance of the songs. For example, the editing unit prioritizes editing of highly relevant songs. For example, the editing unit can also postpone editing of less relevant songs. For example, the editing unit can dynamically adjust the editing order based on the relevance of the songs. This allows for efficient editing by adjusting the editing order based on the relevance of the songs. Some or all of the above-described processing in the editing unit may be performed using AI, for example, or may be performed without using AI. For example, the editing unit can input song relevance data into a generation AI and have the generation AI adjust the editing order.

[0089] During editing, the editing department can adjust the use of technical terms in the editing depending on the user's level of expertise. For example, if the user is a beginner, the editing department can edit using less technical terms. For example, if the user is an intermediate user, the editing department can also edit using appropriate technical terms. For example, if the user is an advanced user, the editing department can also edit using more technical terms. This enables appropriate editing by adjusting the use of technical terms in the editing depending on the user's level of expertise. Some or all of the above-mentioned processing in the editing department can be performed using AI, for example, or without AI. For example, the editing department can input the user's technical knowledge data into a generation AI and have the generation AI execute the use of technical terms.

[0090] The release unit can estimate the user's emotions and adjust the release method based on the estimated user emotions. For example, if the user is relaxed, the release unit can suggest a relaxed release method. For example, if the user is in a hurry, the release unit can suggest a quick release method. For example, if the user is excited, the release unit can suggest an energetic release method. This enables an appropriate release by adjusting the release method based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 release unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the release unit can input the user's emotion data into the generation AI and have the generation AI adjust the release method.

[0091] The release unit can adjust the level of detail of the release based on the importance of the song at the time of release. For example, for an important song, the release unit can provide detailed release procedures. For example, for a general song, the release unit can also provide standard release procedures. For example, for a simple song, the release unit can also provide simple release procedures. This enables appropriate release by adjusting the level of detail of the release based on the importance of the song. Some or all of the above-mentioned processing in the release unit may be performed using AI, for example, or may be performed without using AI. For example, the release unit can input song importance data into a generation AI and have the generation AI adjust the level of detail of the release.

[0092] The release unit can apply different release algorithms depending on the category of the song at the time of release. For example, in the case of a pop song, the release unit can apply a release algorithm specialized for pop. For example, in the case of a rock song, the release unit can also apply a release algorithm specialized for rock. For example, in the case of a classical song, the release unit can also apply a release algorithm specialized for classical. In this way, by applying different release algorithms depending on the category of the song, appropriate release is possible. Some or all of the above-mentioned processing in the release unit may be performed using, for example, AI, or may be performed without using AI. For example, the release unit can input song category data into the generation AI and have the generation AI apply the release algorithm.

[0093] At the time of release, the release unit can improve the accuracy of the release by referring to the user's past release results. The release unit can improve the accuracy of the release, for example, based on data of releases made by the user in the past. The release unit can also analyze the user's past release results and optimize the release algorithm, for example. The release unit can also improve the accuracy of the release by referring to the user's past release history, for example. In this way, the accuracy of the release can be improved by referring to the user's past release results. Some or all of the above-described processing in the release unit can be performed, for example, using AI, or can be performed without using AI. For example, the release unit can input the user's past release data into the generation AI and have the generation AI improve the accuracy of the release.

[0094] The release unit can estimate the user's emotions and adjust the release length based on the estimated user emotions. For example, if the user is relaxed, the release unit can suggest a longer release procedure. For example, if the user is in a hurry, the release unit can also suggest a shorter release procedure. For example, if the user is concentrating, the release unit can also suggest a release procedure of an appropriate length. This enables an appropriate release length by adjusting the release length 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 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 release unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the release unit can input the user's emotion data into the generation AI and have the generation AI adjust the release length.

[0095] At the time of release, the release department can determine the release priority based on the submission date of the song. For example, the release department can prioritize the release of songs with an approaching deadline. For example, the release department can postpone the release of songs with a more distant submission date. The release department can also dynamically adjust the release priority based on the submission date, for example. This enables efficient release by determining the release priority based on the submission date of the song. Some or all of the above-described processing in the release department can be performed using, or without, AI. For example, the release department can input submission date data into a generation AI and have the generation AI determine the release priority.

[0096] The release unit can adjust the release order based on the relevance of the songs at the time of release. For example, the release unit prioritizes the release of highly relevant songs. For example, the release unit can also release less relevant songs later. For example, the release unit can dynamically adjust the release order based on the relevance of the songs. This allows for efficient release by adjusting the release order based on the relevance of the songs. Some or all of the above-described processing in the release unit may be performed using, or without, AI. For example, the release unit can input song relevance data into a generation AI and have the generation AI adjust the release order.

[0097] At the time of release, the release unit can adjust the use of technical terminology in the release according to the user's level of expertise. For example, if the user is a beginner, the release unit can release the release using less technical terminology. For example, if the user is an intermediate user, the release unit can also release the release using appropriate technical terminology. For example, if the user is an advanced user, the release unit can release the release using more technical terminology. This enables an appropriate release by adjusting the use of technical terminology in the release according to the user's level of expertise. Some or all of the above-described processing in the release unit can be performed using AI, for example, or without AI. For example, the release unit can input the user's technical knowledge data into a generation AI and have the generation AI execute the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, generation unit, editing unit, and release unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the collection unit provides an interface through which a user inputs information about the genre and atmosphere of a song and the instruments they wish to use. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates a song based on the information provided by the collection unit. The editing unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and provides a function for the user to edit and adjust the generated song. The release unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides a function for uploading the completed song to an online music distribution service. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, generation unit, editing unit, and release unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the collection unit provides an interface through which a user inputs information about the genre and atmosphere of a piece of music and the instruments they wish to use. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates music based on the information provided by the collection unit. The editing unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides a function for the user to edit and adjust the generated music. The release unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a function for uploading the completed music to an online music distribution service. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, generation unit, editing unit, and release unit, described above, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the collection unit provides an interface through which a user inputs information about the genre and atmosphere of a song and the instruments they wish to use. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates a song based on the information provided by the collection unit. The editing unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12 and provides a function for the user to edit and adjust the generated song. The release unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides a function for uploading the completed song to an online music distribution service. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, generation unit, editing unit, and release unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the collection unit provides an interface through which a user inputs information about the genre and atmosphere of a piece of music and the instruments they wish to use. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates music based on the information provided by the collection unit. The editing unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and provides a function for the user to edit and adjust the generated music. The release unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a function for uploading the completed music to an online music distribution service.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The collection unit can learn the user's musical preferences and automatically extract the characteristics of songs that the user likes. For example, the collection unit can analyze the tempo, key, and instrument combination of songs that the user has previously rated highly, and suggest new songs based on these characteristics. Also, if the user likes a particular artist or band, the collection unit can learn the characteristics of the artist or band's songs and suggest similar songs. Furthermore, the collection unit can learn the musical preferences of the user in specific time periods or situations, and suggest songs that suit those time periods and situations. This enables more personalized song suggestions based on the user's musical preferences.

[0100] The collection unit can estimate the user's emotions and suggest the tempo and key of a song based on the estimated user's emotions. For example, if the user is relaxed, the collection unit can suggest a song with a slow tempo. If the user is sad, the collection unit can suggest a song with a melancholic key. If the user is excited, the collection unit can suggest a song with an energetic tempo. This allows for the generation of more appropriate songs by suggesting the tempo and key of a song based on the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generation AI. The collection unit can input the user's facial expression data and voice data into the generation AI and cause the generation AI to estimate emotions.

[0101] The collection unit can analyze the user's music streaming service history and suggest appropriate songs and artists. For example, the collection unit can suggest similar songs and artists based on the user's history of songs played in the past. Furthermore, if the user creates a specific playlist, the collection unit can analyze the characteristics of the songs in the playlist and suggest songs suitable for adding to the playlist. Furthermore, if the user frequently plays a specific genre or artist, the collection unit can suggest new songs related to that genre or artist. This makes it possible to make more appropriate song suggestions by utilizing the user's music streaming service history.

[0102] The collection unit can suggest music effects and arrangements based on the user's current activity status. For example, if the user is exercising, the collection unit can suggest energetic effects and arrangements. If the user is relaxing, the collection unit can suggest calming effects and arrangements. Furthermore, if the user is concentrating, the collection unit can suggest effects and arrangements that enhance concentration. In this way, more appropriate music can be generated by suggesting music effects and arrangements based on the user's current activity status. The collection unit can input the user's activity data into the generation AI and have the generation AI execute the suggested effects and arrangements.

[0103] The collection unit can automatically classify the elements of a song according to the user's input method. For example, when the user inputs the genre and atmosphere of a song by voice, the collection unit can collect information using voice recognition technology and classify the elements of the song. When the user inputs the genre and atmosphere of a song by text, the collection unit can collect information using text analysis technology and classify the elements of the song. Furthermore, when the user inputs the atmosphere of a song by image, the collection unit can collect information using image analysis technology and classify the elements of the song. In this way, by automatically classifying the elements of a song according to the user's input method, information can be efficiently collected and used to create a song.

[0104] The collection unit can estimate the user's emotions and prioritize music components based on the estimated user emotions. For example, if the user is relaxed, the collection unit can prioritize collecting melodies and rhythms of relaxing music. If the user is sad, the collection unit can prioritize collecting melodies and rhythms of melancholic music. If the user is excited, the collection unit can prioritize collecting melodies and rhythms of energetic music. This allows for the generation of more appropriate music by prioritizing music components based on the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generation AI. The collection unit can input the user's facial expression data and voice data into the generation AI and cause the generation AI to estimate emotions.

[0105] The collection unit can suggest regionally specific musical styles and instruments based on the user's geographical location information. For example, if the user is in a specific region, the collection unit can suggest traditional musical styles and instruments from that region. Also, if the user is traveling, the collection unit can suggest musical styles and instruments from the region where the user is traveling. Furthermore, if the user is participating in a specific event, the collection unit can suggest musical styles and instruments related to that event. This makes it possible to generate a wider variety of songs by suggesting regionally specific musical styles and instruments based on the user's geographical location information. The collection unit can input the user's location data into the generation AI and cause the generation AI to suggest regionally specific musical styles and instruments.

[0106] The collection unit can analyze the user's social media activity and suggest songs based on song information shared by the user's friends and followers. For example, the collection unit can collect song information shared by the user's friends and followers on social media and suggest similar songs. It can also analyze the content of the user's social media posts and suggest related songs. Furthermore, if the user follows a specific artist or band, it can suggest new songs related to that artist or band. This enables more personalized song suggestions by analyzing the user's social media activity. The collection unit can input social media data into the generation AI and have the generation AI perform song suggestions.

[0107] The collection unit can dynamically adjust the song suggestion method based on the user's past feedback. For example, the collection unit can analyze the characteristics of songs that the user has previously rated highly and prioritize suggesting similar songs. The collection unit can also analyze the characteristics of songs that the user has previously rated poorly and suggest songs that exclude such songs. Furthermore, the collection unit can customize the song suggestion method based on the user's feedback and suggest songs that match the user's preferences. This enables more appropriate song suggestions by reflecting the user's past feedback. The collection unit can input the user's feedback data into the generation AI and cause the generation AI to adjust the song suggestion method.

[0108] The generation unit can estimate the user's emotions and adjust the music arrangement and effects based on the estimated user emotions. For example, if the user is relaxed, the generation unit can apply a gentle arrangement and effects. If the user is sad, the generation unit can apply a melancholic arrangement and effects. If the user is excited, the generation unit can apply an energetic arrangement and effects. This allows for the generation of more appropriate music by adjusting the music arrangement and effects based on the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generation AI. The generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the arrangement and effects.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The collection unit receives input from the user about the genre and atmosphere of the music, as well as the instruments they wish to use. The information input by the user includes genres such as pop, rock, and classical, as well as moods such as cheerful, sad, and relaxed. It is also possible to select instruments such as piano, guitar, and drums. The collection unit analyzes the information input by the user and provides it to the generation AI. Step 2: The generation unit uses the generation AI to generate the melody, rhythm, and harmony of the song based on the information collected by the collection unit. The generation AI generates a song that meets the user's specifications based on classic songs and music theory. For example, if a song with a bright atmosphere in the pop genre is specified, the generation AI will generate a melody and rhythm that meets those conditions. Step 3: The editor edits and adjusts the music generated by the generator. The editor can change parts of the melody or adjust the rhythm. They can also add effects to change the atmosphere of the music. This allows users to customize the generated music to their own tastes. Step 4: The Releasing Department releases the song that has been edited and adjusted by the Editing Department. The Releasing Department then uploads the completed song to an online music distribution service, allowing it to be listened to by people all over the world. This allows users to easily distribute their own songs and create masterpieces.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 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.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The 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.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] [Explanation of symbols]

[0183] 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 collection unit where the user inputs information about the genre, atmosphere, and instruments they want to use in the music; a generating unit that generates a melody, rhythm, and harmony of a piece of music based on the information collected by the collecting unit; an editing unit that edits and adjusts the music generated by the generating unit; a release unit that releases the music edited and adjusted by the editing unit. A system characterized by:

2. The collecting unit Estimate the user's emotions and suggest music genres and moods based on the estimated user emotions 2. The system of claim 1.

3. The collecting unit Analyzes the user's past music production history and suggests suitable instruments or genres 2. The system of claim 1.

4. The collecting unit When collecting songs, filtering is performed based on the user's current mood and activity status.

2. The system of claim 1.

5. The collecting unit When collecting songs, select the appropriate collection method according to the user's input method.

2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and determine the priority of music information to be collected based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit When collecting songs, the system prioritizes collection of relevant song information based on the user's geographical location information.

2. The system of claim 1.

8. The collecting unit When collecting songs, analyze users' social media activities and collect related song information 2. The system of claim 1.

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