System

Abstract

An object of a system according to an embodiment is to automatically generate original music according to a user's preference.SOLUTION: A system includes a reception unit, a generation unit, and a provision unit. The reception unit receives an input of a user's favorite music genre or theme. The generation unit analyzes the information received by the reception unit and generates original music. The providing section provides the musical piece generated by the generating section.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] Conventional technology has had the problem of making it difficult to automatically generate original music that matches a user's preferences.

[0005] The system according to the embodiment aims to automatically generate original music that matches the preferences of a user. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives input of a user's preferred music genre or theme. The generating unit analyzes the information received by the receiving unit and generates an original song. The providing unit provides the song generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate original music that matches the preferences of the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A music generation system according to an embodiment of the present invention generates original music based on a user's preferred music genre or theme. The music generation system allows a user to input their preferred music genre or theme, and a generation AI analyzes the input and generates an original music that matches the user's preferences. The generated music is customized based on the user's preferences, allowing the user to enjoy their own original music. For example, the user inputs a genre such as rock, pop, jazz, or classical, or a theme such as love, adventure, or sadness. This information is input into the generation AI. The generation AI then analyzes the input information and generates a melody, rhythm, harmony, and other elements of the music based on the user's preferred music genre or theme. For example, if the user selects rock, a song featuring electric guitar riffs and drum beats is generated. The generated music is customized based on the user's preferences. For example, if the user selects love as a theme, a song containing an emotional melody and lyrics is generated. This allows the user to enjoy their own original music. This allows the music generation system to easily generate original music that matches the user's preferences. This allows the music generation system to generate music tailored to specific events or moods. For example, the user can enjoy music even more.

[0029] A music generation system according to an embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives input of a user's preferred music genre or theme. Examples of the user's preferred music genre or theme include, but are not limited to, rock, pop, classical, and jazz. The receiving unit, for example, receives the user's input of the genre or theme as text data. The receiving unit can also receive the user's preferred music genre or theme using voice input. The generating unit uses a generation AI to analyze the information received by the receiving unit and generate an original song. The generation AI generates a melody, rhythm, and harmony using techniques such as a neural network or a genetic algorithm. The generating unit generates a song using a specific instrument or tone based on, for example, a music genre selected by the user. The generating unit can also generate a song including an emotional melody and lyrics based on a theme selected by the user. The providing unit provides the song generated by the generating unit to the user. The providing unit provides the generated song, for example, in a streaming format. The providing unit can also provide the generated song in a download format. Furthermore, the providing unit can provide the generated music on a physical medium such as a CD, etc. This allows the music generation system according to the embodiment to generate and provide original music based on the user's preferences.

[0030] The generation unit can generate melodies, rhythms, and harmonies using a generation AI. The generation AI generates melodies using, for example, a neural network. The generation AI can also generate rhythms using, for example, a genetic algorithm. The generation AI can also generate harmonies using, for example, reinforcement learning. This makes it possible to generate melodies, rhythms, and harmonies using the generation AI. Some or all of the above-mentioned processes in the generation unit are performed using the generation AI. For example, the generation unit inputs a genre or theme entered by the user into the generation AI, and the generation AI generates the melody, rhythm, and harmony.

[0031] The generation unit can generate a song based on the user's preferred music genre or theme. For example, if the user selects rock, the generation unit generates a song characterized by electric guitar riffs and drum beats. For example, if the user selects pop, the generation unit can also generate a song characterized by a catchy melody and simple rhythm. For example, if the user selects classical, the generation unit can also generate a grand song using orchestral instruments. This makes it possible to generate a song based on the user's preferences. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the genre or theme entered by the user into the generation AI, and the generation AI generates a song based on the user's preferences.

[0032] The providing unit can provide the generated song to the user. For example, the providing unit can provide the generated song in a streaming format. For example, the providing unit can also provide the generated song in a download format. For example, the providing unit can also provide the generated song on a physical medium such as a CD. In this way, the generated song can be provided to the user. Some or all of the above-described processing in the providing unit is performed using AI. For example, the providing unit can use an AI model to stream the generated song to the user's device.

[0033] The music generation system further includes a storage unit that stores the generated song. The storage unit stores the generated song in cloud storage, for example. The storage unit can also store the generated song on a local disk, for example. The storage unit can also store the generated song in a specific format (e.g., MP3, WAV, etc.), for example. This allows the generated song to be stored. Some or all of the above-mentioned processing in the storage unit is performed using AI. For example, the storage unit can use an AI model to store the generated song in cloud storage.

[0034] The music generation system further includes a sharing unit that shares the generated song. The sharing unit, for example, shares the generated song on social media. The sharing unit can also share the generated song by email, for example. The sharing unit can also generate a link to the generated song and share it with other users, for example. This allows the generated song to be shared. Some or all of the above-described processing in the sharing unit is performed using AI. For example, the sharing unit can use an AI model for sharing the generated song on social media.

[0035] The music generation system further includes a feedback unit that accepts user feedback. The feedback unit accepts user feedback in the form of a questionnaire, for example. The feedback unit can also accept user feedback using an evaluation system, for example. The feedback unit can also accept user feedback in the form of comments, for example. This allows for the acceptance of user feedback. Some or all of the above-described processing in the feedback unit is performed using AI. For example, the feedback unit can use an AI model to analyze user feedback and reflect it in improving the system.

[0036] Furthermore, the music generation system includes a reception unit that analyzes the user's past music preference history and presents appropriate genre and theme candidates. The reception unit, for example, prioritizes presenting genres that the user has frequently listened to in the past. The reception unit can also analyze the genres and themes of songs that the user has previously given high ratings to and present them as candidates. The reception unit can also suggest genres and themes that are preferred during specific time periods, for example, based on the user's past playback history. This makes it possible to suggest optimal genres and themes based on the user's past preference history. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's playback history data into the generation AI, which then presents appropriate genres and themes.

[0037] Furthermore, the music generation system includes a reception unit that filters input of music genres and themes based on the user's current activity and environment. For example, if the user is exercising, the reception unit can suggest energetic music genres. For example, if the user is working, the reception unit can also suggest music genres that improve concentration. For example, if the user is relaxing, the reception unit can also suggest relaxing music genres. This allows appropriate genres and themes to be suggested based on the user's current activity and environment. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's activity data into the generation AI, which then suggests appropriate genres and themes.

[0038] Furthermore, the music generation system includes a reception unit that selects an appropriate input means according to the user's input method when inputting a music genre or theme. For example, if the user selects voice input, the reception unit uses voice recognition technology to input the genre or theme. For example, if the user selects text input, the reception unit can also select the genre or theme by entering keywords. For example, if the user selects image input, the reception unit can also suggest related genres and themes using image analysis technology. This makes it possible to provide the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's input data to the generation AI, which then selects the appropriate input means.

[0039] Furthermore, the music generation system includes a reception unit that, when a music genre or theme is input, prioritizes presenting highly relevant genres and themes based on the user's geographical location information. For example, if the user is in a specific area, the reception unit suggests genres and themes that are popular in that area. For example, if the user is traveling, the reception unit can also suggest genres and themes related to the culture of the user's travel destination. For example, if the user is participating in a specific event, the reception unit can also suggest genres and themes related to the event. This makes it possible to suggest highly relevant genres and themes based on the user's geographical location information. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's GPS data into the generation AI, which then suggests highly relevant genres and themes.

[0040] Furthermore, the music generation system includes a reception unit that analyzes the user's social media activity when a music genre or theme is input and suggests related genres and themes. The reception unit, for example, analyzes music shared by the user on social media and suggests related genres and themes. The reception unit can also analyze music shared by the user's friends and suggest related genres and themes. The reception unit can also suggest genres and themes of artists the user follows on social media, for example. This makes it possible to suggest related genres and themes based on the user's social media activity. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's social media data into the generation AI, which then suggests related genres and themes.

[0041] Furthermore, the music generation system includes a reception unit that customizes the input method by reflecting the user's past feedback when inputting a music genre or theme. The reception unit, for example, preferentially suggests input methods that the user has previously rated highly. The reception unit can also analyze input methods that the user has previously used and suggest the optimal input method. The reception unit can also customize the input interface, for example, based on the user's past feedback. This allows the input method to be customized based on the user's past feedback. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs the user's feedback data to the generation AI, which then suggests the optimal input method.

[0042] Furthermore, the music generation system includes a generation unit that adjusts the melody, rhythm, and harmony based on the user's preferred instruments and timbres when generating a song. For example, if the user prefers guitar, the generation unit generates a song that emphasizes guitar riffs. For example, if the user prefers piano, the generation unit can also generate a song that focuses on piano melodies. For example, if the user prefers drums, the generation unit can also generate a song that emphasizes drum beats. This allows the generation of a song to be adjusted based on the user's preferences. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs data on the user's preferred instruments and timbres into the generation AI, which then adjusts the melody, rhythm, and harmony.

[0043] Furthermore, the music generation system includes a generation unit that optimizes the generation algorithm based on the user's past music generation history when generating a song. The generation unit, for example, analyzes the patterns of songs the user has previously generated and selects the optimal algorithm. The generation unit can also extract preferred melodies and rhythms from the user's past generation history and reflect them in the generation. The generation unit can also customize the generation algorithm based on the user's past generation history, for example. This allows the generation algorithm to be optimized based on the user's past generation history. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's past generation history data into the generation AI, which then optimizes the generation algorithm.

[0044] Furthermore, the music generation system includes a generation unit that adjusts the tempo and key of the song according to the user's current mood and situation when generating the song. For example, if the user is relaxed, the generation unit generates a song with a slow tempo. For example, if the user is excited, the generation unit can also generate a song with a fast tempo. For example, if the user is sad, the generation unit can also generate a song in a minor key. This allows the tempo and key of the song to be adjusted according to the user's current mood and situation. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's mood and situation data into the generation AI, which then adjusts the tempo and key of the song.

[0045] Furthermore, the music generation system includes a generation unit that, when generating songs, determines the priority of songs to be generated based on the user's submission time. For example, if the user is in a hurry, the generation unit prioritizes song generation. For example, if the user is generating songs for a specific event, the generation unit can also determine the priority based on the date and time of the event. For example, if the user requests multiple songs, the generation unit can also determine the priority based on the submission time. This makes it possible to determine the priority of song generation based on the user's submission time. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's submission time data into the generation AI, which then determines the priority of songs.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The music generation system may further include a generation unit that analyzes the user's past music generation history and optimizes the generation algorithm. The generation unit, for example, analyzes the patterns of songs the user has previously generated and selects the optimal algorithm. The generation unit may also extract the user's preferred melodies and rhythms from the user's past generation history and reflect them in the generation. The generation unit may also customize the generation algorithm based on the user's past generation history, for example. This allows the generation algorithm to be optimized based on the user's past generation history.

[0048] The music generation system may further include a reception unit that performs filtering based on the user's current activity or environment. For example, if the user is exercising, the reception unit may suggest an energetic music genre. For example, if the user is working, the reception unit may suggest a music genre that helps the user concentrate. For example, if the user is relaxing, the reception unit may suggest a relaxing music genre. This allows appropriate genres and themes to be suggested based on the user's current activity or environment.

[0049] The music generation system may further include a reception unit that preferentially presents highly relevant genres and themes based on the user's geographical location information. For example, if the user is in a specific area, the reception unit may suggest genres and themes that are popular in that area. For example, if the user is traveling, the reception unit may also suggest genres and themes related to the culture of the user's travel destination. For example, if the user is participating in a specific event, the reception unit may also suggest genres and themes related to the event. In this way, highly relevant genres and themes can be suggested based on the user's geographical location information.

[0050] The music generation system may further include a reception unit that analyzes the user's social media activity and suggests related genres and themes. The reception unit may, for example, analyze music shared by the user on social media and suggest related genres and themes. The reception unit may, for example, analyze music shared by the user's friends and suggest related genres and themes. The reception unit may, for example, suggest genres and themes based on artists the user follows on social media. This allows related genres and themes to be suggested based on the user's social media activity.

[0051] The music generation system may further include a reception unit that customizes the input method by reflecting the user's past feedback. The reception unit, for example, preferentially suggests input methods that the user has previously rated highly. The reception unit may also analyze input methods that the user has previously used and suggest the most suitable input method. The reception unit may also customize the input interface based on the user's past feedback, for example. This allows the input method to be customized based on the user's past feedback.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The reception unit receives input of the user's preferred music genre or theme. The user's preferred music genre or theme may include, but is not limited to, rock, pop, classical, jazz, etc. The reception unit receives the genre or theme input by the user as text data, for example. The reception unit can also receive the user's preferred music genre or theme using voice input. Step 2: The generation unit uses a generation AI to analyze the information received by the reception unit and generate an original song. The generation AI generates melody, rhythm, and harmony using technologies such as neural networks and genetic algorithms. The generation unit generates songs using specific instruments and tones based on, for example, a musical genre selected by the user. The generation unit can also generate songs with emotional melodies and lyrics based on a theme selected by the user. Step 3: The providing unit provides the song generated by the generating unit to the user. For example, the providing unit provides the generated song in a streaming format. The providing unit can also provide the generated song in a download format. Furthermore, the providing unit can also provide the generated song on a physical medium such as a CD.

[0054] (Example 2) A music generation system according to an embodiment of the present invention generates original music based on a user's preferred music genre or theme. The music generation system allows a user to input their preferred music genre or theme, and a generation AI analyzes the input and generates an original music that matches the user's preferences. The generated music is customized based on the user's preferences, allowing the user to enjoy their own original music. For example, the user inputs a genre such as rock, pop, jazz, or classical, or a theme such as love, adventure, or sadness. This information is input into the generation AI. The generation AI then analyzes the input information and generates a melody, rhythm, harmony, and other elements of the music based on the user's preferred music genre or theme. For example, if the user selects rock, a song featuring electric guitar riffs and drum beats is generated. The generated music is customized based on the user's preferences. For example, if the user selects love as a theme, a song containing an emotional melody and lyrics is generated. This allows the user to enjoy their own original music. This allows the music generation system to easily generate original music that matches the user's preferences. This allows the music generation system to generate music tailored to specific events or moods. For example, the user can enjoy music even more.

[0055] A music generation system according to an embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives input of a user's preferred music genre or theme. Examples of the user's preferred music genre or theme include, but are not limited to, rock, pop, classical, and jazz. The receiving unit, for example, receives the user's input of the genre or theme as text data. The receiving unit can also receive the user's preferred music genre or theme using voice input. The generating unit uses a generation AI to analyze the information received by the receiving unit and generate an original song. The generation AI generates a melody, rhythm, and harmony using techniques such as a neural network or a genetic algorithm. The generating unit generates a song using a specific instrument or tone based on, for example, a music genre selected by the user. The generating unit can also generate a song including an emotional melody and lyrics based on a theme selected by the user. The providing unit provides the song generated by the generating unit to the user. The providing unit provides the generated song, for example, in a streaming format. The providing unit can also provide the generated song in a download format. Furthermore, the providing unit can provide the generated music on a physical medium such as a CD, etc. This allows the music generation system according to the embodiment to generate and provide original music based on the user's preferences.

[0056] The generation unit can generate melodies, rhythms, and harmonies using a generation AI. The generation AI generates melodies using, for example, a neural network. The generation AI can also generate rhythms using, for example, a genetic algorithm. The generation AI can also generate harmonies using, for example, reinforcement learning. This makes it possible to generate melodies, rhythms, and harmonies using the generation AI. Some or all of the above-mentioned processes in the generation unit are performed using the generation AI. For example, the generation unit inputs a genre or theme entered by the user into the generation AI, and the generation AI generates the melody, rhythm, and harmony.

[0057] The generation unit can generate a song based on the user's preferred music genre or theme. For example, if the user selects rock, the generation unit generates a song characterized by electric guitar riffs and drum beats. For example, if the user selects pop, the generation unit can also generate a song characterized by a catchy melody and simple rhythm. For example, if the user selects classical, the generation unit can also generate a grand song using orchestral instruments. This makes it possible to generate a song based on the user's preferences. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the genre or theme entered by the user into the generation AI, and the generation AI generates a song based on the user's preferences.

[0058] The providing unit can provide the generated song to the user. For example, the providing unit can provide the generated song in a streaming format. For example, the providing unit can also provide the generated song in a download format. For example, the providing unit can also provide the generated song on a physical medium such as a CD. In this way, the generated song can be provided to the user. Some or all of the above-described processing in the providing unit is performed using AI. For example, the providing unit can use an AI model to stream the generated song to the user's device.

[0059] The music generation system further includes a storage unit that stores the generated song. The storage unit stores the generated song in cloud storage, for example. The storage unit can also store the generated song on a local disk, for example. The storage unit can also store the generated song in a specific format (e.g., MP3, WAV, etc.), for example. This allows the generated song to be stored. Some or all of the above-mentioned processing in the storage unit is performed using AI. For example, the storage unit can use an AI model to store the generated song in cloud storage.

[0060] The music generation system further includes a sharing unit that shares the generated song. The sharing unit, for example, shares the generated song on social media. The sharing unit can also share the generated song by email, for example. The sharing unit can also generate a link to the generated song and share it with other users, for example. This allows the generated song to be shared. Some or all of the above-described processing in the sharing unit is performed using AI. For example, the sharing unit can use an AI model for sharing the generated song on social media.

[0061] The music generation system further includes a feedback unit that accepts user feedback. The feedback unit accepts user feedback in the form of a questionnaire, for example. The feedback unit can also accept user feedback using an evaluation system, for example. The feedback unit can also accept user feedback in the form of comments, for example. This allows for the acceptance of user feedback. Some or all of the above-described processing in the feedback unit is performed using AI. For example, the feedback unit can use an AI model to analyze user feedback and reflect it in improving the system.

[0062] The music generation system further includes a reception unit that estimates the user's emotions and guides the user in inputting a music genre or theme based on the estimated user emotions. For example, if the user is sad, the reception unit suggests a soothing music genre or theme. For example, if the user is excited, the reception unit can suggest an energetic music genre or theme. For example, if the user is relaxed, the reception unit can suggest a relaxing music genre or theme. This allows appropriate music genres and themes to be suggested 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions.

[0063] Furthermore, the music generation system includes a reception unit that analyzes the user's past music preference history and presents appropriate genre and theme candidates. The reception unit, for example, prioritizes presenting genres that the user has frequently listened to in the past. The reception unit can also analyze the genres and themes of songs that the user has previously given high ratings to and present them as candidates. The reception unit can also suggest genres and themes that are preferred during specific time periods, for example, based on the user's past playback history. This makes it possible to suggest optimal genres and themes based on the user's past preference history. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's playback history data into the generation AI, which then presents appropriate genres and themes.

[0064] Furthermore, the music generation system includes a reception unit that filters input of music genres and themes based on the user's current activity and environment. For example, if the user is exercising, the reception unit can suggest energetic music genres. For example, if the user is working, the reception unit can also suggest music genres that improve concentration. For example, if the user is relaxing, the reception unit can also suggest relaxing music genres. This allows appropriate genres and themes to be suggested based on the user's current activity and environment. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's activity data into the generation AI, which then suggests appropriate genres and themes.

[0065] Furthermore, the music generation system includes a reception unit that selects an appropriate input means according to the user's input method when inputting a music genre or theme. For example, if the user selects voice input, the reception unit uses voice recognition technology to input the genre or theme. For example, if the user selects text input, the reception unit can also select the genre or theme by entering keywords. For example, if the user selects image input, the reception unit can also suggest related genres and themes using image analysis technology. This makes it possible to provide the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's input data to the generation AI, which then selects the appropriate input means.

[0066] The music generation system further includes a reception unit that estimates the user's emotions and prioritizes the input genres and themes based on the estimated user emotions. For example, if the user is sad, the reception unit may prioritize soothing genres and themes. For example, if the user is excited, the reception unit may prioritize energetic genres and themes. For example, if the user is relaxed, the reception unit may prioritize relaxing genres and themes. This allows the prioritization of genres and themes to be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions and determines the priority.

[0067] Furthermore, the music generation system includes a reception unit that, when a music genre or theme is input, prioritizes presenting highly relevant genres and themes based on the user's geographical location information. For example, if the user is in a specific area, the reception unit suggests genres and themes that are popular in that area. For example, if the user is traveling, the reception unit can also suggest genres and themes related to the culture of the user's travel destination. For example, if the user is participating in a specific event, the reception unit can also suggest genres and themes related to the event. This makes it possible to suggest highly relevant genres and themes based on the user's geographical location information. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's GPS data into the generation AI, which then suggests highly relevant genres and themes.

[0068] Furthermore, the music generation system includes a reception unit that analyzes the user's social media activity when a music genre or theme is input and suggests related genres and themes. The reception unit, for example, analyzes music shared by the user on social media and suggests related genres and themes. The reception unit can also analyze music shared by the user's friends and suggest related genres and themes. The reception unit can also suggest genres and themes of artists the user follows on social media, for example. This makes it possible to suggest related genres and themes based on the user's social media activity. Some or all of the above-described processing in the reception unit is performed using AI. For example, the reception unit inputs the user's social media data into the generation AI, which then suggests related genres and themes.

[0069] Furthermore, the music generation system includes a reception unit that customizes the input method by reflecting the user's past feedback when inputting a music genre or theme. The reception unit, for example, preferentially suggests input methods that the user has previously rated highly. The reception unit can also analyze input methods that the user has previously used and suggest the optimal input method. The reception unit can also customize the input interface, for example, based on the user's past feedback. This allows the input method to be customized based on the user's past feedback. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit inputs the user's feedback data to the generation AI, which then suggests the optimal input method.

[0070] The music generation system further includes a generation unit that estimates the user's emotions and adjusts the expression style of the generated music based on the estimated user emotions. For example, if the user is sad, the generation unit generates a music with an emotional melody and lyrics. For example, if the user is excited, the generation unit can generate a music with an energetic rhythm and beat. For example, if the user is relaxed, the generation unit can generate a music with a calm melody and harmony. This allows the expression style of the music to be adjusted 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the generation unit is performed using AI. For example, the generation unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions and adjusts the expression style of the music.

[0071] Furthermore, the music generation system includes a generation unit that adjusts the melody, rhythm, and harmony based on the user's preferred instruments and timbres when generating a song. For example, if the user prefers guitar, the generation unit generates a song that emphasizes guitar riffs. For example, if the user prefers piano, the generation unit can also generate a song that focuses on piano melodies. For example, if the user prefers drums, the generation unit can also generate a song that emphasizes drum beats. This allows the generation of a song to be adjusted based on the user's preferences. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs data on the user's preferred instruments and timbres into the generation AI, which then adjusts the melody, rhythm, and harmony.

[0072] Furthermore, the music generation system includes a generation unit that optimizes the generation algorithm based on the user's past music generation history when generating a song. The generation unit, for example, analyzes the patterns of songs the user has previously generated and selects the optimal algorithm. The generation unit can also extract preferred melodies and rhythms from the user's past generation history and reflect them in the generation. The generation unit can also customize the generation algorithm based on the user's past generation history, for example. This allows the generation algorithm to be optimized based on the user's past generation history. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's past generation history data into the generation AI, which then optimizes the generation algorithm.

[0073] Furthermore, the music generation system includes a generation unit that adjusts the tempo and key of the song according to the user's current mood and situation when generating the song. For example, if the user is relaxed, the generation unit generates a song with a slow tempo. For example, if the user is excited, the generation unit can also generate a song with a fast tempo. For example, if the user is sad, the generation unit can also generate a song in a minor key. This allows the tempo and key of the song to be adjusted according to the user's current mood and situation. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's mood and situation data into the generation AI, which then adjusts the tempo and key of the song.

[0074] The music generation system further includes a generation unit that estimates the user's emotion and adjusts the length of the generated song based on the estimated user emotion. For example, if the user is relaxed, the generation unit generates a longer song. For example, if the user is in a hurry, the generation unit can also generate a shorter song. For example, if the user is excited, the generation unit can also generate a song of an appropriate length. This allows the length of the song to be adjusted based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotion and adjusts the length of the song.

[0075] Furthermore, the music generation system includes a generation unit that, when generating songs, determines the priority of songs to be generated based on the user's submission time. For example, if the user is in a hurry, the generation unit prioritizes song generation. For example, if the user is generating songs for a specific event, the generation unit can also determine the priority based on the date and time of the event. For example, if the user requests multiple songs, the generation unit can also determine the priority based on the submission time. This makes it possible to determine the priority of song generation based on the user's submission time. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's submission time data into the generation AI, which then determines the priority of songs. === Hard Collateral 1-1 === Each of the above-mentioned elements, including the reception unit, generation unit, provision unit, storage unit, sharing unit, feedback unit, emotion estimation function, analysis of past music preference history, filtering based on current activity and environment, input method selection, suggestions based on geographical location information, analysis of social media activity, customization of input method reflecting past feedback, adjustment of the expression method of the generated song, adjustment based on the user's preferred instruments and timbre, optimization of the generation algorithm, adjustment of the song's tempo and key, adjustment of the song's length, and determination of song priority, may be implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit may input the user's preferred music genre or theme using the reception device 38 of the smart device 14. The generation unit may be implemented by the specific processing unit 290 of the data processing device 12 and generate original songs using a generation AI. The provision unit may provide the generated songs to the user using the output device 40 of the smart device 14. The storage unit may store the generated songs in the storage 32 of the data processing device 12. The sharing unit can share the generated song on social media using the communication I / F 44 of the smart device 14. The feedback unit can accept user feedback using the reception device 38 of the smart device 14. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12 and estimates the user's emotion. The analysis of past music preference history is realized by the specific processing unit 290 of the data processing device 12. Filtering based on current activity and environment is performed using sensors of the smart device 14. Selection of input method is performed by the reception device 38 of the smart device 14. Suggestion based on geographical location information is performed using the GPS function of the smart device 14. Analysis of social media activity is realized by the specific processing unit 290 of the data processing device 12. Customization of input method reflecting past feedback is realized by the specific processing unit 290 of the data processing device 12. Adjustment of the expression method of the generated song is realized by the specific processing unit 290 of the data processing device 12. The adjustments based on the user's preferred instruments and timbres are realized by the specific processing unit 290 of the data processing device 12.The optimization of the generation algorithm is realized by the specific processing unit 290 of the data processing device 12. The adjustment of the tempo and key of the song is realized by the specific processing unit 290 of the data processing device 12. The adjustment of the length of the song is realized by the specific processing unit 290 of the data processing device 12. The determination of the priority of the songs is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the above-mentioned elements, including the reception unit, generation unit, provision unit, storage unit, sharing unit, feedback unit, emotion estimation function, analysis of past music preference history, filtering based on current activity and environment, selection of input method, suggestions based on geographical location information, analysis of social media activity, customization of input method reflecting past feedback, adjustment of the expression method of the generated song, adjustment based on the user's preferred instruments and timbre, optimization of the generation algorithm, adjustment of song tempo and key, adjustment of song length, and determination of song priority, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can input the user's preferred music genre or theme using the microphone 238 of the smart glasses 214. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates original songs using a generation AI. The provision unit can provide the generated songs to the user using the speaker 240 of the smart glasses 214. The storage unit can store the generated songs in the storage 32 of the data processing device 12. The sharing unit can share the generated song on social media using the communication I / F 44 of the smart glasses 214. The feedback unit can receive user feedback using the microphone 238 of the smart glasses 214. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12 and estimates the user's emotion. The analysis of past music preference history is realized by the specific processing unit 290 of the data processing device 12. Filtering based on current activity and environment is performed using sensors of the smart glasses 214. Selection of input method is performed using the microphone 238 of the smart glasses 214. Suggestion based on geographical location information is performed using the GPS function of the smart glasses 214. Analysis of social media activity is realized by the specific processing unit 290 of the data processing device 12. Customization of input method reflecting past feedback is realized by the specific processing unit 290 of the data processing device 12. Adjustment of the presentation method of the generated song is realized by the specific processing unit 290 of the data processing device 12. The adjustments based on the user's preferred instruments and timbres are realized by the specific processing unit 290 of the data processing device 12.The optimization of the generation algorithm is realized by the specific processing unit 290 of the data processing device 12. The adjustment of the tempo and key of the song is realized by the specific processing unit 290 of the data processing device 12. The adjustment of the length of the song is realized by the specific processing unit 290 of the data processing device 12. The determination of the priority of the songs is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the above-mentioned elements, including the reception unit, generation unit, provision unit, storage unit, sharing unit, feedback unit, emotion estimation function, analysis of past music preference history, filtering based on current activity and environment, selection of input method, suggestions based on geographical location information, analysis of social media activity, customization of input method reflecting past feedback, adjustment of the expression method of the generated song, adjustment based on the user's preferred instruments and timbre, optimization of the generation algorithm, adjustment of song tempo and key, adjustment of song length, and determination of song priority, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can input the user's preferred music genre or theme using the microphone 238 of the headset-type terminal 314. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates original songs using a generation AI. The provision unit can provide the generated songs to the user using the speaker 240 of the headset-type terminal 314. The storage unit can store the generated songs in the storage 32 of the data processing device 12. The sharing unit can share the generated song on social media using the communication I / F 44 of the headset type terminal 314. The feedback unit can receive user feedback using the microphone 238 of the headset type terminal 314. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12 and estimates the user's emotion. The analysis of past music preference history is realized by the specific processing unit 290 of the data processing device 12. Filtering based on current activity and environment is performed using sensors of the headset type terminal 314. Selection of input method is performed using the microphone 238 of the headset type terminal 314. Suggestions based on geographical location information are performed using the GPS function of the headset type terminal 314. Analysis of social media activity is realized by the specific processing unit 290 of the data processing device 12. Customization of input method reflecting past feedback is realized by the specific processing unit 290 of the data processing device 12. Adjustment of the presentation method of the generated song is realized by the specific processing unit 290 of the data processing device 12.The adjustment based on the user's preferred instruments and timbres is realized by the specific processing unit 290 of the data processing device 12. The optimization of the generation algorithm is realized by the specific processing unit 290 of the data processing device 12. The adjustment of the tempo and key of the song is realized by the specific processing unit 290 of the data processing device 12. The adjustment of the song length is realized by the specific processing unit 290 of the data processing device 12. The determination of the priority of the songs is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the above-described elements, including the reception unit, generation unit, provision unit, storage unit, sharing unit, feedback unit, emotion estimation function, analysis of past music preference history, filtering based on current activity and environment, selection of input method, suggestions based on geographical location information, analysis of social media activity, customization of input method reflecting past feedback, adjustment of the expression method of the generated song, adjustment based on the user's preferred instruments and timbre, optimization of the generation algorithm, adjustment of the song's tempo and key, adjustment of the song's length, and determination of song priority, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input the user's preferred music genre or theme using the microphone 238 of the robot 414. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates original songs using a generation AI. The provision unit can provide the generated songs to the user using the speaker 240 of the robot 414. The storage unit can store the generated songs in the storage 32 of the data processing device 12. The sharing unit can share the generated song on social media using the communication I / F 44 of the robot 414. The feedback unit can receive user feedback using the microphone 238 of the robot 414. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12 and estimates the user's emotion. The analysis of past music preference history is realized by the specific processing unit 290 of the data processing device 12. Filtering based on current activity and environment is performed using the sensors of the robot 414. Selection of input method is performed using the microphone 238 of the robot 414. Suggestion based on geographical location information is performed using the GPS function of the robot 414. Analysis of social media activity is realized by the specific processing unit 290 of the data processing device 12. Customization of the input method reflecting past feedback is realized by the specific processing unit 290 of the data processing device 12. Adjustment of the expression method of the generated song is realized by the specific processing unit 290 of the data processing device 12. The adjustments based on the user's preferred instruments and timbres are realized by the specific processing unit 290 of the data processing device 12.The optimization of the generation algorithm is realized by the specific processing unit 290 of the data processing device 12. The adjustment of the tempo and key of the song is realized by the specific processing unit 290 of the data processing device 12. The adjustment of the length of the song is realized by the specific processing unit 290 of the data processing device 12. The determination of the priority of the songs is realized by the specific processing unit 290 of the data processing device 12.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The music generation system may further include a biometric information acquisition unit that acquires biometric information of the user. The biometric information acquisition unit measures, for example, the user's heart rate and electrodermal activity, and estimates the user's current state based on this data. For example, if the user's heart rate is high, the generation unit can generate an energetic song. Alternatively, if the user's electrodermal activity is low, the generation unit can generate a relaxing song. This allows the generation of optimal music based on the user's biometric information.

[0078] The music generation system may further include a generation unit that analyzes the user's past music generation history and optimizes the generation algorithm. The generation unit, for example, analyzes the patterns of songs the user has previously generated and selects the optimal algorithm. The generation unit may also extract the user's preferred melodies and rhythms from the user's past generation history and reflect them in the generation. The generation unit may also customize the generation algorithm based on the user's past generation history, for example. This allows the generation algorithm to be optimized based on the user's past generation history.

[0079] The music generation system may further include a generation unit that estimates the user's emotion and adjusts the expression of the generated song based on the estimated user's emotion. For example, if the user is sad, the generation unit generates a song with an emotional melody and lyrics. For example, if the user is excited, the generation unit may also generate a song with an energetic rhythm and beat. For example, if the user is relaxed, the generation unit may also generate a song with a calm melody and harmony. This allows the expression of the song to be adjusted based on the user's emotion.

[0080] The music generation system may further include a reception unit that performs filtering based on the user's current activity or environment. For example, if the user is exercising, the reception unit may suggest an energetic music genre. For example, if the user is working, the reception unit may suggest a music genre that helps the user concentrate. For example, if the user is relaxing, the reception unit may suggest a relaxing music genre. This allows appropriate genres and themes to be suggested based on the user's current activity or environment.

[0081] The music generation system may further include a generation unit that estimates the user's emotion and adjusts the length of the generated song based on the estimated user emotion. For example, the generation unit generates a longer song when the user is relaxed. For example, the generation unit can also generate a shorter song when the user is in a hurry. For example, the generation unit can also generate a song of an appropriate length when the user is excited. This allows the length of the song to be adjusted based on the user's emotion.

[0082] The music generation system may further include a reception unit that preferentially presents highly relevant genres and themes based on the user's geographical location information. For example, if the user is in a specific area, the reception unit may suggest genres and themes that are popular in that area. For example, if the user is traveling, the reception unit may also suggest genres and themes related to the culture of the user's travel destination. For example, if the user is participating in a specific event, the reception unit may also suggest genres and themes related to the event. In this way, highly relevant genres and themes can be suggested based on the user's geographical location information.

[0083] The music generation system may further include a reception unit that estimates the user's emotions and determines the priority of input genres and themes based on the estimated user emotions. For example, if the user is sad, the reception unit may preferentially display soothing genres and themes. For example, if the user is excited, the reception unit may preferentially display energetic genres and themes. For example, if the user is relaxed, the reception unit may preferentially display relaxing genres and themes. In this way, the priority of genres and themes can be determined based on the user's emotions.

[0084] The music generation system may further include a reception unit that analyzes the user's social media activity and suggests related genres and themes. The reception unit may, for example, analyze music shared by the user on social media and suggest related genres and themes. The reception unit may, for example, analyze music shared by the user's friends and suggest related genres and themes. The reception unit may, for example, suggest genres and themes based on artists the user follows on social media. This allows related genres and themes to be suggested based on the user's social media activity.

[0085] The music generation system may further include a generation unit that estimates the user's emotions and adjusts the tempo and key of the generated music based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a music piece with a slow tempo. For example, if the user is excited, the generation unit may also generate a music piece with a fast tempo. For example, if the user is sad, the generation unit may also generate a music piece in a minor key. This allows the tempo and key of the music to be adjusted according to the user's current mood and situation.

[0086] The music generation system may further include a reception unit that customizes the input method by reflecting the user's past feedback. The reception unit, for example, preferentially suggests input methods that the user has previously rated highly. The reception unit may also analyze input methods that the user has previously used and suggest the most suitable input method. The reception unit may also customize the input interface based on the user's past feedback, for example. This allows the input method to be customized based on the user's past feedback.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The reception unit receives input of the user's preferred music genre or theme. The user's preferred music genre or theme may include, but is not limited to, rock, pop, classical, jazz, etc. The reception unit receives the genre or theme input by the user as text data, for example. The reception unit can also receive the user's preferred music genre or theme using voice input. Step 2: The generation unit uses a generation AI to analyze the information received by the reception unit and generate an original song. The generation AI generates melody, rhythm, and harmony using technologies such as neural networks and genetic algorithms. The generation unit generates songs using specific instruments and tones based on, for example, a musical genre selected by the user. The generation unit can also generate songs with emotional melodies and lyrics based on a theme selected by the user. Step 3: The providing unit provides the song generated by the generating unit to the user. For example, the providing unit provides the generated song in a streaming format. The providing unit can also provide the generated song in a download format. Furthermore, the providing unit can also provide the generated song on a physical medium such as a CD.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

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

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

[0136] In the 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.

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

[0138] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0140] 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.

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

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of a user's preferred music genre or theme; a generation unit that analyzes the information received by the reception unit and generates an original song; a providing unit that provides the music generated by the generating unit. A system characterized by:

2. The generation unit Generate melodies, rhythms, and harmonies using generative AI 2. The system of claim 1.

3. The generation unit Generate songs based on a user's preferred music genre or theme 2. The system of claim 1.

4. The providing unit Providing the generated song to the user 2. The system of claim 1.

5. The providing unit Includes a storage unit for storing the generated songs 2. The system of claim 1.

6. The providing unit Includes a sharing section for sharing generated songs 2. The system of claim 1.

7. The providing unit A feedback unit is provided to receive user feedback.

2. The system of claim 1.

8. The reception unit Estimating a user's emotions and guiding input of a music genre or theme based on the estimated user's emotions 2. The system of claim 1.

Images