System

Abstract

An object of a system according to an embodiment is to automatically generate original lyrics and an original melody that match a theme and a mood specified by a user.SOLUTION: A system includes a theme designation unit, a lyrics generation unit, and a melody generation unit. The theme designation unit receives a theme or a mood designated by the user. The lyrics generation unit generates original lyrics based on the theme or mood designated by the theme designation unit. The melody generation unit generates an original melody in accordance with the lyrics generated by the lyrics generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to automatically generate original lyrics and melodies that match a theme or mood specified by a user.

[0005] The system according to the embodiment aims to automatically generate original lyrics and melodies that match a theme or mood specified by a user. [Means for solving the problem]

[0006] The system according to the embodiment includes a theme designation unit, a lyrics generation unit, and a melody generation unit. The theme designation unit accepts a theme or mood designated by a user. The lyrics generation unit generates original lyrics based on the theme or mood designated by the theme designation unit. The melody generation unit generates an original melody to match the lyrics generated by the lyrics generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate original lyrics and melodies that match a theme or mood specified by 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) The music generation system according to an embodiment of the present invention is a system in which a generation AI automatically generates original lyrics and melodies based on a theme and mood specified by a user. This allows the music generation system to provide original music based on the user's specifications without the involvement of a professional songwriter.

[0029] A music generation system according to an embodiment includes a theme specification unit, a lyrics generation unit, and a melody generation unit. The theme specification unit accepts a theme or mood specified by a user. For example, the user can specify a theme such as "happy summer memories at the beach." The user can also specify a mood such as "sadness over a broken heart." The lyrics generation unit generates original lyrics based on the theme or mood specified by the theme specification unit. For example, for the theme "happy summer memories at the beach," the generation AI generates lyrics such as "Those summer days spent with you on the sandy beach with the sound of waves echoing." For the mood "sadness over a broken heart," the generation AI generates lyrics such as "On nights when tears run down my cheeks, I remember you." The melody generation unit generates an original melody to match the lyrics generated by the lyrics generation unit. For example, for a mood of "bright and cheerful," the generation AI generates an up-tempo, rhythmic melody. For a mood of "sadness over a broken heart," the generation AI generates a slow, emotional melody. As a result, the music creation system according to the embodiment can automatically create original music based on a theme or mood specified by a user. For example, a user can quickly create music that matches a theme or mood for a specific event or personal project. Furthermore, because there is no need to involve professional songwriters, it is possible to provide high-quality music while keeping costs down.

[0030] The theme specification unit allows the generation AI to refer to a past music database and suggest songs with a similar theme or mood to the theme or mood specified by the user. For example, if the user specifies the theme "fun summer memories at the beach," the generation AI searches the past music database for songs with a similar theme and suggests them to the user. For example, it may suggest Beach Boys songs or surf rock songs. If the user specifies the mood "sadness over a broken heart," the generation AI searches the past music database for songs related to heartbreak and suggests them to the user. For example, it may suggest Adele's "Someone Like You" or Taylor Swift's "All Too Well." If the user specifies the mood "cheerful and cheerful," the generation AI searches the past music database for songs with a similar mood and suggests them to the user. For example, it may suggest Pharrell Williams' "Happy" or Justin Timberlake's "Can't Stop the Feeling!". This allows the generation AI to suggest similar songs from the past music database based on the user's specified theme or mood.

[0031] The theme specification unit allows the generation AI to automatically generate related visuals and videos based on the theme or mood input by the user, thereby providing inspiration for the song. For example, if the user specifies the theme "fun summer memories at the beach," the generation AI automatically generates videos of beaches and oceans and provides them to the user. This allows the user to gain visual inspiration. If the user specifies the mood "sadness from a broken heart," the generation AI automatically generates videos of rain and tears and provides them to the user. This allows the user to gain visual inspiration. If the user specifies the mood "bright and cheerful," the generation AI automatically generates videos of sunshine and smiles and provides them to the user. This allows the user to gain visual inspiration. This allows the generation AI to automatically generate related visuals and videos based on the theme or mood input by the user, thereby providing inspiration for the song.

[0032] The theme specification unit allows the user to input a theme or mood by voice, and the generation AI can analyze the theme or mood using voice recognition technology. For example, if the user inputs the theme "fun memories at the beach in summer" by voice, the generation AI analyzes the theme using voice recognition technology and uses it as a prompt for generating music. Also, if the user inputs the mood "sadness from a broken heart" by voice, the generation AI analyzes the mood using voice recognition technology and uses it as a prompt for generating music. Also, if the user inputs the mood "cheerful and cheerful" by voice, the generation AI analyzes the mood using voice recognition technology and uses it as a prompt for generating music. This allows the user to input a theme or mood by voice, and the generation AI can analyze the theme or mood using voice recognition technology.

[0033] When the user specifies a theme or mood, the generation AI provides real-time feedback to the theme specification unit, thereby assisting in the selection of the optimal theme or mood. For example, when the user inputs the theme "fun memories at the beach in summer," the generation AI provides real-time feedback and suggests adding specific episodes and emotions. When the user inputs the mood "sadness from a broken heart," the generation AI provides real-time feedback and suggests adding specific events and emotions. When the user inputs the mood "cheerful and cheerful," the generation AI provides real-time feedback and suggests adding specific situations and emotions. In this way, when the user specifies a theme or mood, the generation AI provides real-time feedback to assist in the selection of the optimal theme or mood.

[0034] The lyrics generation unit can generate personalized lyrics by referring to the user's past music preferences and playlists. For example, the lyrics generation unit references a playlist of music that the user has listened to in the past, and the generation AI generates lyrics based on those preferences. For example, lyrics that reflect the style of the user's favorite artists are created. The unit also analyzes the user's past music history, and the generation AI generates lyrics based on those tendencies. For example, lyrics that reflect the user's favorite themes and moods are created. The unit also analyzes the lyrics of music included in the user's playlist, and the generation AI generates lyrics that incorporate those characteristics. For example, lyrics that reflect specific phrases and expressions are created. In this way, the unit can generate personalized lyrics by referring to the user's past music preferences and playlists.

[0035] The lyrics generation unit can simultaneously generate lyrics in different languages, thereby providing multilingual music. For example, the lyrics generation unit uses a generation AI to simultaneously generate English and Japanese lyrics based on a theme or mood specified by the user. For example, English lyrics and their translations are provided. The generation AI can also simultaneously generate Spanish and French lyrics based on the user's specifications. For example, Spanish lyrics and their translations are provided. The generation AI can also simultaneously generate Chinese and Korean lyrics based on the user's specifications. For example, Chinese lyrics and their translations are provided. This allows lyrics in different languages ​​to be simultaneously generated, thereby providing multilingual music.

[0036] The lyrics generation unit can analyze photos and videos provided by the user and generate lyrics based on them. For example, the lyrics generation unit analyzes a summer beach photo provided by the user, and the generation AI generates lyrics based on that visual. For example, it creates lyrics such as, "Those summer days I spent with you on the sandy beach with the sound of waves echoing." It can also analyze a video of a broken heart provided by the user, and the generation AI generates lyrics based on that visual. For example, it creates lyrics such as, "On nights when tears run down my cheeks, I remember you." It can also analyze a bright and cheerful photo provided by the user, and the generation AI generates lyrics based on that visual. For example, it creates lyrics such as, "You dancing in the sun, your smile shining." This makes it possible to analyze photos and videos provided by the user and generate lyrics based on them.

[0037] The lyric generation unit can refer to the user's social media posts and incorporate related topics and keywords. For example, the generation AI in the lyric generation unit analyzes the user's social media posts and generates lyrics based on the content of recent posts. For example, lyrics that reflect travel photos and comments posted by the user are created. Topics and keywords can also be extracted from the user's social media posts, and the generation AI generates lyrics based on them. For example, lyrics that reflect emotions and events posted by the user are created. The generation AI can also refer to the user's social media posts and generate lyrics based on specific hashtags and trends. For example, lyrics that reflect hashtags used by the user are created. This makes it possible to refer to the user's social media posts and incorporate related topics and keywords.

[0038] The melody generation unit can automatically select the tones of different instruments and create an arrangement that suits the user's preferences. For example, the generation AI analyzes the user's past song history and automatically selects the preferred instrument tones. For example, if the user prefers piano tones, it generates a melody that mainly features piano. The generation AI also selects the optimal instrument tones based on the theme and mood specified by the user. For example, for a mood such as "bright and lively," it generates a melody that mainly features guitar and drums. The generation AI also combines the tones of different instruments to create an arrangement that suits the user's preferences. For example, it generates a melody that combines violin and piano. This allows the tones of different instruments to be automatically selected and an arrangement that suits the user's preferences.

[0039] The melody generation unit can incorporate environmental sounds specified by the user into the background. For example, if the user specifies a theme such as "fun memories at the beach in summer," the generation AI generates a melody that incorporates the sound of waves in the background. For example, it creates a melody with the sound of waves flowing in rhythm. If the user specifies a mood such as "I want to relax," the generation AI generates a melody that incorporates the sound of rain in the background. For example, it creates a melody with the sound of rain gently flowing. If the user specifies a theme such as "quiet time in the forest," the generation AI generates a melody that incorporates the sound of birds chirping in the background. For example, it creates a piece of music in which the birds' voices blend into the melody. This allows the environmental sounds specified by the user to be incorporated into the background.

[0040] The melody generation unit can analyze the user's walking rhythm and exercise data and generate a rhythmic melody based on that. For example, the melody generation unit analyzes the user's walking rhythm in real time, and the generation AI generates a melody that matches that rhythm. For example, a melody is created with a tempo adjusted according to walking speed. The unit also analyzes the user's exercise data, and the generation AI generates a rhythmic melody that matches the movement. For example, an up-tempo melody is created based on data from running. The unit also simultaneously analyzes the user's walking rhythm and exercise data, and the generation AI generates a melody based on that. For example, a rhythmic melody is created based on data from walking. This makes it possible to analyze the user's walking rhythm and exercise data and generate a rhythmic melody based on that.

[0041] The music integration unit can analyze the characteristics of the user's voice and automatically adjust the optimal key and tempo. For example, the music integration unit analyzes the characteristics of the user's voice and the generation AI automatically adjusts the optimal key and tempo for that voice. For example, if the user's voice is strong in the high range, the generation AI generates music in a higher key. The unit also analyzes the characteristics of the user's voice and the generation AI automatically adjusts the optimal tempo for that voice. For example, if the user's voice is strong in the mid-range, the generation AI generates music at a slow tempo. The unit also analyzes the characteristics of the user's voice and the generation AI automatically adjusts the optimal key and tempo for that voice at the same time. For example, if the user's voice is strong in the mid-range, the generation AI generates music in a medium key and tempo. This makes it possible to analyze the characteristics of the user's voice and automatically adjust the optimal key and tempo for that voice.

[0042] The music integration unit can incorporate elements from different genres to generate cross-genre music tailored to the user's preferences. For example, the music integration unit generates cross-genre music by having the generation AI combine elements of pop and rock based on a theme or mood specified by the user. For example, a rock guitar riff may be incorporated into a pop melody. The generation AI may also generate cross-genre music by combining elements of jazz and hip-hop based on the user's preferences. For example, a hip-hop beat may be incorporated into a jazz saxophone. The generation AI may also analyze the user's past music history to generate cross-genre music by combining elements of classical and electronica. For example, an electronica synthesizer may be incorporated into a classical string instrument. This allows the generation AI to incorporate elements from different genres and generate cross-genre music tailored to the user's preferences.

[0043] The music integration unit can analyze the user's exercise data and generate music that is optimal for a fitness or yoga session. For example, the music integration unit analyzes the user's exercise data, and the generation AI generates music that is optimal for a fitness session. For example, an up-tempo piece of music is created based on data from running. The user's exercise data is also analyzed, and the generation AI generates music that is optimal for a yoga session. For example, a gentle melody is created to match a relaxed state. The user's exercise data is also analyzed, and the generation AI generates music that is optimal for a fitness or yoga session. For example, a rhythmic piece of music is created based on data from exercise. In this way, the user's exercise data can be analyzed and music that is optimal for a fitness or yoga session can be generated.

[0044] The music output unit can refer to the user's past music history and automatically add the generated music to a playlist. In the music output unit, for example, the generation AI analyzes the user's past music history and automatically adds the generated music to a playlist. For example, the playlist is updated based on the genres and artists the user often listens to. The generation AI also refers to the user's past music history and automatically adds newly generated music to an optimal playlist. For example, the music is added to a theme-based playlist created by the user. The generation AI also analyzes the user's past music history and automatically adds the generated music to a playlist. For example, the playlist is updated to match the time of day or situation when the user often listens. In this way, the generated music can be automatically added to a playlist by referring to the user's past music history.

[0045] The music output unit can provide a function to automatically share the generated music on a social media platform designated by the user. For example, the music output unit can provide a function to automatically share a music completed by the generation AI on a social media platform designated by the user. For example, the music can be posted on Facebook or Twitter. The unit can also provide a function to automatically share a music on a social media platform designated by the user, with the generation AI posting the music in a format optimal for that platform. For example, the music can be shared in the form of a story on Instagram. The unit can also provide a function to automatically share a completed music on a social media platform designated by the user, with the generation AI notifying the user's followers. For example, the music can be posted in the form of a video on YouTube. This makes it possible to provide a function to automatically share the generated music on a social media platform designated by the user.

[0046] The music output unit can refer to the user's calendar and automatically suggest music that matches a specific event. In the music output unit, for example, the generation AI refers to the user's calendar and automatically suggests music that matches a specific event. For example, it suggests the best music for a birthday party. The generation AI can also analyze the user's calendar and automatically suggest the best music for that event. For example, it can suggest music that matches a wedding or anniversary. The generation AI can also refer to the user's calendar and automatically suggest music that matches a specific event. For example, it can suggest the best music for a Christmas or New Year's event. In this way, the generation AI can refer to the user's calendar and automatically suggest music that matches a specific event.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] In the theme specification unit, when the user inputs a theme or mood, the generation AI provides real-time feedback, helping to select the optimal theme or mood. For example, when the user inputs the theme "fun memories at the beach in summer," the generation AI provides real-time feedback, suggesting the addition of specific episodes and emotions. Also, when the user inputs the mood "sadness from a broken heart," the generation AI provides real-time feedback, suggesting the addition of specific events and emotions. Also, when the user inputs the mood "cheerful and cheerful," the generation AI provides real-time feedback, suggesting the addition of specific situations and emotions. In this way, when the user inputs a theme or mood, the generation AI provides real-time feedback, helping to select the optimal theme or mood.

[0049] The theme specification unit allows the generation AI to refer to a past music database and suggest songs with a similar theme or mood to the user's specified theme or mood. For example, if a user specifies the theme "happy summer memories at the beach," the generation AI searches the past music database for songs with a similar theme and suggests them to the user. For example, it might suggest Beach Boys songs or surf rock songs. If a user specifies the mood "sadness over a broken heart," the generation AI searches the past music database for songs related to heartbreak and suggests them to the user. For example, it might suggest Adele's "Someone Like You" or Taylor Swift's "All Too Well." If a user specifies the mood "cheerful and cheerful," the generation AI searches the past music database for songs with a similar mood and suggests them to the user. For example, it might suggest Pharrell Williams' "Happy" or Justin Timberlake's "Can't Stop the Feeling!". This allows the generation AI to suggest similar songs from the past music database based on the user's specified theme or mood.

[0050] The theme specification unit allows the generation AI to automatically generate related visuals and videos based on the theme and mood input by the user, thereby providing inspiration for the song. For example, if the user specifies the theme "fun memories at the beach in summer," the generation AI automatically generates videos of beaches and oceans and provides them to the user. This allows the user to gain visual inspiration. If the user specifies the mood "sadness from a broken heart," the generation AI automatically generates videos of rain and tears and provides them to the user. This allows the user to gain visual inspiration. If the user specifies the mood "bright and cheerful," the generation AI automatically generates videos of sunshine and smiles and provides them to the user. This allows the user to gain visual inspiration. This allows the generation AI to automatically generate related visuals and videos based on the theme and mood input by the user, thereby providing inspiration for the song.

[0051] The theme specification unit allows the user to input a theme or mood by voice, and the generation AI can analyze the theme or mood using voice recognition technology. For example, if the user inputs the theme "fun memories at the beach in summer" by voice, the generation AI analyzes the theme using voice recognition technology and uses it as a prompt for generating music. Also, if the user inputs the mood "sadness from a broken heart" by voice, the generation AI analyzes the mood using voice recognition technology and uses it as a prompt for generating music. Also, if the user inputs the mood "cheerful and cheerful" by voice, the generation AI analyzes the mood using voice recognition technology and uses it as a prompt for generating music. This allows the user to input a theme or mood by voice, and the generation AI can analyze the theme or mood using voice recognition technology.

[0052] The lyrics generation unit can generate personalized lyrics by referencing the user's past music preferences and playlists. For example, the generation AI can reference a playlist of songs the user has listened to in the past and generate lyrics based on those preferences. For example, it can create lyrics that reflect the style of the user's favorite artists. The generation AI can also analyze the user's past music history and generate lyrics based on those tendencies. For example, it can create lyrics that reflect the user's favorite themes and moods. The generation AI can also analyze the lyrics of songs included in the user's playlist and generate lyrics that incorporate those characteristics. For example, it can create lyrics that reflect specific phrases and expressions. This makes it possible to generate personalized lyrics by referencing the user's past music preferences and playlists.

[0053] The lyrics generation unit can simultaneously generate lyrics in different languages, thereby providing multilingual music. For example, the generation AI can simultaneously generate English and Japanese lyrics based on a theme or mood specified by the user. For example, it can provide English lyrics and their translations. Furthermore, the generation AI can simultaneously generate Spanish and French lyrics based on the user's specifications. For example, it can provide Spanish lyrics and their translations. Furthermore, the generation AI can simultaneously generate Chinese and Korean lyrics based on the user's specifications. For example, it can provide Chinese lyrics and their translations. This allows lyrics in different languages ​​to be simultaneously generated, thereby providing multilingual music.

[0054] The melody generation unit can automatically select the tones of different instruments and create arrangements that suit the user's preferences. For example, the generation AI analyzes the user's past song history and automatically selects the preferred instrument tones. For example, if the user prefers piano tones, it will generate a melody that mainly features piano. The generation AI also selects the optimal instrument tones based on the theme and mood specified by the user. For example, for a "bright and lively" mood, it will generate a melody that mainly features guitar and drums. The generation AI also combines the tones of different instruments to create arrangements that suit the user's preferences. For example, it can generate a melody that combines violin and piano. This allows the tones of different instruments to be automatically selected and arrangements that suit the user's preferences.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The theme specification unit accepts the theme and mood specified by the user. For example, the user can specify the theme "happy memories of summer at the beach" or the mood "sadness of a broken heart." Step 2: The lyrics generator generates original lyrics based on the theme and mood specified by the theme specification unit. For example, for the theme "Happy summer memories at the beach," it generates lyrics such as "Those summer days I spent with you on the sandy beach with the sound of waves echoing," and for the mood "Sadness of a broken heart," it generates lyrics such as "On nights when tears run down my cheeks, I remember you." Step 3: The melody generation unit generates an original melody to match the lyrics generated by the lyrics generation unit. For example, a fast-tempo, rhythmic melody is generated for a "bright and lively" mood, and a slow, emotional melody is generated for a "sad and mellow" mood.

[0057] (Example 2) The music generation system according to an embodiment of the present invention is a system in which a generation AI automatically generates original lyrics and melodies based on a theme and mood specified by a user. This allows the music generation system to provide original music based on the user's specifications without the involvement of a professional songwriter.

[0058] A music generation system according to an embodiment includes a theme specification unit, a lyrics generation unit, and a melody generation unit. The theme specification unit accepts a theme or mood specified by a user. For example, the user can specify a theme such as "happy summer memories at the beach." The user can also specify a mood such as "sadness over a broken heart." The lyrics generation unit generates original lyrics based on the theme or mood specified by the theme specification unit. For example, for the theme "happy summer memories at the beach," the generation AI generates lyrics such as "Those summer days spent with you on the sandy beach with the sound of waves echoing." For the mood "sadness over a broken heart," the generation AI generates lyrics such as "On nights when tears run down my cheeks, I remember you." The melody generation unit generates an original melody to match the lyrics generated by the lyrics generation unit. For example, for a mood of "bright and cheerful," the generation AI generates an up-tempo, rhythmic melody. For a mood of "sadness over a broken heart," the generation AI generates a slow, emotional melody. As a result, the music creation system according to the embodiment can automatically create original music based on a theme or mood specified by a user. For example, a user can quickly create music that matches a theme or mood for a specific event or personal project. Furthermore, because there is no need to involve professional songwriters, it is possible to provide high-quality music while keeping costs down.

[0059] The theme specification unit allows the generation AI to refer to a past music database and suggest songs with a similar theme or mood to the theme or mood specified by the user. For example, if the user specifies the theme "fun summer memories at the beach," the generation AI searches the past music database for songs with a similar theme and suggests them to the user. For example, it may suggest Beach Boys songs or surf rock songs. If the user specifies the mood "sadness over a broken heart," the generation AI searches the past music database for songs related to heartbreak and suggests them to the user. For example, it may suggest Adele's "Someone Like You" or Taylor Swift's "All Too Well." If the user specifies the mood "cheerful and cheerful," the generation AI searches the past music database for songs with a similar mood and suggests them to the user. For example, it may suggest Pharrell Williams' "Happy" or Justin Timberlake's "Can't Stop the Feeling!". This allows the generation AI to suggest similar songs from the past music database based on the user's specified theme or mood.

[0060] The theme specification unit allows the generation AI to automatically generate related visuals and videos based on the theme or mood input by the user, thereby providing inspiration for the song. For example, if the user specifies the theme "fun summer memories at the beach," the generation AI automatically generates videos of beaches and oceans and provides them to the user. This allows the user to gain visual inspiration. If the user specifies the mood "sadness from a broken heart," the generation AI automatically generates videos of rain and tears and provides them to the user. This allows the user to gain visual inspiration. If the user specifies the mood "bright and cheerful," the generation AI automatically generates videos of sunshine and smiles and provides them to the user. This allows the user to gain visual inspiration. This allows the generation AI to automatically generate related visuals and videos based on the theme or mood input by the user, thereby providing inspiration for the song.

[0061] The theme specification unit uses the emotion estimation function to analyze the emotions associated with the theme or mood entered by the user and can suggest a musical style that best suits that emotion. For example, if the user specifies the theme "Fun summer memories at the beach," the theme specification unit uses the emotion estimation function to analyze the user's emotions and detects that positive emotions are strong. The generation AI suggests pop or surf rock styles. If the user specifies the mood "Sadness from a broken heart," the emotion estimation function analyzes the user's emotions and detects that negative emotions are strong. The generation AI suggests ballad or acoustic styles. If the user specifies the mood "cheerful and lively," the emotion estimation function analyzes the user's emotions and detects that energetic emotions are strong. The generation AI suggests up-tempo pop or dance music styles. This allows the unit to analyze the emotions associated with the theme or mood entered by the user and suggest a musical style that best suits that emotion.

[0062] The theme specification unit allows the user to input a theme or mood by voice, and the generation AI can analyze the theme or mood using voice recognition technology. For example, if the user inputs the theme "fun memories at the beach in summer" by voice, the generation AI analyzes the theme using voice recognition technology and uses it as a prompt for generating music. Also, if the user inputs the mood "sadness from a broken heart" by voice, the generation AI analyzes the mood using voice recognition technology and uses it as a prompt for generating music. Also, if the user inputs the mood "cheerful and cheerful" by voice, the generation AI analyzes the mood using voice recognition technology and uses it as a prompt for generating music. This allows the user to input a theme or mood by voice, and the generation AI can analyze the theme or mood using voice recognition technology.

[0063] When the user specifies a theme or mood, the generation AI provides real-time feedback to the theme specification unit, thereby assisting in the selection of the optimal theme or mood. For example, when the user inputs the theme "fun memories at the beach in summer," the generation AI provides real-time feedback and suggests adding specific episodes and emotions. When the user inputs the mood "sadness from a broken heart," the generation AI provides real-time feedback and suggests adding specific events and emotions. When the user inputs the mood "cheerful and cheerful," the generation AI provides real-time feedback and suggests adding specific situations and emotions. In this way, when the user specifies a theme or mood, the generation AI provides real-time feedback to assist in the selection of the optimal theme or mood.

[0064] The theme specification unit uses the emotion estimation function to analyze the user's facial expression and tone of voice when inputting a theme or mood, thereby enabling more accurate identification of the theme or mood. For example, when a user inputs the theme "fun memories at the beach in summer," the theme specification unit uses the emotion estimation function to analyze the user's facial expression and tone of voice and detect a strong positive emotion. Furthermore, when a user inputs the mood "sadness from a broken heart," the emotion estimation function analyzes the user's facial expression and tone of voice and detects a strong negative emotion. Furthermore, when a user inputs the mood "cheerful and energetic," the emotion estimation function analyzes the user's facial expression and tone of voice and detects a strong energetic emotion. This allows the user to analyze the user's facial expression and tone of voice when inputting a theme or mood, enabling more accurate identification of the theme or mood.

[0065] The lyrics generation unit can generate personalized lyrics by referring to the user's past music preferences and playlists. For example, the lyrics generation unit references a playlist of music that the user has listened to in the past, and the generation AI generates lyrics based on those preferences. For example, lyrics that reflect the style of the user's favorite artists are created. The unit also analyzes the user's past music history, and the generation AI generates lyrics based on those tendencies. For example, lyrics that reflect the user's favorite themes and moods are created. The unit also analyzes the lyrics of music included in the user's playlist, and the generation AI generates lyrics that incorporate those characteristics. For example, lyrics that reflect specific phrases and expressions are created. In this way, the unit can generate personalized lyrics by referring to the user's past music preferences and playlists.

[0066] The lyrics generation unit can simultaneously generate lyrics in different languages, thereby providing multilingual music. For example, the lyrics generation unit uses a generation AI to simultaneously generate English and Japanese lyrics based on a theme or mood specified by the user. For example, English lyrics and their translations are provided. The generation AI can also simultaneously generate Spanish and French lyrics based on the user's specifications. For example, Spanish lyrics and their translations are provided. The generation AI can also simultaneously generate Chinese and Korean lyrics based on the user's specifications. For example, Chinese lyrics and their translations are provided. This allows lyrics in different languages ​​to be simultaneously generated, thereby providing multilingual music.

[0067] The lyrics generation unit can analyze photos and videos provided by the user and generate lyrics based on them. For example, the lyrics generation unit analyzes a summer beach photo provided by the user, and the generation AI generates lyrics based on that visual. For example, it creates lyrics such as, "Those summer days I spent with you on the sandy beach with the sound of waves echoing." It can also analyze a video of a broken heart provided by the user, and the generation AI generates lyrics based on that visual. For example, it creates lyrics such as, "On nights when tears run down my cheeks, I remember you." It can also analyze a bright and cheerful photo provided by the user, and the generation AI generates lyrics based on that visual. For example, it creates lyrics such as, "You dancing in the sun, your smile shining." This makes it possible to analyze photos and videos provided by the user and generate lyrics based on them.

[0068] The lyric generation unit can refer to the user's social media posts and incorporate related topics and keywords. For example, the generation AI in the lyric generation unit analyzes the user's social media posts and generates lyrics based on the content of recent posts. For example, lyrics that reflect travel photos and comments posted by the user are created. Topics and keywords can also be extracted from the user's social media posts, and the generation AI generates lyrics based on them. For example, lyrics that reflect emotions and events posted by the user are created. The generation AI can also refer to the user's social media posts and generate lyrics based on specific hashtags and trends. For example, lyrics that reflect hashtags used by the user are created. This makes it possible to refer to the user's social media posts and incorporate related topics and keywords.

[0069] The lyrics generation unit uses the emotion estimation function to analyze the emotions of the user when inputting lyrics in real time and can suggest lyrics that match the emotions. For example, when the user inputs lyrics, the lyrics generation unit uses the emotion estimation function to analyze the user's facial expression and tone of voice and suggest lyrics that evoke positive emotions. Also, when the user inputs lyrics, the emotion estimation function is used to analyze the user's emotions in real time and adjust the lyrics to avoid negative emotions. Also, the emotion estimation function is used to analyze the emotions of the user when inputting lyrics and suggest lyrics that are most emotionally relatable. In this way, the emotions of the user when inputting lyrics can be analyzed in real time and lyrics that match the emotions can be suggested.

[0070] The melody generation unit can automatically select the tones of different instruments and create an arrangement that suits the user's preferences. For example, the generation AI analyzes the user's past song history and automatically selects the preferred instrument tones. For example, if the user prefers piano tones, it generates a melody that mainly features piano. The generation AI also selects the optimal instrument tones based on the theme and mood specified by the user. For example, for a mood such as "bright and lively," it generates a melody that mainly features guitar and drums. The generation AI also combines the tones of different instruments to create an arrangement that suits the user's preferences. For example, it generates a melody that combines violin and piano. This allows the tones of different instruments to be automatically selected and an arrangement that suits the user's preferences.

[0071] The melody generation unit uses the emotion estimation function to analyze how the generated melody affects the user's emotions and selects the optimal melody. For example, the melody generation unit uses the emotion estimation function to analyze the user's emotional response to a melody generated by the generation AI and selects a melody that elicits positive emotions. It also monitors the impact of the generated melody on the user's emotions in real time and adjusts the melody to avoid negative emotions. It also uses the emotion estimation function to analyze the impact of the generated melody on the user's emotions and selects the melody that most emotionally resonates with the user. This makes it possible to analyze how the generated melody affects the user's emotions and select the optimal melody.

[0072] The melody generation unit can incorporate environmental sounds specified by the user into the background. For example, if the user specifies a theme such as "fun memories at the beach in summer," the generation AI generates a melody that incorporates the sound of waves in the background. For example, it creates a melody with the sound of waves flowing in rhythm. If the user specifies a mood such as "I want to relax," the generation AI generates a melody that incorporates the sound of rain in the background. For example, it creates a melody with the sound of rain gently flowing. If the user specifies a theme such as "quiet time in the forest," the generation AI generates a melody that incorporates the sound of birds chirping in the background. For example, it creates a piece of music in which the birds' voices blend into the melody. This allows the environmental sounds specified by the user to be incorporated into the background.

[0073] The melody generation unit can analyze the user's walking rhythm and exercise data and generate a rhythmic melody based on that. For example, the melody generation unit analyzes the user's walking rhythm in real time, and the generation AI generates a melody that matches that rhythm. For example, a melody is created with a tempo adjusted according to walking speed. The unit also analyzes the user's exercise data, and the generation AI generates a rhythmic melody that matches the movement. For example, an up-tempo melody is created based on data from running. The unit also simultaneously analyzes the user's walking rhythm and exercise data, and the generation AI generates a melody based on that. For example, a rhythmic melody is created based on data from walking. This makes it possible to analyze the user's walking rhythm and exercise data and generate a rhythmic melody based on that.

[0074] The melody generation unit uses the emotion estimation function to analyze the emotion of the user when inputting a melody in real time and can suggest a melody that matches the emotion. For example, when the user inputs a melody, the melody generation unit uses the emotion estimation function to analyze the user's facial expression and tone of voice and suggest a melody that elicits positive emotions. Furthermore, when the user inputs a melody, the emotion estimation function is used to analyze the user's emotion in real time and adjust the melody to avoid negative emotions. Furthermore, the emotion estimation function is used to analyze the emotion of the user when inputting a melody and suggest a melody that is most emotionally relatable. In this way, the emotion of the user when inputting a melody can be analyzed in real time and a melody that matches the emotion can be suggested.

[0075] The music integration unit can analyze the characteristics of the user's voice and automatically adjust the optimal key and tempo. For example, the music integration unit analyzes the characteristics of the user's voice and the generation AI automatically adjusts the optimal key and tempo for that voice. For example, if the user's voice is strong in the high range, the generation AI generates music in a higher key. The unit also analyzes the characteristics of the user's voice and the generation AI automatically adjusts the optimal tempo for that voice. For example, if the user's voice is strong in the mid-range, the generation AI generates music at a slow tempo. The unit also analyzes the characteristics of the user's voice and the generation AI automatically adjusts the optimal key and tempo for that voice at the same time. For example, if the user's voice is strong in the mid-range, the generation AI generates music in a medium key and tempo. This makes it possible to analyze the characteristics of the user's voice and automatically adjust the optimal key and tempo for that voice.

[0076] The music integration unit can incorporate elements from different genres to generate cross-genre music tailored to the user's preferences. For example, the music integration unit generates cross-genre music by having the generation AI combine elements of pop and rock based on a theme or mood specified by the user. For example, a rock guitar riff may be incorporated into a pop melody. The generation AI may also generate cross-genre music by combining elements of jazz and hip-hop based on the user's preferences. For example, a hip-hop beat may be incorporated into a jazz saxophone. The generation AI may also analyze the user's past music history to generate cross-genre music by combining elements of classical and electronica. For example, an electronica synthesizer may be incorporated into a classical string instrument. This allows the generation AI to incorporate elements from different genres and generate cross-genre music tailored to the user's preferences.

[0077] The music integration unit uses an emotion estimation function to analyze how the integrated music affects the user's emotions and selects the optimal music. For example, the music integration unit uses the emotion estimation function to analyze the user's emotional response to the music integrated by the generation AI and selects music that elicits positive emotions. It also monitors the impact of the integrated music on the user's emotions in real time and adjusts the music to avoid negative emotions. It also uses the emotion estimation function to analyze the impact of the integrated music on the user's emotions and selects the music that most emotionally resonates with the user. This allows it to analyze how the integrated music affects the user's emotions and select the optimal music.

[0078] The music integration unit can analyze the user's exercise data and generate music that is optimal for a fitness or yoga session. For example, the music integration unit analyzes the user's exercise data, and the generation AI generates music that is optimal for a fitness session. For example, an up-tempo piece of music is created based on data from running. The user's exercise data is also analyzed, and the generation AI generates music that is optimal for a yoga session. For example, a gentle melody is created to match a relaxed state. The user's exercise data is also analyzed, and the generation AI generates music that is optimal for a fitness or yoga session. For example, a rhythmic piece of music is created based on data from exercise. In this way, the user's exercise data can be analyzed and music that is optimal for a fitness or yoga session can be generated.

[0079] The music integration unit uses the emotion estimation function to analyze the emotions of the user when integrating songs in real time and can suggest music that matches the emotions. For example, when the user integrates songs, the music integration unit uses the emotion estimation function to analyze the user's facial expressions and tone of voice and suggest music that elicits positive emotions. Furthermore, when the user integrates songs, the emotion estimation function is used to analyze the user's emotions in real time and adjust the music to avoid negative emotions. Furthermore, the emotion estimation function is used to analyze the emotions of the user when integrating songs and suggest music that most emotionally resonates with the user. In this way, the emotions of the user when integrating songs can be analyzed in real time and music can be suggested that matches the emotions.

[0080] The music output unit can refer to the user's past music history and automatically add the generated music to a playlist. In the music output unit, for example, the generation AI analyzes the user's past music history and automatically adds the generated music to a playlist. For example, the playlist is updated based on the genres and artists the user often listens to. The generation AI also refers to the user's past music history and automatically adds newly generated music to an optimal playlist. For example, the music is added to a theme-based playlist created by the user. The generation AI also analyzes the user's past music history and automatically adds the generated music to a playlist. For example, the playlist is updated to match the time of day or situation when the user often listens. In this way, the generated music can be automatically added to a playlist by referring to the user's past music history.

[0081] The music output unit uses the emotion estimation function to analyze the emotion a user feels when downloading music and recommends music according to the emotion. For example, when a user downloads music, the music output unit uses the emotion estimation function to analyze the user's facial expression and tone of voice and recommends music that elicits positive emotions. Furthermore, when a user downloads music, the music output unit uses the emotion estimation function to analyze the user's emotion in real time and recommends music that avoids negative emotions. Furthermore, the emotion estimation function is used to analyze the emotion a user feels when downloading music and recommends music that most emotionally resonates with the user. This makes it possible to analyze the emotion a user feels when downloading music and recommend music according to the emotion.

[0082] The music output unit can provide a function to automatically share the generated music on a social media platform designated by the user. For example, the music output unit can provide a function to automatically share a music completed by the generation AI on a social media platform designated by the user. For example, the music can be posted on Facebook or Twitter. The unit can also provide a function to automatically share a music on a social media platform designated by the user, with the generation AI posting the music in a format optimal for that platform. For example, the music can be shared in the form of a story on Instagram. The unit can also provide a function to automatically share a completed music on a social media platform designated by the user, with the generation AI notifying the user's followers. For example, the music can be posted in the form of a video on YouTube. This makes it possible to provide a function to automatically share the generated music on a social media platform designated by the user.

[0083] The music output unit can refer to the user's calendar and automatically suggest music that matches a specific event. In the music output unit, for example, the generation AI refers to the user's calendar and automatically suggests music that matches a specific event. For example, it suggests the best music for a birthday party. The generation AI can also analyze the user's calendar and automatically suggest the best music for that event. For example, it can suggest music that matches a wedding or anniversary. The generation AI can also refer to the user's calendar and automatically suggest music that matches a specific event. For example, it can suggest the best music for a Christmas or New Year's event. In this way, the generation AI can refer to the user's calendar and automatically suggest music that matches a specific event.

[0084] The music output unit can use the emotion estimation function to analyze the emotions of the user when outputting music in real time and suggest music edits that correspond to the emotions. For example, when the user outputs music, the music output unit uses the emotion estimation function to analyze the user's facial expressions and tone of voice and suggest music edits that bring out positive emotions. Also, when the user outputs music, the emotion estimation function can analyze the user's emotions in real time and suggest music edits that avoid negative emotions. Also, the emotion estimation function can analyze the emotions of the user when outputting music and suggest music edits that most emotionally resonate with the user. In this way, the emotions of the user when outputting music can be analyzed in real time and music edits that correspond to the emotions can be suggested.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] In the theme specification unit, when the user inputs a theme or mood, the generation AI provides real-time feedback, helping to select the optimal theme or mood. For example, when the user inputs the theme "fun memories at the beach in summer," the generation AI provides real-time feedback, suggesting the addition of specific episodes and emotions. Also, when the user inputs the mood "sadness from a broken heart," the generation AI provides real-time feedback, suggesting the addition of specific events and emotions. Also, when the user inputs the mood "cheerful and cheerful," the generation AI provides real-time feedback, suggesting the addition of specific situations and emotions. In this way, when the user inputs a theme or mood, the generation AI provides real-time feedback, helping to select the optimal theme or mood.

[0087] The theme specification unit allows the generation AI to refer to a past music database and suggest songs with a similar theme or mood to the user's specified theme or mood. For example, if a user specifies the theme "happy summer memories at the beach," the generation AI searches the past music database for songs with a similar theme and suggests them to the user. For example, it might suggest Beach Boys songs or surf rock songs. If a user specifies the mood "sadness over a broken heart," the generation AI searches the past music database for songs related to heartbreak and suggests them to the user. For example, it might suggest Adele's "Someone Like You" or Taylor Swift's "All Too Well." If a user specifies the mood "cheerful and cheerful," the generation AI searches the past music database for songs with a similar mood and suggests them to the user. For example, it might suggest Pharrell Williams' "Happy" or Justin Timberlake's "Can't Stop the Feeling!". This allows the generation AI to suggest similar songs from the past music database based on the user's specified theme or mood.

[0088] The theme specification unit allows the generation AI to automatically generate related visuals and videos based on the theme and mood input by the user, thereby providing inspiration for the song. For example, if the user specifies the theme "fun memories at the beach in summer," the generation AI automatically generates videos of beaches and oceans and provides them to the user. This allows the user to gain visual inspiration. If the user specifies the mood "sadness from a broken heart," the generation AI automatically generates videos of rain and tears and provides them to the user. This allows the user to gain visual inspiration. If the user specifies the mood "bright and cheerful," the generation AI automatically generates videos of sunshine and smiles and provides them to the user. This allows the user to gain visual inspiration. This allows the generation AI to automatically generate related visuals and videos based on the theme and mood input by the user, thereby providing inspiration for the song.

[0089] The theme specification unit uses the emotion estimation function to analyze the emotions associated with the theme or mood entered by the user and suggests the style of music that best suits that emotion. For example, if the user specifies the theme "Fun summer memories at the beach," the emotion estimation function analyzes the user's emotions and detects a strong positive emotion. The generation AI suggests pop or surf rock styles. If the user specifies the mood "Sadness from a broken heart," the emotion estimation function analyzes the user's emotions and detects a strong negative emotion. The generation AI suggests ballad or acoustic styles. If the user specifies the mood "cheerful and lively," the emotion estimation function analyzes the user's emotions and detects a strong energetic emotion. The generation AI suggests up-tempo pop or dance music styles. This allows the system to analyze the emotions associated with the theme or mood entered by the user and suggest the style of music that best suits that emotion.

[0090] The theme specification unit allows the user to input a theme or mood by voice, and the generation AI can analyze the theme or mood using voice recognition technology. For example, if the user inputs the theme "fun memories at the beach in summer" by voice, the generation AI analyzes the theme using voice recognition technology and uses it as a prompt for generating music. Also, if the user inputs the mood "sadness from a broken heart" by voice, the generation AI analyzes the mood using voice recognition technology and uses it as a prompt for generating music. Also, if the user inputs the mood "cheerful and cheerful" by voice, the generation AI analyzes the mood using voice recognition technology and uses it as a prompt for generating music. This allows the user to input a theme or mood by voice, and the generation AI can analyze the theme or mood using voice recognition technology.

[0091] The theme specification unit uses the emotion estimation function to analyze the user's facial expression and tone of voice when inputting a theme or mood, thereby enabling more accurate identification of the theme or mood. For example, when a user inputs the theme "fun memories at the beach in summer," the emotion estimation function analyzes the user's facial expression and tone of voice to detect a strong positive emotion. When a user inputs the mood "sadness from a broken heart," the emotion estimation function analyzes the user's facial expression and tone of voice to detect a strong negative emotion. When a user inputs the mood "cheerful and energetic," the emotion estimation function analyzes the user's facial expression and tone of voice to detect a strong energetic emotion. This allows the user to analyze the user's facial expression and tone of voice when inputting a theme or mood, enabling more accurate identification of the theme or mood.

[0092] The lyrics generation unit can generate personalized lyrics by referencing the user's past music preferences and playlists. For example, the generation AI can reference a playlist of songs the user has listened to in the past and generate lyrics based on those preferences. For example, it can create lyrics that reflect the style of the user's favorite artists. The generation AI can also analyze the user's past music history and generate lyrics based on those tendencies. For example, it can create lyrics that reflect the user's favorite themes and moods. The generation AI can also analyze the lyrics of songs included in the user's playlist and generate lyrics that incorporate those characteristics. For example, it can create lyrics that reflect specific phrases and expressions. This makes it possible to generate personalized lyrics by referencing the user's past music preferences and playlists.

[0093] The lyrics generation unit can simultaneously generate lyrics in different languages, thereby providing multilingual music. For example, the generation AI can simultaneously generate English and Japanese lyrics based on a theme or mood specified by the user. For example, it can provide English lyrics and their translations. Furthermore, the generation AI can simultaneously generate Spanish and French lyrics based on the user's specifications. For example, it can provide Spanish lyrics and their translations. Furthermore, the generation AI can simultaneously generate Chinese and Korean lyrics based on the user's specifications. For example, it can provide Chinese lyrics and their translations. This allows lyrics in different languages ​​to be simultaneously generated, thereby providing multilingual music.

[0094] The lyrics generation unit uses the emotion estimation function to analyze the emotions of the user when entering lyrics in real time and suggest lyrics that match the emotions. For example, when the user enters lyrics, the emotion estimation function is used to analyze the user's facial expression and tone of voice, and suggest lyrics that evoke positive emotions. Also, when the user enters lyrics, the emotion estimation function is used to analyze the user's emotions in real time and adjust the lyrics to avoid negative emotions. Also, the emotion estimation function is used to analyze the emotions of the user when entering lyrics, and suggest lyrics that will most emotionally resonate with the user. In this way, the emotions of the user when entering lyrics can be analyzed in real time and lyrics that match the emotions can be suggested.

[0095] The melody generation unit can automatically select the tones of different instruments and create arrangements that suit the user's preferences. For example, the generation AI analyzes the user's past song history and automatically selects the preferred instrument tones. For example, if the user prefers piano tones, it will generate a melody that mainly features piano. The generation AI also selects the optimal instrument tones based on the theme and mood specified by the user. For example, for a "bright and lively" mood, it will generate a melody that mainly features guitar and drums. The generation AI also combines the tones of different instruments to create arrangements that suit the user's preferences. For example, it can generate a melody that combines violin and piano. This allows the tones of different instruments to be automatically selected and arrangements that suit the user's preferences.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The theme specification unit accepts the theme and mood specified by the user. For example, the user can specify the theme "happy memories of summer at the beach" or the mood "sadness of a broken heart." Step 2: The lyrics generator generates original lyrics based on the theme and mood specified by the theme specification unit. For example, for the theme "Happy summer memories at the beach," it generates lyrics such as "Those summer days I spent with you on the sandy beach with the sound of waves echoing," and for the mood "Sadness of a broken heart," it generates lyrics such as "On nights when tears run down my cheeks, I remember you." Step 3: The melody generation unit generates an original melody to match the lyrics generated by the lyrics generation unit. For example, a fast-tempo, rhythmic melody is generated for a "bright and lively" mood, and a slow, emotional melody is generated for a "sad and mellow" mood.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The 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.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] In the robot 414, 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 robot 414 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.

[0143] 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.

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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. [Explanation of symbols]

[0165] 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 theme specification section for accepting a theme or mood specified by a user; a lyrics generation unit that generates original lyrics based on the theme and mood designated by the theme designation unit; a melody generation unit that generates an original melody in accordance with the lyrics generated by the lyrics generation unit. A system characterized by:

2. The theme designation unit When a user specifies a theme or mood, the AI ​​refers to a database of past songs and suggests songs with a similar theme or mood.

2. The system of claim 1.

3. The theme designation unit Based on the theme and mood entered by the user, the generative AI automatically generates related visuals and videos to provide inspiration for songs.

2. The system of claim 1.

4. The theme designation unit Analyzes the user's emotions regarding themes and moods they input, and suggests the style of music that best suits those emotions 2. The system of claim 1.

5. The theme designation unit Users can input the theme and mood by voice, and the generation AI analyzes the theme and mood using voice recognition technology.

2. The system of claim 1.

6. The theme designation unit When the user specifies a theme or mood, the generative AI provides real-time feedback to help select the optimal theme or mood.

2. The system of claim 1.

7. The theme designation unit Analyzes facial expressions and tone of voice when users input a theme or mood to identify a more accurate theme or mood.

2. The system of claim 1.

8. The lyrics generation unit Generate personalized lyrics based on the user's past song preferences and playlists 2. The system of claim 1.

Images