system

Abstract

The system according to the embodiment is intended for a singer to complete a piece of music alone.SOLUTION: A system according to an embodiment includes an information acquisition unit, a lyrics unit, a composition unit, and a singing unit. The information acquisition unit acquires information such as a conversation, a diary, and SNS. The lyrics unit generates lyrics based on the information acquired by the information acquisition unit. The composition unit composes a melody based on the lyrics generated by the lyric unit. The singing unit allows the user to sing into the melody composed by the composition 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 requires a division of labor between the composer and the singer, making it difficult for the singer to complete a song on their own.

[0005] The system according to the embodiment aims to allow a singer to complete a song independently. [Means for solving the problem]

[0006] The system according to the embodiment includes an information acquisition unit, a lyrics unit, a composition unit, and a singing unit. The information acquisition unit acquires information such as conversations, diaries, and social media. The lyrics unit generates lyrics based on the information acquired by the information acquisition unit. The composition unit composes a melody based on the lyrics generated by the lyrics unit. The singing unit allows a user to sing to the melody composed by the composition unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a singer to complete a song by himself. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A music production system according to an embodiment of the present invention allows singers to complete a song independently. In this music production system, a generation AI writes lyrics based on information from conversations, diaries, social media, etc., composes music to match the content, and finally, a human records the vocals to complete the song. This allows singers to create and complete a song independently. For example, a user might write about everyday events in a diary, and the generation AI might create lyrics based on the lyrics, which would then be combined with a melody to match the lyrics. Finally, the user records the vocals in their own voice to complete an original song. This system allows singers to freely create music without worrying about copyright restrictions.

[0029] A music production system according to an embodiment includes an information acquisition unit, a lyricist, a composer, and a singer. The information acquisition unit acquires information such as conversations, diaries, and social media. For example, the information acquisition unit acquires text data entered by a user. The information acquisition unit can also acquire audio data. For example, it acquires audio memos recorded by a user. The information acquisition unit can also acquire image data. For example, it acquires images posted by a user to a social media site. The lyricist generates lyrics based on the information acquired by the information acquisition unit. For example, the lyricist uses a generation AI to generate lyrics based on emotions and events written by a user in a diary. The lyricist can also use the generation AI to generate lyrics based on content posted by a user to a social media site. The lyricist can also use the generation AI to generate lyrics based on conversations the user has with friends. The composer composes a melody based on the lyrics generated by the lyricist. For example, the composer uses the generation AI to compose a melody that enhances emotions to match moving lyrics. The composer can also use the generation AI to compose a fun melody to match fun lyrics. The composition unit can also use the generation AI to compose a humorous melody to match humorous lyrics. The singing unit allows a user to record a melody composed by the composition unit. For example, the singing unit allows a user to record a song at home using recording equipment and incorporate the audio data into a song. The singing unit can also allow a user to record a song using a smartphone and incorporate the audio data into a song. The singing unit can also allow a user to record a song in a studio using professional recording equipment and incorporate the audio data into a song. This allows the music production system according to the embodiment to allow singers to independently create and complete songs. For example, a user can write about daily events in a diary, and the generation AI can create lyrics based on those, and then the generation AI can compose a melody to match those lyrics. Finally, the user can record the song in their own voice to complete an original song. This system allows singers to freely create songs without worrying about copyright restrictions.

[0030] The lyric writing unit can study a user's past posts and conversation history to generate personalized lyrics for each individual user. For example, the generation AI of the lyric writing unit learns the content of a user's past social media posts and diary entries to understand the user's preferences and emotional patterns. For example, if a user has posted many positive things in the past, the generation AI will generate positive lyrics. The lyric writing unit also analyzes the user's conversation history to generate personalized lyrics based on specific themes and keywords. For example, it creates lyrics based on themes that the user frequently talks about (e.g., love, friendship). The lyric writing unit also learns the user's emotional tendencies based on past posts and conversation history and generates lyrics accordingly. For example, if a user has posted many sad things in the past, the generation AI will generate lyrics that empathize with those emotions. This allows the unit to generate lyrics that are optimal for the user.

[0031] The lyric writing unit can analyze a user's voice memos and video logs and generate lyrics based on information obtained from the audio and video. For example, the lyric writing unit uses a generation AI to analyze a user's voice memos and generate lyrics based on their content. For example, it converts what the user says in the voice memos into text and creates lyrics based on that text. The lyric writing unit can also analyze video logs and generate lyrics based on information obtained from the video. For example, it can analyze what the user says in the video log and the scenes shown in the video and create lyrics based on that. The lyric writing unit can also analyze emotions from the voice memos and video logs and generate lyrics based on those emotions. For example, if the user is telling a moving story in the voice memo, it can generate lyrics that empathize with those emotions. This allows lyrics to be generated based on information obtained from audio and video.

[0032] The lyric writing unit can analyze information in different languages ​​and generate lyrics in multiple languages. For example, the generation AI analyzes social media posts and diaries in different languages ​​to generate lyrics in multiple languages. For example, it analyzes posts in English and Japanese and creates lyrics in both languages. The lyric writing unit also analyzes conversation history in different languages ​​to generate lyrics in multiple languages. For example, if a user is conversing in English and Spanish, it creates lyrics in multiple languages ​​based on the content of that conversation. The lyric writing unit also performs emotion analysis in multiple languages ​​and generates lyrics based on emotion data in different languages. For example, it analyzes emotion data in English and French and generates emotional lyrics in both languages. This allows lyrics to be generated in multiple languages.

[0033] The composition unit can analyze the emotional nuances of the lyrics and automatically select the musical genre and instrumentation that best suits them. For example, the composition unit uses a generative AI to analyze the emotional nuances of the lyrics and select the musical genre that best suits them. For example, it may select a ballad for moving lyrics and pop for happy lyrics. The composition unit also automatically selects the optimal instrumentation based on the emotion of the lyrics. For example, it may select piano and string instruments for sad lyrics and guitar and drums for happy lyrics. The composition unit also uses emotional analysis to select the musical style that best suits the tone and theme of the lyrics. For example, it may select acoustic for romantic lyrics and rock for energetic lyrics. This allows it to select the musical genre and instrumentation that best suits the emotion of the lyrics.

[0034] The composition unit can incorporate musical styles from different cultural spheres to generate diverse melodies from a global perspective. For example, the generation AI in the composition unit learns musical styles from different cultural spheres and generates melodies that incorporate them. For example, it could incorporate African rhythms and Asian melody patterns. The composition unit can also generate melodies that fuse diverse musical styles from a global perspective. For example, it could create a melody that fuses Latin music with jazz. The composition unit can also combine musical elements from different cultural spheres to generate unique melodies. For example, it could create a melody that combines the Indian sitar with the Western piano. This makes it possible to generate diverse melodies that incorporate musical styles from different cultural spheres.

[0035] The composition unit can analyze the user's physical movements and generate a rhythm and tempo that matches those movements. For example, the composition unit uses a generation AI to analyze the user's dance movements and generate a rhythm and tempo that is optimal for those movements. For example, if the user is dancing at a fast tempo, the composition unit generates a fast rhythm that matches those movements. The composition unit also builds a system that analyzes the user's physical movements in real time and generates music that matches those movements. For example, if the user is moving slowly, the composition unit generates music with a slow tempo that matches those movements. The composition unit also develops a system that dynamically adjusts the rhythm and tempo based on the dance movements. For example, the rhythm and tempo are adjusted each time the user changes their movements. This makes it possible to generate a rhythm and tempo that matches the user's physical movements.

[0036] The singing unit can analyze the user's voice quality and singing style and automatically apply the most suitable sound effects. For example, the singing unit uses a generation AI to analyze the user's voice quality and automatically apply the most suitable sound effects. For example, if the user's voice is in a high range, it applies echo and reverb that are suitable for high frequencies. The singing unit also analyzes the user's singing style and automatically applies the most suitable effects. For example, if the user sings in a rock style, it applies distortion and chorus effects. The singing unit also builds a system that applies the optimal sound effects in real time based on the user's voice quality and singing style. For example, it applies the optimal effects on the spot as the user sings. This allows the application of the optimal sound effects and effects for the user's voice quality and singing style.

[0037] The singing unit can learn the user's singing data and provide technical advice and feedback on singing in real time. For example, the singing unit uses a generative AI to learn the user's singing data and provide technical advice in real time. For example, it points out discrepancies in pitch or rhythm and suggests ways to correct them. The singing unit also analyzes the user's singing data and provides feedback in real time that is useful for improving singing technique. For example, it points out areas for improvement in vocal technique or breath control. The singing unit also builds a system that evaluates the user's singing technique based on the singing data and provides specific areas for improvement in real time. For example, it displays areas for pitch or rhythm correction while singing. This makes it possible to provide advice and feedback to improve the user's singing technique in real time.

[0038] The singing club can share a user's singing data with other users and generate collaborative songs. For example, the singing club builds a system in which a generation AI shares a user's singing data with other users and generates collaborative songs. For example, multiple users sing their own parts and combine them into one song. The singing club also shares users' singing data and creates songs collaboratively with other users. For example, different users play different instrument parts and combine them into one song. The singing club also develops a platform for generating collaborative songs, allowing users to easily create songs collaboratively with other users. For example, it provides a function that allows online real-time collaboration. This enables users to collaborate on song creation.

[0039] The singing unit can analyze a user's singing data and automatically generate remix versions with different instruments and musical styles. For example, the singing unit uses a generation AI to analyze a user's singing data and automatically generate remix versions with different instruments and musical styles. For example, pop singing data is remixed into a jazz style. The singing unit also builds a system that generates remix versions in different musical styles based on the user's singing data. For example, rock singing data is remixed into a classical style. The singing unit also develops an algorithm for automatically generating remix versions, allowing users to easily enjoy songs in different musical styles. For example, a remix is ​​generated to match the musical style selected by the user. This makes it possible to automatically generate remix versions with different instruments and musical styles.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The music production system can also analyze the user's physical movements and generate rhythms and tempos that match those movements. For example, if the user is dancing at a fast tempo, a fast rhythm that matches those movements can be generated. Conversely, if the user is moving slowly, music with a slow tempo that matches those movements can be generated. Furthermore, a system can be developed that dynamically adjusts the rhythm and tempo based on the user's movements. This allows the generation of rhythms and tempos that match the user's physical movements.

[0042] The lyric writing unit can study a user's past posts and conversation history to generate lyrics personalized for each individual user. For example, the generation AI can study the content of a user's past social media posts and diary entries to understand the user's preferences and emotional patterns. If a user has posted many positive things in the past, the generation AI can generate positive lyrics. It can also analyze a user's conversation history to generate personalized lyrics based on specific themes or keywords. Furthermore, it can learn a user's emotional tendencies based on past posts and conversation history and generate lyrics accordingly. This allows it to generate lyrics that are optimal for each user.

[0043] The lyric writing unit can analyze a user's voice memos and video logs and generate lyrics based on information obtained from the audio and video. For example, the generation AI can analyze a user's voice memos and generate lyrics based on their content. It can also convert what the user says in the voice memos into text and create lyrics based on that text. It can also analyze video logs and generate lyrics based on information obtained from the video. It can also analyze emotions from voice memos and video logs and generate lyrics based on those emotions. This makes it possible to generate lyrics based on information obtained from audio and video.

[0044] The lyric writing section can analyze information in different languages ​​and generate lyrics in multiple languages. For example, the generation AI can analyze social media posts and diaries in different languages ​​and generate lyrics in multiple languages. It can also analyze posts in English and Japanese and create lyrics in both languages. It can also analyze conversation history in different languages ​​and generate lyrics in multiple languages. It can also perform emotion analysis in multiple languages ​​and generate lyrics based on emotion data in different languages. This allows it to generate lyrics in multiple languages.

[0045] The composition department can analyze the emotional nuances of lyrics and automatically select the most appropriate musical genre and instrumentation. For example, generative AI can analyze the emotional nuances of lyrics and select the most appropriate musical genre. It can select a ballad for moving lyrics and pop for happy lyrics. It can also automatically select the optimal instrumentation based on the emotion of the lyrics. Furthermore, it can use emotional analysis to select the musical style that best suits the tone and theme of the lyrics. This allows it to select the musical genre and instrumentation that best matches the emotion of the lyrics.

[0046] The composition unit can incorporate musical styles from different cultural spheres to generate diverse melodies from a global perspective. For example, the generation AI can learn musical styles from different cultural spheres and generate melodies that incorporate them. It can also incorporate African rhythms and Asian melody patterns. It can also generate melodies that fuse diverse musical styles from a global perspective. It can also combine musical elements from different cultural spheres to generate unique melodies. This allows it to generate diverse melodies that incorporate musical styles from different cultural spheres.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The information acquisition unit acquires information such as conversations, diaries, and social media. For example, it acquires text data entered by the user, recorded voice memos, and images posted to social media. Step 2: The lyrics writer generates lyrics based on the information acquired by the information acquisition unit. For example, using a generation AI, lyrics are generated based on the user's diary, social media posts, conversations with friends, etc. Step 3: The composer composes a melody based on the lyrics generated by the lyricist. For example, using generative AI, they can compose an emotional melody to match moving lyrics, a fun melody to match happy lyrics, or a humorous melody to match humorous lyrics. Step 4: In the vocal section, the user sings the melody composed by the composition section. For example, the user can record their singing at home using recording equipment and incorporate the audio data into the song. Recording can also be done using a smartphone or in a studio.

[0049] (Example 2) A music production system according to an embodiment of the present invention allows singers to complete a song independently. In this music production system, a generation AI writes lyrics based on information from conversations, diaries, social media, etc., composes music to match the content, and finally, a human records the vocals to complete the song. This allows singers to create and complete a song independently. For example, a user might write about everyday events in a diary, and the generation AI might create lyrics based on the lyrics, which would then be combined with a melody to match the lyrics. Finally, the user records the vocals in their own voice to complete an original song. This system allows singers to freely create music without worrying about copyright restrictions.

[0050] A music production system according to an embodiment includes an information acquisition unit, a lyricist, a composer, and a singer. The information acquisition unit acquires information such as conversations, diaries, and social media. For example, the information acquisition unit acquires text data entered by a user. The information acquisition unit can also acquire audio data. For example, it acquires audio memos recorded by a user. The information acquisition unit can also acquire image data. For example, it acquires images posted by a user to a social media site. The lyricist generates lyrics based on the information acquired by the information acquisition unit. For example, the lyricist uses a generation AI to generate lyrics based on emotions and events written by a user in a diary. The lyricist can also use the generation AI to generate lyrics based on content posted by a user to a social media site. The lyricist can also use the generation AI to generate lyrics based on conversations the user has with friends. The composer composes a melody based on the lyrics generated by the lyricist. For example, the composer uses the generation AI to compose a melody that enhances emotions to match moving lyrics. The composer can also use the generation AI to compose a fun melody to match fun lyrics. The composition unit can also use the generation AI to compose a humorous melody to match humorous lyrics. The singing unit allows a user to record a melody composed by the composition unit. For example, the singing unit allows a user to record a song at home using recording equipment and incorporate the audio data into a song. The singing unit can also allow a user to record a song using a smartphone and incorporate the audio data into a song. The singing unit can also allow a user to record a song in a studio using professional recording equipment and incorporate the audio data into a song. This allows the music production system according to the embodiment to allow singers to independently create and complete songs. For example, a user can write about daily events in a diary, and the generation AI can create lyrics based on those, and then the generation AI can compose a melody to match those lyrics. Finally, the user can record the song in their own voice to complete an original song. This system allows singers to freely create songs without worrying about copyright restrictions.

[0051] The lyricist can analyze the user's emotions in real time and dynamically adjust the tone and theme of the lyrics according to changes in emotion. For example, the lyricist uses a generation AI to analyze the user's input data (conversations, diary entries, and social media posts) in real time to detect changes in emotion. For example, if a user posts about a sad event, the generation AI generates lyrics that express sadness to match that emotion. The lyricist also tracks the user's emotional changes in real time and dynamically adjusts the tone and theme of the lyrics. For example, if a user writes about joy in their diary entry and then sadness, the generation AI will change the tone of the lyrics according to the change in emotion. The lyricist also uses emotion analysis to detect peaks and declines in the user's emotions and dynamically generate climaxes and calmer parts of the lyrics based on that. For example, it generates lyrics that express strong emotions when emotions are at their peak and calmer lyrics when emotions have calmed down. This allows the system to generate lyrics that correspond to the user's emotions.

[0052] The lyric writing unit can study a user's past posts and conversation history to generate personalized lyrics for each individual user. For example, the generation AI of the lyric writing unit learns the content of a user's past social media posts and diary entries to understand the user's preferences and emotional patterns. For example, if a user has posted many positive things in the past, the generation AI will generate positive lyrics. The lyric writing unit also analyzes the user's conversation history to generate personalized lyrics based on specific themes and keywords. For example, it creates lyrics based on themes that the user frequently talks about (e.g., love, friendship). The lyric writing unit also learns the user's emotional tendencies based on past posts and conversation history and generates lyrics accordingly. For example, if a user has posted many sad things in the past, the generation AI will generate lyrics that empathize with those emotions. This allows the unit to generate lyrics that are optimal for the user.

[0053] The lyric writing unit can use the emotion estimation function to estimate the user's emotions and generate lyrics that most empathize with those emotions. For example, the lyric writing unit uses the emotion estimation function to estimate emotions from user input data and generate lyrics that most empathize with those emotions. For example, if a user posts about a sad event, the generation AI generates lyrics that empathize with that emotion. The lyric writing unit also estimates the user's emotions in real time and adjusts the tone and theme of the lyrics based on that emotion. For example, if the user is feeling happy, the generation AI generates positive lyrics that empathize with that emotion. The lyric writing unit also builds a system that generates lyrics that most empathize with the user's emotions based on the emotion estimation data. For example, if the user's emotion score is high, lyrics that empathize with that emotion are generated. This makes it possible to generate lyrics that empathize with the user's emotions.

[0054] The lyric writing unit can analyze a user's voice memos and video logs and generate lyrics based on information obtained from the audio and video. For example, the lyric writing unit uses a generation AI to analyze a user's voice memos and generate lyrics based on their content. For example, it converts what the user says in the voice memos into text and creates lyrics based on that text. The lyric writing unit can also analyze video logs and generate lyrics based on information obtained from the video. For example, it can analyze what the user says in the video log and the scenes shown in the video and create lyrics based on that. The lyric writing unit can also analyze emotions from the voice memos and video logs and generate lyrics based on those emotions. For example, if the user is telling a moving story in the voice memo, it can generate lyrics that empathize with those emotions. This allows lyrics to be generated based on information obtained from audio and video.

[0055] The lyric writing unit can analyze information in different languages ​​and generate lyrics in multiple languages. For example, the generation AI analyzes social media posts and diaries in different languages ​​to generate lyrics in multiple languages. For example, it analyzes posts in English and Japanese and creates lyrics in both languages. The lyric writing unit also analyzes conversation history in different languages ​​to generate lyrics in multiple languages. For example, if a user is conversing in English and Spanish, it creates lyrics in multiple languages ​​based on the content of that conversation. The lyric writing unit also performs emotion analysis in multiple languages ​​and generates lyrics based on emotion data in different languages. For example, it analyzes emotion data in English and French and generates emotional lyrics in both languages. This allows lyrics to be generated in multiple languages.

[0056] The lyric writing unit can use the emotion estimation function to estimate the emotion a user is feeling when entering lyrics in real time and make suggestions that will elicit positive emotions. For example, the lyric writing unit can use the emotion estimation function to estimate the emotion a user is feeling when entering lyrics in real time and make suggestions that will elicit positive emotions. For example, if the user has negative emotions, it presents examples of positive lyrics. The lyric writing unit also analyzes the user's emotions in real time and provides an interface for eliciting positive emotions. For example, if the user enters sad lyrics, it displays an encouraging message. The lyric writing unit also builds a system that suggests lyrics that will elicit positive emotions in the user based on the emotion estimation data. For example, if the user has negative emotions, it presents samples of positive lyrics. This makes it possible to generate lyrics that will elicit positive emotions in the user.

[0057] The composition unit can analyze the emotional nuances of the lyrics and automatically select the musical genre and instrumentation that best suits them. For example, the composition unit uses a generative AI to analyze the emotional nuances of the lyrics and select the musical genre that best suits them. For example, it may select a ballad for moving lyrics and pop for happy lyrics. The composition unit also automatically selects the optimal instrumentation based on the emotion of the lyrics. For example, it may select piano and string instruments for sad lyrics and guitar and drums for happy lyrics. The composition unit also uses emotional analysis to select the musical style that best suits the tone and theme of the lyrics. For example, it may select acoustic for romantic lyrics and rock for energetic lyrics. This allows it to select the musical genre and instrumentation that best suits the emotion of the lyrics.

[0058] The composition unit can use the emotion estimation function to generate a melody that best sympathizes with the emotion of the lyrics. For example, the composition unit uses the emotion estimation function to analyze the emotion of the lyrics and generate a melody that best sympathizes with that emotion. For example, it generates a melancholic melody for sad lyrics and a cheerful melody for happy lyrics. The composition unit also builds a system that generates optimal melodies based on the emotion score of the lyrics. For example, it generates a melody that expresses strong emotions for parts with a high emotion score. The composition unit also generates melodies that sympathize with the emotion of the lyrics in real time based on the emotion estimation data. For example, when a user inputs lyrics, it instantly generates a melody that matches the emotion. This makes it possible to generate melodies that sympathize with the emotion of the lyrics.

[0059] The composition unit can incorporate musical styles from different cultural spheres to generate diverse melodies from a global perspective. For example, the generation AI in the composition unit learns musical styles from different cultural spheres and generates melodies that incorporate them. For example, it could incorporate African rhythms and Asian melody patterns. The composition unit can also generate melodies that fuse diverse musical styles from a global perspective. For example, it could create a melody that fuses Latin music with jazz. The composition unit can also combine musical elements from different cultural spheres to generate unique melodies. For example, it could create a melody that combines the Indian sitar with the Western piano. This makes it possible to generate diverse melodies that incorporate musical styles from different cultural spheres.

[0060] The composition unit can analyze the user's physical movements and generate a rhythm and tempo that matches those movements. For example, the composition unit uses a generation AI to analyze the user's dance movements and generate a rhythm and tempo that is optimal for those movements. For example, if the user is dancing at a fast tempo, the composition unit generates a fast rhythm that matches those movements. The composition unit also builds a system that analyzes the user's physical movements in real time and generates music that matches those movements. For example, if the user is moving slowly, the composition unit generates music with a slow tempo that matches those movements. The composition unit also develops a system that dynamically adjusts the rhythm and tempo based on the dance movements. For example, the rhythm and tempo are adjusted each time the user changes their movements. This makes it possible to generate a rhythm and tempo that matches the user's physical movements.

[0061] The composition unit can use the emotion estimation function to estimate the emotion a user is feeling when inputting a melody in real time and make suggestions that will elicit positive emotions. For example, the composition unit can use the emotion estimation function to estimate the emotion a user is feeling when inputting a melody in real time and make suggestions that will elicit positive emotions. For example, if the user has negative emotions, it presents examples of positive melodies. The composition unit also analyzes the user's emotions in real time and provides an interface for eliciting positive emotions. For example, if the user is inputting a sad melody, it displays an encouraging message. The composition unit also builds a system that suggests melodies that will elicit positive emotions in the user based on the emotion estimation data. For example, if the user has negative emotions, it presents samples of positive melodies. This makes it possible to generate melodies that elicit positive emotions in the user.

[0062] The singing unit can analyze the user's voice quality and singing style and automatically apply the most suitable sound effects. For example, the singing unit uses a generation AI to analyze the user's voice quality and automatically apply the most suitable sound effects. For example, if the user's voice is in a high range, it applies echo and reverb that are suitable for high frequencies. The singing unit also analyzes the user's singing style and automatically applies the most suitable effects. For example, if the user sings in a rock style, it applies distortion and chorus effects. The singing unit also builds a system that applies the optimal sound effects in real time based on the user's voice quality and singing style. For example, it applies the optimal effects on the spot as the user sings. This allows the application of the optimal sound effects and effects for the user's voice quality and singing style.

[0063] The singing unit can learn the user's singing data and provide technical advice and feedback on singing in real time. For example, the singing unit uses a generative AI to learn the user's singing data and provide technical advice in real time. For example, it points out discrepancies in pitch or rhythm and suggests ways to correct them. The singing unit also analyzes the user's singing data and provides feedback in real time that is useful for improving singing technique. For example, it points out areas for improvement in vocal technique or breath control. The singing unit also builds a system that evaluates the user's singing technique based on the singing data and provides specific areas for improvement in real time. For example, it displays areas for pitch or rhythm correction while singing. This makes it possible to provide advice and feedback to improve the user's singing technique in real time.

[0064] The singing unit can use the emotion estimation function to analyze the user's emotions while singing in real time and make suggestions to enhance emotional expression. The singing unit, for example, uses the emotion estimation function to analyze the user's emotions while singing in real time and make suggestions to enhance emotional expression. For example, it provides advice to add emotion to parts where emotion is weak. The singing unit also analyzes the user's emotions while singing and makes specific suggestions to enhance emotional expression in real time. For example, it suggests a vocal technique to add emotion to a specific phrase. The singing unit also builds a system that provides feedback to enhance emotional expression of the user while singing in real time based on the emotion estimation data. For example, it displays specific advice to add emotion to parts where emotion is weak. This makes it possible to provide suggestions to enhance emotional expression of the user while singing in real time.

[0065] The singing club can share a user's singing data with other users and generate collaborative songs. For example, the singing club builds a system in which a generation AI shares a user's singing data with other users and generates collaborative songs. For example, multiple users sing their own parts and combine them into one song. The singing club also shares users' singing data and creates songs collaboratively with other users. For example, different users play different instrument parts and combine them into one song. The singing club also develops a platform for generating collaborative songs, allowing users to easily create songs collaboratively with other users. For example, it provides a function that allows online real-time collaboration. This enables users to collaborate on song creation.

[0066] The singing unit can analyze a user's singing data and automatically generate remix versions with different instruments and musical styles. For example, the singing unit uses a generation AI to analyze a user's singing data and automatically generate remix versions with different instruments and musical styles. For example, pop singing data is remixed into a jazz style. The singing unit also builds a system that generates remix versions in different musical styles based on the user's singing data. For example, rock singing data is remixed into a classical style. The singing unit also develops an algorithm for automatically generating remix versions, allowing users to easily enjoy songs in different musical styles. For example, a remix is ​​generated to match the musical style selected by the user. This makes it possible to automatically generate remix versions with different instruments and musical styles.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The music production system can also analyze the user's physical movements and generate rhythms and tempos that match those movements. For example, if the user is dancing at a fast tempo, a fast rhythm that matches those movements can be generated. Conversely, if the user is moving slowly, music with a slow tempo that matches those movements can be generated. Furthermore, a system can be developed that dynamically adjusts the rhythm and tempo based on the user's movements. This allows the generation of rhythms and tempos that match the user's physical movements.

[0069] The lyric writing unit can analyze the user's emotions in real time and dynamically adjust the tone and theme of the lyrics according to changes in emotion. For example, if a user posts about a sad event, the generation AI will generate lyrics that express sadness to match that emotion. Also, if a user writes about joy in their diary and then sadness, the generation AI can change the tone of the lyrics according to the change in emotion. Furthermore, it can detect peaks and declines in emotion and dynamically generate climaxes and calm parts of the lyrics based on that. This allows it to generate lyrics that correspond to the user's emotions.

[0070] The lyric writing unit can study a user's past posts and conversation history to generate lyrics personalized for each individual user. For example, the generation AI can study the content of a user's past social media posts and diary entries to understand the user's preferences and emotional patterns. If a user has posted many positive things in the past, the generation AI can generate positive lyrics. It can also analyze a user's conversation history to generate personalized lyrics based on specific themes or keywords. Furthermore, it can learn a user's emotional tendencies based on past posts and conversation history and generate lyrics accordingly. This allows it to generate lyrics that are optimal for each user.

[0071] The lyric writing department can use the emotion estimation function to estimate the user's emotions and generate lyrics that best empathize with those emotions. For example, if a user posts about a sad event, the generation AI can generate lyrics that empathize with those emotions. It can also estimate the user's emotions in real time and adjust the tone and theme of the lyrics based on those emotions. Furthermore, it can build a system that generates lyrics that best empathize with the user's emotions based on the emotion estimation data. This makes it possible to generate lyrics that empathize with the user's emotions.

[0072] The lyric writing unit can analyze a user's voice memos and video logs and generate lyrics based on information obtained from the audio and video. For example, the generation AI can analyze a user's voice memos and generate lyrics based on their content. It can also convert what the user says in the voice memos into text and create lyrics based on that text. It can also analyze video logs and generate lyrics based on information obtained from the video. It can also analyze emotions from voice memos and video logs and generate lyrics based on those emotions. This makes it possible to generate lyrics based on information obtained from audio and video.

[0073] The lyric writing section can analyze information in different languages ​​and generate lyrics in multiple languages. For example, the generation AI can analyze social media posts and diaries in different languages ​​and generate lyrics in multiple languages. It can also analyze posts in English and Japanese and create lyrics in both languages. It can also analyze conversation history in different languages ​​and generate lyrics in multiple languages. It can also perform emotion analysis in multiple languages ​​and generate lyrics based on emotion data in different languages. This allows it to generate lyrics in multiple languages.

[0074] The lyrics writing unit can use the emotion estimation function to estimate the user's emotions in real time when they enter lyrics and make suggestions that will elicit positive emotions. For example, if the user has negative emotions, it can present examples of positive lyrics. It can also analyze the user's emotions in real time and provide an interface for eliciting positive emotions. Furthermore, it can build a system that suggests lyrics that will elicit positive emotions in the user based on the emotion estimation data. This makes it possible to generate lyrics that will elicit positive emotions in the user.

[0075] The composition department can analyze the emotional nuances of lyrics and automatically select the most appropriate musical genre and instrumentation. For example, generative AI can analyze the emotional nuances of lyrics and select the most appropriate musical genre. It can select a ballad for moving lyrics and pop for happy lyrics. It can also automatically select the optimal instrumentation based on the emotion of the lyrics. Furthermore, it can use emotional analysis to select the musical style that best suits the tone and theme of the lyrics. This allows it to select the musical genre and instrumentation that best matches the emotion of the lyrics.

[0076] The composition unit can incorporate musical styles from different cultural spheres to generate diverse melodies from a global perspective. For example, the generation AI can learn musical styles from different cultural spheres and generate melodies that incorporate them. It can also incorporate African rhythms and Asian melody patterns. It can also generate melodies that fuse diverse musical styles from a global perspective. It can also combine musical elements from different cultural spheres to generate unique melodies. This allows it to generate diverse melodies that incorporate musical styles from different cultural spheres.

[0077] The composition unit can use the emotion estimation function to generate a melody that best sympathizes with the emotion of the lyrics. For example, the emotion estimation function can be used to analyze the emotion of the lyrics and generate a melody that best sympathizes with that emotion. It is possible to generate a melancholic melody for sad lyrics and a cheerful melody for happy lyrics. It is also possible to build a system that generates optimal melodies based on the emotion score of lyrics. Furthermore, it is possible to generate melodies that sympathize with the emotion of the lyrics in real time based on the emotion estimation data. This makes it possible to generate melodies that sympathize with the emotion of the lyrics.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The information acquisition unit acquires information such as conversations, diaries, and social media. For example, it acquires text data entered by the user, recorded voice memos, and images posted to social media. Step 2: The lyrics writer generates lyrics based on the information acquired by the information acquisition unit. For example, using a generation AI, lyrics are generated based on the user's diary, social media posts, conversations with friends, etc. Step 3: The composer composes a melody based on the lyrics generated by the lyricist. For example, using generative AI, they can compose an emotional melody to match moving lyrics, a fun melody to match happy lyrics, or a humorous melody to match humorous lyrics. Step 4: In the vocal section, the user sings the melody composed by the composition section. For example, the user can record their singing at home using recording equipment and incorporate the audio data into the song. Recording can also be done using a smartphone or in a studio.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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]

[0147] 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. An information acquisition unit that acquires information such as conversations, diaries, and social media, and a lyrics writing unit that generates lyrics based on the information acquired by the information acquisition unit; a composition unit that composes a melody based on the lyrics generated by the lyrics unit; a singing section in which a user sings to the melody composed by the composition section. A system characterized by:

2. The lyrics section is Analyzing the user's emotions in real time and dynamically adjusting the tone and theme of the lyrics according to changes in emotions.

2. The system of claim 1.

3. The lyrics section is It learns the user's past posts and conversation history and generates lyrics personalized for each individual user.

2. The system of claim 1.

4. The lyrics section is Estimate the user's emotions and generate lyrics that best empathize with those emotions.

2. The system of claim 1.

5. The lyrics section is Analyzes the user's voice memos and video logs and generates lyrics based on information obtained from the audio and video.

2. The system of claim 1.

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