System

Abstract

An object of the system according to the embodiment is to enable a user to easily create and publish a video that the user "sings".SOLUTION: A system according to an embodiment includes a generation AI, a user interface, and an upload unit. The generation AI automatically composes lyrics in response to an instruction from the user. The user interface allows a user to preview a musical piece generated by the generation AI. The upload unit uploads the moving picture sung by the user to the WEB through the user interface.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 required a division of labor between the composer and the singer, and strict copyright regulations made it difficult to easily create and publish "cover songs" videos.

[0005] The system according to the embodiment aims to enable users to easily create and publish "sing-along" videos. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a user interface, and an upload unit. The generation AI automatically composes lyrics and music in response to instructions from a user. The user interface allows the user to preview the music generated by the generation AI. The upload unit uploads a video of the user singing to the web via the user interface. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily create and publish "sing-along" videos. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The automatic songwriting system according to an embodiment of the present invention uses AI technology to automate songwriting and allows singers to easily create and upload "cover songs" videos. This allows singers to create their own original songs and freely publish the videos.

[0029] An automatic lyric and composition system according to an embodiment includes a generation AI, a user interface, and an upload unit. The generation AI automatically composes lyrics and music in response to user instructions. For example, when a user inputs a prompt such as "Please create a song with an upbeat atmosphere," the generation AI generates lyrics and a melody based on the user's instructions. The generation AI can also generate music using a text generation AI (e.g., LLM) or a multimodal generation AI. The user interface provides a function for a user to preview music generated by the generation AI. For example, the user interface may include a text box for inputting a prompt and a play button for previewing the generated music. The upload unit uploads a video of the user singing to the web through the user interface. For example, the upload unit has a function for uploading video files to a specified platform. This allows the automatic lyric and composition system according to an embodiment to easily create and upload "cover songs" videos.

[0030] Generative AI can analyze a user's past music history and compose lyrics and music that match the user's preferences. For example, generative AI can analyze the user's previously created songs and playlists to learn the user's musical preferences. For example, it can extract specific genres, tempos, lyric themes, etc., and generate new songs based on them. Generative AI can also create songs that match the user's preferences based on songs that the user has previously rated highly or frequently played. For example, it can incorporate the user's favorite melody lines and rhythm patterns. Generative AI can also generate songs that imitate the style of a specific artist or band. For example, it can create songs that incorporate characteristic elements of the user's favorite artists. This allows it to generate songs that match the user's preferences.

[0031] The generation AI can learn the user's voice quality and singing style and generate music that is optimized for that. For example, the generation AI can analyze the user's voice quality and generate music that takes into account the key and range that is best suited to that voice. For example, if the user's voice is strong in the high range, it can create a melody that makes use of the high notes. The generation AI can also learn the user's singing style and generate music that incorporates rhythms and phrases that match that. For example, if the user is good at rapping, it can create rhythmic lyrics and beats. The generation AI can also generate music that reflects the user's vocal characteristics based on the user's past singing data. For example, it can create a melody line that makes use of the user's vocal tone and vibrato. This allows it to generate music that is optimized for the user's voice quality and singing style.

[0032] Generative AI can generate music that incorporates musical styles from different genres and cultural spheres. For example, generative AI can learn the characteristics of different musical genres and generate music that fuses them together. For example, it can create a song that combines jazz and hip-hop. Generative AI can also generate music that incorporates musical styles from different cultural spheres. For example, it can create a song that fuses African rhythms with European melodies. Generative AI can also generate music that incorporates elements from different genres and cultural spheres based on the user's preferences. For example, if a user likes electronica and traditional folk music, it can create a song that fuses the two. This makes it possible to generate music that incorporates musical styles from different genres and cultural spheres.

[0033] The generation AI can generate music based on poems and phrases entered by the user. For example, the generation AI creates a melody and accompaniment that matches the content of the poem or phrase entered by the user. For example, if the poem entered by the user has a love theme, the generation AI will generate a romantic melody. The generation AI also generates music that uses the phrase entered by the user repeatedly. For example, if the user enters the phrase "I love you," the generation AI will create a song that incorporates that phrase into the chorus. The generation AI also analyzes the poem or phrase entered by the user and generates a music composition that is optimal for the content. For example, if the poem entered by the user is in the form of a story, the generation AI will create a song that follows that story. This allows music to be generated based on the poem or phrase entered by the user.

[0034] The uploading unit uses a generation AI to automatically generate thumbnails for videos when they are uploaded, attracting viewers' interest. For example, the uploading unit uses a generation AI to analyze the content of the video and automatically generate thumbnails for the most visually appealing scenes. For example, it may select scenes in which the user is singing or scenes that express emotions. The uploading unit also adds elements to the video thumbnails that attract viewers' interest. For example, it may insert catchy text or graphics into the thumbnail. The uploading unit also uses a generation AI to generate multiple thumbnail candidates based on the content of the video, allowing the user to select the most suitable one. For example, it may suggest thumbnails that combine different scenes and text. This allows the automatic generation of thumbnails that attract viewers' interest.

[0035] The uploading unit uses a generation AI to automatically generate video titles and descriptions when uploading videos, enabling SEO measures. For example, the generation AI analyzes the content of the video and automatically generates titles and descriptions containing optimal keywords. For example, it creates a title that reflects the theme and characteristics of the video. The uploading unit also generates catchphrases and descriptions that will attract viewers' interest based on the content of the video. For example, it emphasizes the highlights and unique points of the video. The generation AI also takes SEO measures into consideration when generating titles and descriptions that will improve the video's ranking in search engines. For example, it incorporates popular keywords and phrases. This allows for the automatic generation of titles and descriptions that take SEO measures into account.

[0036] In the uploading section, the generation AI automatically adds effects and filters to videos when they are uploaded, enhancing their visual appeal. For example, in the uploading section, the generation AI automatically adds optimal effects and filters based on the content of the video. For example, it applies color correction and special effects to specific scenes. In the uploading section, the generation AI also suggests effects and filters that match the theme and atmosphere of the video, allowing the user to select one. For example, it offers retro and modern filters. In the uploading section, the generation AI also adds dynamic effects and animations to enhance the visual appeal of the video. For example, it displays lyrics or incorporates background movement. This allows the automatic addition of effects and filters that enhance visual appeal.

[0037] The uploading unit uses a generation AI to automatically generate subtitles for videos when they are uploaded, helping viewers understand. For example, the uploading unit uses a generation AI to analyze the audio of the video and automatically generate subtitles. For example, it converts lyrics and spoken content into text and displays it in the video. The uploading unit also adjusts the format and display timing of the subtitles based on the content of the video to make them easier for viewers to understand. For example, it changes the font size to emphasize important parts. The uploading unit also uses a generation AI to automatically generate subtitles in multiple languages, making it suitable for international viewers. For example, it provides subtitles in English, Spanish, and other languages. This allows for the automatic generation of subtitles that help viewers understand.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The automatic lyrics and composition system can also monitor the user's physical condition and generate music based on that condition. For example, it can use sensors to acquire the user's heart rate and breathing rhythm and create a relaxing melody and tempo based on that data. It can also generate energetic, fast-paced music when the user is exercising. It can also analyze the user's level of physical fatigue and provide music with a refreshing effect. This allows it to generate music that matches the user's physical condition.

[0040] The automatic lyrics and composition system can also generate music incorporating the user's environmental sounds. For example, the system can incorporate the chirping of birds or the murmuring of a river recorded by the user in nature into a song. It can also create music incorporating the user's surrounding environmental sounds, such as the sounds of a busy city or a cafe. It can also generate music that reflects the atmosphere of a specific location based on sounds recorded by the user in that location. This allows the system to generate music incorporating the user's environmental sounds.

[0041] The automatic lyrics and composition system can also generate music incorporating the user's favorite instruments. For example, if the user likes the guitar, the system can create music that emphasizes guitar riffs and solos. If the user likes the piano, the system can provide music that focuses on piano melodies. Furthermore, if the user likes the tone of a particular instrument, the system can generate music that mainly uses that instrument. This allows the system to generate music incorporating the user's favorite instruments.

[0042] The automatic lyrics and composition system can also suggest new musical styles that the user has not yet tried based on the user's past song history. For example, if the user primarily likes pop music, the system can suggest songs that incorporate elements of jazz and classical music. If the user prefers rock, the system can provide songs that incorporate elements of electronica and hip hop. Furthermore, if the user is biased toward a particular genre, the system can suggest new musical styles that differ from that genre, broadening the user's musical horizons. This allows the system to generate songs in new styles that broaden the user's musical horizons.

[0043] The automatic songwriting system can also generate music that incorporates the style of a user's favorite artists or bands. For example, if a user is a fan of a particular artist, the system can create music that imitates that artist's distinctive sound and rhythm. If a user likes multiple artists, the system can provide music that combines elements of those artists. Furthermore, if a user wants to discover a new artist, the system can suggest music that incorporates that artist's style, broadening the user's musical horizons. This allows the system to generate music that incorporates the style of a user's favorite artists or bands.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The generative AI automatically composes lyrics and music based on user instructions. For example, if a user inputs a prompt such as "Please create a song with an upbeat atmosphere," the AI ​​generates lyrics and a melody based on the user's instructions. The generative AI can also generate music using text generation AI (e.g., LLM) or multimodal generation AI. Step 2: The user interface provides functionality for the user to preview the music generated by the generative AI. For example, the user interface may include a text box for entering prompts and a play button for previewing the generated music. Step 3: The uploading unit uploads the video of the user singing to the web through a user interface. For example, the uploading unit has a function of uploading the video file to a specified platform.

[0046] (Example 2) The automatic songwriting system according to an embodiment of the present invention uses AI technology to automate songwriting and allows singers to easily create and upload "cover songs" videos. This allows singers to create their own original songs and freely publish the videos.

[0047] An automatic lyric and composition system according to an embodiment includes a generation AI, a user interface, and an upload unit. The generation AI automatically composes lyrics and music in response to user instructions. For example, when a user inputs a prompt such as "Please create a song with an upbeat atmosphere," the generation AI generates lyrics and a melody based on the user's instructions. The generation AI can also generate music using a text generation AI (e.g., LLM) or a multimodal generation AI. The user interface provides a function for a user to preview music generated by the generation AI. For example, the user interface may include a text box for inputting a prompt and a play button for previewing the generated music. The upload unit uploads a video of the user singing to the web through the user interface. For example, the upload unit has a function for uploading video files to a specified platform. This allows the automatic lyric and composition system according to an embodiment to easily create and upload "cover songs" videos.

[0048] Generative AI can analyze a user's past music history and compose lyrics and music that match the user's preferences. For example, generative AI can analyze the user's previously created songs and playlists to learn the user's musical preferences. For example, it can extract specific genres, tempos, lyric themes, etc., and generate new songs based on them. Generative AI can also create songs that match the user's preferences based on songs that the user has previously rated highly or frequently played. For example, it can incorporate the user's favorite melody lines and rhythm patterns. Generative AI can also generate songs that imitate the style of a specific artist or band. For example, it can create songs that incorporate characteristic elements of the user's favorite artists. This allows it to generate songs that match the user's preferences.

[0049] The generation AI can learn the user's voice quality and singing style and generate music that is optimized for that. For example, the generation AI can analyze the user's voice quality and generate music that takes into account the key and range that is best suited to that voice. For example, if the user's voice is strong in the high range, it can create a melody that makes use of the high notes. The generation AI can also learn the user's singing style and generate music that incorporates rhythms and phrases that match that. For example, if the user is good at rapping, it can create rhythmic lyrics and beats. The generation AI can also generate music that reflects the user's vocal characteristics based on the user's past singing data. For example, it can create a melody line that makes use of the user's vocal tone and vibrato. This allows it to generate music that is optimized for the user's voice quality and singing style.

[0050] The generation AI can use its emotion estimation function to generate lyrics and melodies that correspond to the user's emotional state. For example, the generation AI can estimate emotions from text or voice input by the user and generate lyrics and melodies that match those emotions. For example, if a user wants to express sadness, it can create a melancholic melody and lyrics. The generation AI can also analyze the user's emotional state in real time and generate music that corresponds to the emotion at that time. For example, if the user is feeling happy, it can create a bright and positive melody and lyrics. The generation AI can also use its emotion estimation function to generate music that corresponds to the user's emotional changes. For example, if a user wants to express emotional ups and downs, it can create a melody and lyrics that reflect the emotional changes in the music. This allows it to generate music that corresponds to the user's emotional state.

[0051] Generative AI can generate music that incorporates musical styles from different genres and cultural spheres. For example, generative AI can learn the characteristics of different musical genres and generate music that fuses them together. For example, it can create a song that combines jazz and hip-hop. Generative AI can also generate music that incorporates musical styles from different cultural spheres. For example, it can create a song that fuses African rhythms with European melodies. Generative AI can also generate music that incorporates elements from different genres and cultural spheres based on the user's preferences. For example, if a user likes electronica and traditional folk music, it can create a song that fuses the two. This makes it possible to generate music that incorporates musical styles from different genres and cultural spheres.

[0052] The generation AI can generate music based on poems and phrases entered by the user. For example, the generation AI creates a melody and accompaniment that matches the content of the poem or phrase entered by the user. For example, if the poem entered by the user has a love theme, the generation AI will generate a romantic melody. The generation AI also generates music that uses the phrase entered by the user repeatedly. For example, if the user enters the phrase "I love you," the generation AI will create a song that incorporates that phrase into the chorus. The generation AI also analyzes the poem or phrase entered by the user and generates a music composition that is optimal for the content. For example, if the poem entered by the user is in the form of a story, the generation AI will create a song that follows that story. This allows music to be generated based on the poem or phrase entered by the user.

[0053] The generation AI can use its emotion estimation function to analyze the emotion of the prompt entered by the user and generate music that matches that emotion. For example, the generation AI can analyze the emotion of the prompt entered by the user and generate lyrics and a melody that match that emotion. For example, if the user enters "I'm feeling happy," it will create a bright, up-tempo song. The generation AI can also use its emotion estimation function to extract emotions from the user's prompt and generate music based on that emotion. For example, if the user enters "Sad memories," it will create a melancholic melody and lyrics. The generation AI can also analyze the emotion of the user's prompt and generate a music composition that best matches that emotion. For example, if the user enters "A hopeful future," it will create a melody and lyrics that evoke a sense of hope. This allows it to generate music that matches the emotion of the prompt entered by the user.

[0054] The uploading unit uses a generation AI to automatically generate thumbnails for videos when they are uploaded, attracting viewers' interest. For example, the uploading unit uses a generation AI to analyze the content of the video and automatically generate thumbnails for the most visually appealing scenes. For example, it may select scenes in which the user is singing or scenes that express emotions. The uploading unit also adds elements to the video thumbnails that attract viewers' interest. For example, it may insert catchy text or graphics into the thumbnail. The uploading unit also uses a generation AI to generate multiple thumbnail candidates based on the content of the video, allowing the user to select the most suitable one. For example, it may suggest thumbnails that combine different scenes and text. This allows the automatic generation of thumbnails that attract viewers' interest.

[0055] The uploading unit uses a generation AI to automatically generate video titles and descriptions when uploading videos, enabling SEO measures. For example, the generation AI analyzes the content of the video and automatically generates titles and descriptions containing optimal keywords. For example, it creates a title that reflects the theme and characteristics of the video. The uploading unit also generates catchphrases and descriptions that will attract viewers' interest based on the content of the video. For example, it emphasizes the highlights and unique points of the video. The generation AI also takes SEO measures into consideration when generating titles and descriptions that will improve the video's ranking in search engines. For example, it incorporates popular keywords and phrases. This allows for the automatic generation of titles and descriptions that take SEO measures into account.

[0056] The uploading unit can use the emotion estimation function to analyze the emotional reactions of viewers of a video in real time and provide feedback. For example, the uploading unit uses the emotion estimation function to analyze the emotional reactions from viewers' facial expressions and comments, and provides feedback based on that data. For example, it can grasp in real time whether the viewer is enjoying the video. The uploading unit also analyzes the emotional reactions of viewers and provides advice on how to improve the video or how to create the next video. For example, it can highlight parts that viewers found particularly interesting. The uploading unit also displays the emotional reactions of viewers in real time based on the emotion estimation data, allowing the user to adjust the content of the video while viewing those reactions. For example, it can highlight scenes that heighten the viewer's emotions. This allows the uploading unit to analyze the emotional reactions of viewers in real time and provide feedback.

[0057] In the uploading section, the generation AI automatically adds effects and filters to videos when they are uploaded, enhancing their visual appeal. For example, in the uploading section, the generation AI automatically adds optimal effects and filters based on the content of the video. For example, it applies color correction and special effects to specific scenes. In the uploading section, the generation AI also suggests effects and filters that match the theme and atmosphere of the video, allowing the user to select one. For example, it offers retro and modern filters. In the uploading section, the generation AI also adds dynamic effects and animations to enhance the visual appeal of the video. For example, it displays lyrics or incorporates background movement. This allows the automatic addition of effects and filters that enhance visual appeal.

[0058] The uploading unit uses a generation AI to automatically generate subtitles for videos when they are uploaded, helping viewers understand. For example, the uploading unit uses a generation AI to analyze the audio of the video and automatically generate subtitles. For example, it converts lyrics and spoken content into text and displays it in the video. The uploading unit also adjusts the format and display timing of the subtitles based on the content of the video to make them easier for viewers to understand. For example, it changes the font size to emphasize important parts. The uploading unit also uses a generation AI to automatically generate subtitles in multiple languages, making it suitable for international viewers. For example, it provides subtitles in English, Spanish, and other languages. This allows for the automatic generation of subtitles that help viewers understand.

[0059] The uploading unit can use the emotion estimation function to analyze the emotional reactions of viewers of a video and provide insights that will be useful for creating the next video. For example, the uploading unit can use the emotion estimation function to analyze the emotional reactions of viewers and provide insights that will be useful for creating the next video based on that data. For example, it can highlight parts that viewers particularly enjoyed. The uploading unit can also analyze the emotional reactions of viewers and suggest improvements and new ideas for the video. For example, it can incorporate themes or styles that viewers are interested in. The uploading unit can also display the emotional reactions of viewers in real time based on the emotion estimation data, allowing the user to use those reactions to help create the next video. For example, it can refer to scenes that heighten viewers' emotions. This can provide insights that will be useful for creating the next video.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The automatic lyrics and composition system can also monitor the user's physical condition and generate music based on that condition. For example, it can use sensors to acquire the user's heart rate and breathing rhythm and create a relaxing melody and tempo based on that data. It can also generate energetic, fast-paced music when the user is exercising. It can also analyze the user's level of physical fatigue and provide music with a refreshing effect. This allows it to generate music that matches the user's physical condition.

[0062] The automatic lyrics and composition system can also estimate the user's emotions and adjust the tempo and key of the music based on those emotions. For example, if the user is feeling stressed, it can generate a relaxing song with a slow tempo. On the other hand, if the user is excited, it can provide an energetic, up-tempo song. Furthermore, it can adjust the key of the music according to the user's emotional changes, providing a musical experience that matches the flow of the user's emotions. This allows it to generate music that matches the user's emotions.

[0063] The automatic lyrics and composition system can also generate music incorporating the user's environmental sounds. For example, the system can incorporate the chirping of birds or the murmuring of a river recorded by the user in nature into a song. It can also create music incorporating the user's surrounding environmental sounds, such as the sounds of a busy city or a cafe. It can also generate music that reflects the atmosphere of a specific location based on sounds recorded by the user in that location. This allows the system to generate music incorporating the user's environmental sounds.

[0064] The automatic songwriting system can also estimate the user's emotions and dynamically change the composition of a song based on those emotions. For example, if the user wants to express sadness, the system can shorten the intro and lengthen the chorus, which emphasizes the emotion. Alternatively, if the user is feeling happy, the system can emphasize the bridge section of the song to convey a positive message. Furthermore, the system can adjust the composition of the song in real time according to the user's emotional changes, providing a musical experience that matches the flow of the user's emotions. This allows the system to generate a song composition that matches the user's emotions.

[0065] The automatic lyrics and composition system can also generate music incorporating the user's favorite instruments. For example, if the user likes the guitar, the system can create music that emphasizes guitar riffs and solos. If the user likes the piano, the system can provide music that focuses on piano melodies. Furthermore, if the user likes the tone of a particular instrument, the system can generate music that mainly uses that instrument. This allows the system to generate music incorporating the user's favorite instruments.

[0066] The automatic songwriting system can also estimate the user's emotions and adjust the musical effects based on those emotions. For example, if the user wants to relax, the system can generate a musical piece with a spacious feel using a lot of reverb and delay. If the user is feeling energetic, the system can provide a powerful sound using distortion and overdrive. Furthermore, the system can adjust the effect settings in real time according to the user's emotional changes, providing a musical experience that matches the flow of the user's emotions. This allows the system to generate a musical piece with effects that match the user's emotions.

[0067] The automatic lyrics and composition system can also suggest new musical styles that the user has not yet tried based on the user's past song history. For example, if the user primarily likes pop music, the system can suggest songs that incorporate elements of jazz and classical music. If the user prefers rock, the system can provide songs that incorporate elements of electronica and hip hop. Furthermore, if the user is biased toward a particular genre, the system can suggest new musical styles that differ from that genre, broadening the user's musical horizons. This allows the system to generate songs in new styles that broaden the user's musical horizons.

[0068] The automatic songwriting system can also estimate the user's emotions and adjust the mix of the music based on those emotions. For example, if the user wants to relax, the system can bring the vocals to the forefront and tone down the background instruments. Alternatively, if the user is feeling energetic, the system can emphasize the drums and bass to enhance the rhythm. Furthermore, the system can adjust the mix balance in real time according to the user's emotional changes, providing a musical experience that matches the flow of the user's emotions. This allows the system to generate music with a mix that matches the user's emotions.

[0069] The automatic songwriting system can also generate music that incorporates the style of a user's favorite artists or bands. For example, if a user is a fan of a particular artist, the system can create music that imitates that artist's distinctive sound and rhythm. If a user likes multiple artists, the system can provide music that combines elements of those artists. Furthermore, if a user wants to discover a new artist, the system can suggest music that incorporates that artist's style, broadening the user's musical horizons. This allows the system to generate music that incorporates the style of a user's favorite artists or bands.

[0070] The automatic songwriting system can also estimate the user's emotions and adjust the musical arrangement based on those emotions. For example, if the user wants to feel calm, a simple and minimal arrangement can be used. On the other hand, if the user is excited, a complex and dynamic arrangement can be provided. Furthermore, the system can adjust the musical arrangement in real time according to the user's emotional changes, providing a musical experience that follows the flow of the user's emotions. This allows the system to generate musical pieces with arrangements that match the user's emotions.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The generative AI automatically composes lyrics and music based on user instructions. For example, if a user inputs a prompt such as "Please create a song with an upbeat atmosphere," the AI ​​generates lyrics and a melody based on the user's instructions. The generative AI can also generate music using text generation AI (e.g., LLM) or multimodal generation AI. Step 2: The user interface provides functionality for the user to preview the music generated by the generative AI. For example, the user interface may include a text box for entering prompts and a play button for previewing the generated music. Step 3: The uploading unit uploads the video of the user singing to the web through a user interface. For example, the uploading unit has a function of uploading the video file to a specified platform.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 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. Generative AI and A user interface; an upload unit; The generated AI is It automatically composes lyrics and music based on instructions from the user. The user interface includes: A user previews the music generated by the generation AI, The upload unit The user uploads the singing video to the web through the user interface. A system characterized by:

2. The generated AI is Analyze the user's past song history and compose lyrics and music that match the user's preferences 2. The system of claim 1.

3. The generated AI is Learns the user's voice quality and singing style and generates music optimized for that.

2. The system of claim 1.

4. The generated AI is Generate lyrics and a melody according to the emotional state of the user 2. The system of claim 1.

5. The generated AI is Generate music incorporating musical styles from different genres and cultural spheres 2. The system of claim 1.

Images