system

Abstract

The system according to the embodiment aims to perform all processes from translating lyrics to creating music. [Solution] A system according to an embodiment includes a reception unit, a translation unit, a generation unit, and a management unit. The reception unit inputs lyrics. The translation unit translates the lyrics input by the reception unit. The generation unit generates music based on the lyrics translated by the translation unit. The management unit manages the rights to the generated music.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the processes of lyric translation and music creation were separated, making it difficult to carry out the process efficiently.

[0005] The system according to the embodiment aims to perform all processes from translating lyrics to creating music. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a translation unit, a generation unit, and a management unit. The reception unit inputs lyrics. The translation unit translates the lyrics input by the reception unit. The generation unit generates music based on the lyrics translated by the translation unit. The management unit manages the rights to the generated music. [Effects of the Invention]

[0007] The system according to the embodiment can consistently perform processes from translating lyrics to creating music. [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) An anime song translation system according to an embodiment of the present invention translates anime songs and generates international versions of anime songs. This system inputs the lyrics of an original anime song, and the AI ​​translation generates a translation that matches the vowels of key message words as closely as possible to the original song. This eliminates the sense of incongruity of the melody line. Furthermore, the system retains only 20% of the rights to the newly generated song, with the remainder going back to the song provider. For example, a user inputs the lyrics of an original anime song. This requires only entering the entire lyrics. For example, the user can input the lyrics of the opening theme of a popular anime. This information is then input into the AI ​​generation system. The AI ​​then analyzes the input lyrics and performs a translation. The AI ​​generates a translation that matches the vowels of key message words as closely as possible to the original song. For example, if the original lyrics contain the word "love," the AI ​​can match the vowels of the word corresponding to "love" in the translated lyrics to eliminate the sense of incongruity of the melody line. A new song is then generated based on the generated translated lyrics. The AI ​​can also adjust the melody line to match the translated lyrics. This allows us to generate seamless international versions of anime songs while preserving the atmosphere of the original. Furthermore, we only keep 20% of the rights to the newly generated songs as profit, with the remainder going back to the music provider. This allows music providers to generate new revenue. This system makes it easier to promote anime songs overseas and increases the revenue of music providers. For example, if the opening theme of a popular anime becomes a hit overseas, music providers can earn revenue in a new market. Furthermore, using generation AI streamlines the translation process, allowing us to quickly provide international versions of songs. This allows our anime song translation system to efficiently translate and generate anime songs, bringing new revenue to music providers.

[0029] An anime song translation system according to an embodiment includes a reception unit, a translation unit, a generation unit, and a management unit. The reception unit receives lyrics for an original anime song input by a user. For example, the reception unit can receive lyrics input by a user in digital format. The reception unit can also scan handwritten lyrics and convert them into digital data. For example, the reception unit scans the handwritten lyrics and converts them into text data using OCR technology. The translation unit uses a generation AI to translate the lyrics input by the reception unit. For example, the translation unit generates a translation that matches the vowels of words that form a key message as closely as possible to those in the original song. For example, the generation AI matches the vowels of the word "love" in the lyrics of the original song in the translated lyrics. The generation unit generates a musical piece based on the lyrics translated by the translation unit. For example, the generation unit adjusts the melody line to match the translated lyrics. For example, the generation unit adjusts the rhythm and pitch of the translated lyrics to match those of the original song. The management unit manages the rights to the generated musical piece. For example, the management department may keep 20% of the rights to the created music as profits for the company and return the remainder to the music provider. For example, the management department may manage a contract under which 80% of the profits are returned to the music provider. This allows the anime song translation system according to the embodiment to perform an integrated process from lyric input to translation, music creation, and rights management.

[0030] The translation unit can generate a translation that matches the vowels of words that form the key message as closely as possible to those in the original song. For example, the translation unit uses a generation AI to match the vowels of words that form the key message in the lyrics of the original song to those in the translated lyrics. For example, if the original lyrics contain the word "love," the vowels of the word equivalent to "love" can be matched in the translated lyrics to eliminate any sense of incongruity in the melody line. The translation unit can also use a generation AI to apply an algorithm that prioritizes matching the vowels of words that form the key message when translating. For example, the generation AI receives a prompt that prioritizes matching the vowels of words that form the key message when translating, and then performs the translation. This allows the translation unit to eliminate any sense of incongruity in the melody line.

[0031] The generation unit can adjust the melody line to match the translated lyrics. For example, the generation unit adjusts the rhythm and pitch of the translated lyrics to match the original song. For example, the generation unit adjusts the rhythm of the translated lyrics to match the rhythm of the original song. The generation unit can also adjust the pitch of the translated lyrics to match the pitch of the original song. For example, the generation unit can apply an algorithm that adjusts the pitch of the translated lyrics to match the pitch of the original song. This allows the generation unit to generate a seamless piece of music while maintaining the atmosphere of the original song.

[0032] The management department can keep 20% of the rights to the generated songs as profits for the company and return the remainder to the song provider. For example, the management department manages a contract under which 20% of the rights to the generated songs are profits for the company and the remainder is returned to the song provider. For example, the management department manages a contract under which 80% of the revenue is returned to the song provider. The management department can also build a system for managing the rights to the generated songs. For example, the management department manages the rights to the generated songs in digital format and builds a system that automates the distribution of revenue. This allows the management department to return new revenue to the song provider.

[0033] The reception unit can analyze the user's past lyric input history and select the most appropriate input method. The reception unit, for example, stores the user's past lyric input history in a database and analyzes it. For example, the reception unit can preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also analyze patterns of lyrics that the user has input in the past and suggest the optimal input method. For example, the reception unit can predict and suggest the input method that will be used in a specific time period based on the user's past input history. In this way, the reception unit can provide the user with the optimal input method.

[0034] The reception unit can filter lyrics based on the user's current project or field of interest when the lyrics are input. For example, the reception unit stores and analyzes information related to the user's current project or field of interest in a database. For example, the reception unit preferentially displays lyrics related to the project the user is currently working on. The reception unit can also filter and display related lyrics based on the user's field of interest. For example, the reception unit suggests related lyrics based on fields in which the user has shown interest in the past. This allows the reception unit to preferentially input lyrics that match the user's interests.

[0035] When inputting lyrics, the reception unit can prioritize inputting highly relevant lyrics in consideration of the user's geographical location information. The reception unit uses, for example, GPS data or an IP address to acquire the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize displaying lyrics related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize displaying lyrics related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize displaying lyrics related to the home. This allows the reception unit to input lyrics that are highly relevant according to the user's geographical location.

[0036] The reception unit can analyze the user's social media activity and input relevant lyrics when inputting lyrics. For example, the reception unit stores and analyzes the content of posts and the number of followers in a database to analyze the user's social media activity. For example, the reception unit can suggest related lyrics based on the content the user shared on social media. The reception unit can also preferentially display lyrics of artists the user follows on social media. Furthermore, the reception unit can suggest related lyrics based on posts the user "liked" on social media. This allows the reception unit to input lyrics that are highly relevant based on the user's social media activity.

[0037] During translation, the translation unit can adjust the level of detail of the translation based on the importance of the lyrics. For example, the translation unit analyzes the meaning and message of the lyrics to evaluate the importance of the lyrics. For example, the translation unit provides a detailed translation, focusing on words that represent key messages. The translation unit can also provide a concise translation of less important parts. Furthermore, the translation unit can provide a detailed translation of more important parts while preserving the nuances of the original song. This allows the translation unit to provide a translation with an appropriate level of detail according to the importance of the lyrics.

[0038] The translation unit can apply different translation algorithms depending on the category of lyrics during translation. For example, the translation unit analyzes genres and themes to classify the category of lyrics. For example, the translation unit can apply a translation algorithm that uses emotional expressions to a love song. The translation unit can also apply a translation algorithm that uses powerful expressions to an action song. Furthermore, the translation unit can apply a translation algorithm that uses delicate expressions to a ballad. This allows the translation unit to apply an appropriate translation algorithm depending on the category of lyrics.

[0039] During translation, the translation department can determine the priority of translation based on the time of submission of lyrics. For example, the translation department stores and analyzes the submission date and time in a database to evaluate the time of submission of lyrics. For example, the translation department gives priority to translating lyrics with an approaching deadline. The translation department can also give priority to translating lyrics with an early submission date. Furthermore, the translation department can postpone lyrics with a late submission date. This allows the translation department to perform translations with appropriate priority according to the time of submission.

[0040] During translation, the translation unit can adjust the order of translation based on the relevance of lyrics. For example, the translation unit analyzes thematic matches and commonality of keywords to evaluate the relevance of lyrics. For example, the translation unit prioritizes translating lyrics that are highly relevant to the original song. The translation unit can also postpone translating lyrics that are less relevant. Furthermore, the translation unit can translate highly relevant parts in detail and less relevant parts briefly. This allows the translation unit to translate in an appropriate order based on relevance.

[0041] When generating music, the generation unit can analyze the user's past music generation history and select the optimal generation method. For example, the generation unit stores the user's past music generation history in a database and analyzes it. For example, the generation unit analyzes the patterns of music generated by the user in the past and proposes the optimal generation method. The generation unit can also predict and propose a generation method to be used in a specific time period based on the user's past music generation history. Furthermore, the generation unit can preferentially propose a generation method (audio, text, etc.) that the user has used in the past. This allows the generation unit to generate music using the optimal method based on the user's past history.

[0042] The generation unit can customize the generation means based on the user's current living situation when generating music. For example, the generation unit stores and analyzes daily activities and living environment in a database to evaluate the user's current living situation. For example, if the user is busy, the generation unit can suggest music that can be generated in a short time. Also, if the user is relaxed, the generation unit can provide detailed generation instructions. Furthermore, if the user is participating in a specific event, the generation unit can suggest music that matches the event. This allows the generation unit to generate music in an appropriate manner according to the user's living situation.

[0043] When generating music, the generation unit can select the most appropriate generation method based on the user's geographical location information. The generation unit, for example, uses GPS data or an IP address to acquire the user's geographical location information. For example, if the user is in a specific area, the generation unit can prioritize generating music related to that area. Also, if the user is traveling, the generation unit can prioritize generating music related to the travel destination. Furthermore, if the user is at home, the generation unit can prioritize generating music related to the home. This allows the generation unit to generate music using the optimal method according to the user's geographical location.

[0044] When generating music, the generation unit can analyze the user's social media activity and suggest a generation method. For example, to analyze the user's social media activity, the generation unit stores and analyzes the content of posts and the number of followers in a database. For example, the generation unit can suggest related music based on the content the user shared on social media. The generation unit can also prioritize generating music by artists the user follows on social media. Furthermore, the generation unit can suggest related music based on posts the user "liked" on social media. This allows the generation unit to generate music in an optimal manner based on the user's social media activity.

[0045] During rights management, the management unit can select the most appropriate management method based on past rights management data. For example, the management unit stores past rights management data in a database and analyzes it. For example, the management unit analyzes past rights management data and proposes an optimal management method. The management unit can also extract specific patterns from past rights management data and propose an optimal management method. Furthermore, the management unit can propose an efficient management method based on past rights management data. This allows the management unit to perform rights management using the optimal method based on past data.

[0046] The management unit can apply different management methods depending on the category of a song during rights management. For example, the management unit analyzes genres and themes to classify songs into categories. For example, the management unit can apply a rights management method that emphasizes emotional elements to a love song. The management unit can also apply a rights management method that emphasizes powerful expressions to an action song. Furthermore, the management unit can apply a rights management method that emphasizes delicate expressions to a ballad. This allows the management unit to perform rights management in an appropriate manner depending on the category of the song.

[0047] During rights management, the management unit can determine management priorities based on the time of submission of the music. For example, the management unit stores and analyzes the submission date and time in a database to evaluate the time of submission of the music. For example, the management unit prioritizes rights management for music with an approaching deadline. The management unit can also prioritize rights management for music submitted earlier. Furthermore, the management unit can postpone rights management for music submitted later. This allows the management unit to perform rights management with appropriate priorities according to the time of submission.

[0048] During rights management, the management unit can adjust the management order based on the relevance of songs. For example, the management unit analyzes thematic matches and commonality of keywords to evaluate the relevance of songs. For example, the management unit prioritizes rights management of songs that are highly related to the original song. The management unit can also postpone rights management of songs that are less related. Furthermore, the management unit can manage highly related parts in detail and less related parts simply. This allows the management unit to perform rights management in an appropriate order based on relevance.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The reception unit can analyze the context of the lyrics entered by the user and automatically suggest related images and videos. For example, if the lyrics contain the word "ocean," the reception unit can suggest images and videos of the ocean. Also, if the lyrics contain the word "love," the reception unit can suggest images and videos of romantic scenes. Furthermore, the reception unit can reflect images and videos selected by the user in the translation of the lyrics. In this way, the reception unit can visually enrich the user's lyric input experience.

[0051] The generation unit can propose different instrument arrangements based on the rhythm of the lyrics entered by the user. For example, if the lyrics have a fast rhythm, the generation unit can propose an arrangement that emphasizes drums or guitar. Alternatively, if the lyrics have a slow rhythm, the generation unit can propose an arrangement that emphasizes piano or violin. Furthermore, the generation unit can propose arrangements with different tempos depending on the rhythm of the lyrics. This allows the generation unit to provide an optimal instrument arrangement that matches the rhythm of the lyrics.

[0052] The management department can manage the rights of the generated music using blockchain technology. For example, the management department can record the rights information of the generated music on the blockchain to prevent tampering. The management department can also record revenue sharing information between the music provider and our company on the blockchain. Furthermore, the management department can record the usage history of the music on the blockchain to ensure transparency. This allows the management department to manage the rights of the generated music safely and transparently.

[0053] The reception unit can suggest the optimal translation language based on the user's past lyric input history. For example, the reception unit can prioritize suggesting languages ​​that the user has used frequently in the past. The reception unit can also suggest languages ​​suitable for a specific genre based on the user's past input history. Furthermore, the reception unit can analyze the user's past input history and suggest the optimal translation style. This allows the reception unit to provide the optimal translation language and style based on the user's past history.

[0054] The reception unit can suggest relevant lyrics by taking into consideration the user's current weather information when the user inputs lyrics. For example, if the user is inputting lyrics on a rainy day, the reception unit can suggest lyrics related to rain. Furthermore, if the user is inputting lyrics on a sunny day, the reception unit can also suggest lyrics related to sunny days. Furthermore, if the user is inputting lyrics on a snowy day, the reception unit can also suggest lyrics related to snow. In this way, the reception unit can provide highly relevant lyrics according to the user's current weather information.

[0055] The reception unit can suggest relevant lyrics in consideration of the user's current activity status when the user is entering lyrics. For example, if the user is entering lyrics while exercising, the reception unit can suggest energetic lyrics. If the user is relaxing, the reception unit can also suggest calm lyrics. Furthermore, if the user is entering lyrics while working, the reception unit can also suggest lyrics that will help the user to concentrate. In this way, the reception unit can provide lyrics that are highly relevant to the user's current activity status.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The reception unit receives the lyrics of the original anime song entered by the user. For example, the reception unit can receive the lyrics entered by the user in digital format. The reception unit can also scan handwritten lyrics and convert them into digital data. For example, the reception unit can read the handwritten lyrics with a scanner and convert them into text data using OCR technology. Step 2: The translation unit uses the generation AI to translate the lyrics entered by the reception unit. The translation unit generates a translation that matches the vowels of words that form the key message as closely as possible to the original song. For example, the generation AI matches the vowels of the word "love" in the original lyrics to the translated lyrics. Step 3: The generation unit generates a musical piece based on the lyrics translated by the translation unit. The generation unit, for example, adjusts the melody line to match the translated lyrics. For example, the generation unit adjusts the rhythm and pitch of the translated lyrics to match the original song. Step 4: The management department manages the rights of the generated music. For example, the management department keeps 20% of the rights of the generated music as profit for our company and returns the rest to the music provider. For example, the management department manages a contract that returns 80% of the revenue to the music provider.

[0058] (Example 2) An anime song translation system according to an embodiment of the present invention translates anime songs and generates international versions of anime songs. This system inputs the lyrics of an original anime song, and the AI ​​translation generates a translation that matches the vowels of key message words as closely as possible to the original song. This eliminates the sense of incongruity of the melody line. Furthermore, the system retains only 20% of the rights to the newly generated song, with the remainder going back to the song provider. For example, a user inputs the lyrics of an original anime song. This requires only entering the entire lyrics. For example, the user can input the lyrics of the opening theme of a popular anime. This information is then input into the AI ​​generation system. The AI ​​then analyzes the input lyrics and performs a translation. The AI ​​generates a translation that matches the vowels of key message words as closely as possible to the original song. For example, if the original lyrics contain the word "love," the AI ​​can match the vowels of the word corresponding to "love" in the translated lyrics to eliminate the sense of incongruity of the melody line. A new song is then generated based on the generated translated lyrics. The AI ​​can also adjust the melody line to match the translated lyrics. This allows us to generate seamless international versions of anime songs while preserving the atmosphere of the original. Furthermore, we only keep 20% of the rights to the newly generated songs as profit, with the remainder going back to the music provider. This allows music providers to generate new revenue. This system makes it easier to promote anime songs overseas and increases the revenue of music providers. For example, if the opening theme of a popular anime becomes a hit overseas, music providers can earn revenue in a new market. Furthermore, using generation AI streamlines the translation process, allowing us to quickly provide international versions of songs. This allows our anime song translation system to efficiently translate and generate anime songs, bringing new revenue to music providers.

[0059] An anime song translation system according to an embodiment includes a reception unit, a translation unit, a generation unit, and a management unit. The reception unit receives lyrics for an original anime song input by a user. For example, the reception unit can receive lyrics input by a user in digital format. The reception unit can also scan handwritten lyrics and convert them into digital data. For example, the reception unit scans the handwritten lyrics and converts them into text data using OCR technology. The translation unit uses a generation AI to translate the lyrics input by the reception unit. For example, the translation unit generates a translation that matches the vowels of words that form a key message as closely as possible to those in the original song. For example, the generation AI matches the vowels of the word "love" in the lyrics of the original song in the translated lyrics. The generation unit generates a musical piece based on the lyrics translated by the translation unit. For example, the generation unit adjusts the melody line to match the translated lyrics. For example, the generation unit adjusts the rhythm and pitch of the translated lyrics to match those of the original song. The management unit manages the rights to the generated musical piece. For example, the management department may keep 20% of the rights to the created music as profits for the company and return the remainder to the music provider. For example, the management department may manage a contract under which 80% of the profits are returned to the music provider. This allows the anime song translation system according to the embodiment to perform an integrated process from lyric input to translation, music creation, and rights management.

[0060] The translation unit can generate a translation that matches the vowels of words that form the key message as closely as possible to those in the original song. For example, the translation unit uses a generation AI to match the vowels of words that form the key message in the lyrics of the original song to those in the translated lyrics. For example, if the original lyrics contain the word "love," the vowels of the word equivalent to "love" can be matched in the translated lyrics to eliminate any sense of incongruity in the melody line. The translation unit can also use a generation AI to apply an algorithm that prioritizes matching the vowels of words that form the key message when translating. For example, the generation AI receives a prompt that prioritizes matching the vowels of words that form the key message when translating, and then performs the translation. This allows the translation unit to eliminate any sense of incongruity in the melody line.

[0061] The generation unit can adjust the melody line to match the translated lyrics. For example, the generation unit adjusts the rhythm and pitch of the translated lyrics to match the original song. For example, the generation unit adjusts the rhythm of the translated lyrics to match the rhythm of the original song. The generation unit can also adjust the pitch of the translated lyrics to match the pitch of the original song. For example, the generation unit can apply an algorithm that adjusts the pitch of the translated lyrics to match the pitch of the original song. This allows the generation unit to generate a seamless piece of music while maintaining the atmosphere of the original song.

[0062] The management department can keep 20% of the rights to the generated songs as profits for the company and return the remainder to the song provider. For example, the management department manages a contract under which 20% of the rights to the generated songs are profits for the company and the remainder is returned to the song provider. For example, the management department manages a contract under which 80% of the revenue is returned to the song provider. The management department can also build a system for managing the rights to the generated songs. For example, the management department manages the rights to the generated songs in digital format and builds a system that automates the distribution of revenue. This allows the management department to return new revenue to the song provider.

[0063] The reception unit can estimate the user's emotions and adjust the timing of lyric input based on the estimated user emotions. The reception unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the reception unit adjusts the timing of lyric input based on the user's emotions. For example, if the user is relaxed, the reception unit can encourage the user to input lyrics at a slower pace. If the user is in a hurry, the reception unit can simplify the interface to allow for quick input. Furthermore, if the user is excited, the reception unit can make the interface visually stimulating to encourage input. This allows the reception unit to input lyrics at an appropriate timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0064] The reception unit can analyze the user's past lyric input history and select the most appropriate input method. The reception unit, for example, stores the user's past lyric input history in a database and analyzes it. For example, the reception unit can preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also analyze patterns of lyrics that the user has input in the past and suggest the optimal input method. For example, the reception unit can predict and suggest the input method that will be used in a specific time period based on the user's past input history. In this way, the reception unit can provide the user with the optimal input method.

[0065] The reception unit can filter lyrics based on the user's current project or field of interest when the lyrics are input. For example, the reception unit stores and analyzes information related to the user's current project or field of interest in a database. For example, the reception unit preferentially displays lyrics related to the project the user is currently working on. The reception unit can also filter and display related lyrics based on the user's field of interest. For example, the reception unit suggests related lyrics based on fields in which the user has shown interest in the past. This allows the reception unit to preferentially input lyrics that match the user's interests.

[0066] The reception unit can estimate the user's emotions and determine the priority of lyrics to be input based on the estimated user emotions. The reception unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the user's voice to estimate the emotions. The reception unit further determines the priority of lyrics to be input based on the user's emotions. For example, the reception unit can prioritize inputting emotional lyrics if the user is relaxed. The reception unit can also prioritize inputting short and concise lyrics if the user is in a hurry. The reception unit can also prioritize inputting energetic lyrics if the user is excited. This allows the reception unit to input lyrics in a priority order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0067] When inputting lyrics, the reception unit can prioritize inputting highly relevant lyrics in consideration of the user's geographical location information. The reception unit uses, for example, GPS data or an IP address to acquire the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize displaying lyrics related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize displaying lyrics related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize displaying lyrics related to the home. This allows the reception unit to input lyrics that are highly relevant according to the user's geographical location.

[0068] The reception unit can analyze the user's social media activity and input relevant lyrics when inputting lyrics. For example, the reception unit stores and analyzes the content of posts and the number of followers in a database to analyze the user's social media activity. For example, the reception unit can suggest related lyrics based on the content the user shared on social media. The reception unit can also preferentially display lyrics of artists the user follows on social media. Furthermore, the reception unit can suggest related lyrics based on posts the user "liked" on social media. This allows the reception unit to input lyrics that are highly relevant based on the user's social media activity.

[0069] The translation unit can estimate the user's emotions and adjust the translation expression based on the estimated user emotions. The translation unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the translation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The translation unit can also estimate the user's emotions using voice analysis technology. For example, the translation unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the translation unit adjusts the translation expression based on the user's emotions. For example, the translation unit generates a translation using soft expressions when the user is relaxed. The translation unit can also generate a translation using concise and direct expressions when the user is in a hurry. Furthermore, the translation unit can also generate a translation using energetic expressions when the user is excited. This allows the translation unit to perform translation using an appropriate expression based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0070] During translation, the translation unit can adjust the level of detail of the translation based on the importance of the lyrics. For example, the translation unit analyzes the meaning and message of the lyrics to evaluate the importance of the lyrics. For example, the translation unit provides a detailed translation, focusing on words that represent key messages. The translation unit can also provide a concise translation of less important parts. Furthermore, the translation unit can provide a detailed translation of more important parts while preserving the nuances of the original song. This allows the translation unit to provide a translation with an appropriate level of detail according to the importance of the lyrics.

[0071] The translation unit can apply different translation algorithms depending on the category of lyrics during translation. For example, the translation unit analyzes genres and themes to classify the category of lyrics. For example, the translation unit can apply a translation algorithm that uses emotional expressions to a love song. The translation unit can also apply a translation algorithm that uses powerful expressions to an action song. Furthermore, the translation unit can apply a translation algorithm that uses delicate expressions to a ballad. This allows the translation unit to apply an appropriate translation algorithm depending on the category of lyrics.

[0072] The translation unit can estimate the user's emotions and adjust the length of the translation based on the estimated user emotions. The translation unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the translation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The translation unit can also estimate the user's emotions using voice analysis technology. For example, the translation unit analyzes the tone and speed of the user's voice to estimate the emotions. The translation unit further adjusts the length of the translation based on the user's emotions. For example, the translation unit generates a short, concise translation if the user is in a hurry. The translation unit can also generate a longer translation with detailed explanations if the user is relaxed. The translation unit can also generate a translation with visually stimulating effects if the user is excited. This allows the translation unit to provide a translation of an appropriate length based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0073] During translation, the translation department can determine the priority of translation based on the time of submission of lyrics. For example, the translation department stores and analyzes the submission date and time in a database to evaluate the time of submission of lyrics. For example, the translation department gives priority to translating lyrics with an approaching deadline. The translation department can also give priority to translating lyrics with an early submission date. Furthermore, the translation department can postpone lyrics with a late submission date. This allows the translation department to perform translations with appropriate priority according to the time of submission.

[0074] During translation, the translation unit can adjust the order of translation based on the relevance of lyrics. For example, the translation unit analyzes thematic matches and commonality of keywords to evaluate the relevance of lyrics. For example, the translation unit prioritizes translating lyrics that are highly relevant to the original song. The translation unit can also postpone translating lyrics that are less relevant. Furthermore, the translation unit can translate highly relevant parts in detail and less relevant parts briefly. This allows the translation unit to translate in an appropriate order based on relevance.

[0075] The generation unit can estimate the user's emotions and adjust the music generation method based on the estimated user emotions. The generation unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the generation unit adjusts the music generation method based on the user's emotions. For example, if the user is relaxed, the generation unit can generate music that progresses at a leisurely pace. If the user is in a hurry, the generation unit can generate music that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate music that adds visually stimulating effects. This allows the generation unit to generate music in an appropriate manner according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0076] When generating music, the generation unit can analyze the user's past music generation history and select the optimal generation method. For example, the generation unit stores the user's past music generation history in a database and analyzes it. For example, the generation unit analyzes the patterns of music generated by the user in the past and proposes the optimal generation method. The generation unit can also predict and propose a generation method to be used in a specific time period based on the user's past music generation history. Furthermore, the generation unit can preferentially propose a generation method (audio, text, etc.) that the user has used in the past. This allows the generation unit to generate music using the optimal method based on the user's past history.

[0077] The generation unit can customize the generation means based on the user's current living situation when generating music. For example, the generation unit stores and analyzes daily activities and living environment in a database to evaluate the user's current living situation. For example, if the user is busy, the generation unit can suggest music that can be generated in a short time. Also, if the user is relaxed, the generation unit can provide detailed generation instructions. Furthermore, if the user is participating in a specific event, the generation unit can suggest music that matches the event. This allows the generation unit to generate music in an appropriate manner according to the user's living situation.

[0078] The generation unit can estimate the user's emotions and determine the priority of music generation based on the estimated user emotions. The generation unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the generation unit determines the priority of music generation based on the user's emotions. For example, the generation unit can prioritize generating emotional music if the user is relaxed. The generation unit can also prioritize generating short and concise music if the user is in a hurry. Furthermore, the generation unit can prioritize generating energetic music if the user is excited. This allows the generation unit to generate music in a priority order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0079] When generating music, the generation unit can select the most appropriate generation method based on the user's geographical location information. The generation unit, for example, uses GPS data or an IP address to acquire the user's geographical location information. For example, if the user is in a specific area, the generation unit can prioritize generating music related to that area. Also, if the user is traveling, the generation unit can prioritize generating music related to the travel destination. Furthermore, if the user is at home, the generation unit can prioritize generating music related to the home. This allows the generation unit to generate music using the optimal method according to the user's geographical location.

[0080] When generating music, the generation unit can analyze the user's social media activity and suggest a generation method. For example, to analyze the user's social media activity, the generation unit stores and analyzes the content of posts and the number of followers in a database. For example, the generation unit can suggest related music based on the content the user shared on social media. The generation unit can also prioritize generating music by artists the user follows on social media. Furthermore, the generation unit can suggest related music based on posts the user "liked" on social media. This allows the generation unit to generate music in an optimal manner based on the user's social media activity.

[0081] The management unit can estimate the user's emotions and adjust the rights management method based on the estimated user emotions. The management unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the management unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The management unit can also estimate the user's emotions using voice analysis technology. For example, the management unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the management unit adjusts the rights management method based on the user's emotions. For example, the management unit provides a flexible rights management method when the user is relaxed. The management unit can also provide a quick rights management method when the user is in a hurry. Furthermore, the management unit can provide a detailed rights management method when the user is excited. This allows the management unit to perform rights management in an appropriate manner according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] During rights management, the management unit can select the most appropriate management method based on past rights management data. For example, the management unit stores past rights management data in a database and analyzes it. For example, the management unit analyzes past rights management data and proposes an optimal management method. The management unit can also extract specific patterns from past rights management data and propose an optimal management method. Furthermore, the management unit can propose an efficient management method based on past rights management data. This allows the management unit to perform rights management using the optimal method based on past data.

[0083] The management unit can apply different management methods depending on the category of a song during rights management. For example, the management unit analyzes genres and themes to classify songs into categories. For example, the management unit can apply a rights management method that emphasizes emotional elements to a love song. The management unit can also apply a rights management method that emphasizes powerful expressions to an action song. Furthermore, the management unit can apply a rights management method that emphasizes delicate expressions to a ballad. This allows the management unit to perform rights management in an appropriate manner depending on the category of the song.

[0084] The management unit can estimate the user's emotions and determine the priority of rights management based on the estimated user emotions. The management unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the management unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The management unit can also estimate the user's emotions using voice analysis technology. For example, the management unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the management unit determines the priority of rights management based on the user's emotions. For example, if the user is relaxed, the management unit can prioritize rights management of emotional songs. Also, if the user is in a hurry, the management unit can prioritize rights management of songs that can be processed quickly. Furthermore, if the user is excited, the management unit can prioritize rights management of energetic songs. This allows the management unit to perform rights management based on the priority of the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0085] During rights management, the management unit can determine management priorities based on the time of submission of the music. For example, the management unit stores and analyzes the submission date and time in a database to evaluate the time of submission of the music. For example, the management unit prioritizes rights management for music with an approaching deadline. The management unit can also prioritize rights management for music submitted earlier. Furthermore, the management unit can postpone rights management for music submitted later. This allows the management unit to perform rights management with appropriate priorities according to the time of submission.

[0086] During rights management, the management unit can adjust the management order based on the relevance of songs. For example, the management unit analyzes thematic matches and commonality of keywords to evaluate the relevance of songs. For example, the management unit prioritizes rights management of songs that are highly related to the original song. The management unit can also postpone rights management of songs that are less related. Furthermore, the management unit can manage highly related parts in detail and less related parts simply. This allows the management unit to perform rights management in an appropriate order based on relevance. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, translation unit, generation unit, and management unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives lyrics input by the user in digital format. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and translates the lyrics using a generation AI. The generation unit is realized, for example, by the control unit 46A of the smart device 14 and generates a song based on the translated lyrics. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the rights to the generated song. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, translation unit, generation unit, and management unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives lyrics input by a user in digital format. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and translates the lyrics using a generation AI. The generation unit is realized, for example, by the control unit 46A of the smart glasses 214 and generates music based on the translated lyrics. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the rights to the generated music. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, translation unit, generation unit, and management unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives lyrics input by the user in digital form. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and translates the lyrics using a generation AI. The generation unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and generates music based on the translated lyrics. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the rights to the generated music. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, translation unit, generation unit, and management unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives lyrics input by a user in digital form. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and translates the lyrics using a generation AI. The generation unit is realized, for example, by the control unit 46A of the robot 414 and generates a song based on the translated lyrics. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the rights to the generated song.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The reception unit can analyze the context of the lyrics entered by the user and automatically suggest related images and videos. For example, if the lyrics contain the word "ocean," the reception unit can suggest images and videos of the ocean. Also, if the lyrics contain the word "love," the reception unit can suggest images and videos of romantic scenes. Furthermore, the reception unit can reflect images and videos selected by the user in the translation of the lyrics. In this way, the reception unit can visually enrich the user's lyric input experience.

[0089] The translation unit can analyze the emotion of the lyrics entered by the user and select a translation style according to the emotion. For example, if the lyrics express sadness, the translation unit can generate a translation that emphasizes sadness. If the lyrics express joy, the translation unit can also generate a bright and positive translation. Furthermore, if the lyrics express anger, the translation unit can also generate a translation that uses powerful expressions. This allows the translation unit to provide an appropriate translation according to the emotion of the lyrics.

[0090] The generation unit can propose different instrument arrangements based on the rhythm of the lyrics entered by the user. For example, if the lyrics have a fast rhythm, the generation unit can propose an arrangement that emphasizes drums or guitar. Alternatively, if the lyrics have a slow rhythm, the generation unit can propose an arrangement that emphasizes piano or violin. Furthermore, the generation unit can propose arrangements with different tempos depending on the rhythm of the lyrics. This allows the generation unit to provide an optimal instrument arrangement that matches the rhythm of the lyrics.

[0091] The management department can manage the rights of the generated music using blockchain technology. For example, the management department can record the rights information of the generated music on the blockchain to prevent tampering. The management department can also record revenue sharing information between the music provider and our company on the blockchain. Furthermore, the management department can record the usage history of the music on the blockchain to ensure transparency. This allows the management department to manage the rights of the generated music safely and transparently.

[0092] The reception unit can estimate the user's emotion and customize the lyrics input interface based on the estimated emotion. For example, if the user is relaxed, the reception unit can provide an interface with a simple and calm design. If the user is excited, the reception unit can also provide an interface with a colorful and dynamic design. Furthermore, if the user is sad, the reception unit can also provide an interface with soft colors. In this way, the reception unit can provide an optimal input interface according to the user's emotion.

[0093] The reception unit can suggest the optimal translation language based on the user's past lyric input history. For example, the reception unit can prioritize suggesting languages ​​that the user has used frequently in the past. The reception unit can also suggest languages ​​suitable for a specific genre based on the user's past input history. Furthermore, the reception unit can analyze the user's past input history and suggest the optimal translation style. This allows the reception unit to provide the optimal translation language and style based on the user's past history.

[0094] The reception unit can suggest relevant lyrics by taking into consideration the user's current weather information when the user inputs lyrics. For example, if the user is inputting lyrics on a rainy day, the reception unit can suggest lyrics related to rain. Furthermore, if the user is inputting lyrics on a sunny day, the reception unit can also suggest lyrics related to sunny days. Furthermore, if the user is inputting lyrics on a snowy day, the reception unit can also suggest lyrics related to snow. In this way, the reception unit can provide highly relevant lyrics according to the user's current weather information.

[0095] The reception unit can estimate the user's emotion and suggest a lyric input method based on the estimated emotion. For example, the reception unit can suggest voice input when the user is relaxed. The reception unit can also suggest text input when the user is in a hurry. Furthermore, the reception unit can also suggest gesture input when the user is excited. In this way, the reception unit can provide the optimal input method according to the user's emotion.

[0096] The reception unit can suggest relevant lyrics in consideration of the user's current activity status when the user is entering lyrics. For example, if the user is entering lyrics while exercising, the reception unit can suggest energetic lyrics. If the user is relaxing, the reception unit can also suggest calm lyrics. Furthermore, if the user is entering lyrics while working, the reception unit can also suggest lyrics that will help the user to concentrate. In this way, the reception unit can provide lyrics that are highly relevant to the user's current activity status.

[0097] The translation unit can estimate the user's emotions and adjust the tone of the translation based on the estimated emotions. For example, the translation unit can generate a translation with a calm tone if the user is relaxed. The translation unit can also generate a translation with a concise and direct tone if the user is in a hurry. Furthermore, the translation unit can generate a translation with an energetic tone if the user is excited. This allows the translation unit to perform translation in an appropriate tone according to the user's emotions.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The reception unit receives the lyrics of the original anime song entered by the user. For example, the reception unit can receive the lyrics entered by the user in digital format. The reception unit can also scan handwritten lyrics and convert them into digital data. For example, the reception unit can read the handwritten lyrics with a scanner and convert them into text data using OCR technology. Step 2: The translation unit uses the generation AI to translate the lyrics entered by the reception unit. The translation unit generates a translation that matches the vowels of words that form the key message as closely as possible to the original song. For example, the generation AI matches the vowels of the word "love" in the original lyrics to the translated lyrics. Step 3: The generation unit generates a musical piece based on the lyrics translated by the translation unit. The generation unit, for example, adjusts the melody line to match the translated lyrics. For example, the generation unit adjusts the rhythm and pitch of the translated lyrics to match the original song. Step 4: The management department manages the rights of the generated music. For example, the management department keeps 20% of the rights of the generated music as profit for our company and returns the rest to the music provider. For example, the management department manages a contract that returns 80% of the revenue to the music provider.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0102] 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.

[0103] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0131] 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.

[0132] 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.

[0133] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception section for inputting lyrics; a translation unit that translates the lyrics input by the reception unit; a generation unit that generates music based on the lyrics translated by the translation unit; a management unit that manages the rights of the generated music; Equipped with A system characterized by:

2. The translation unit Generate a translation that matches the vowels of key words as closely as possible to the original song 2. The system of claim 1.

3. The generation unit Adjust the melody line to match the translated lyrics 2. The system of claim 1.

4. The management unit The company will keep 20% of the rights to the music it creates, and the rest will be returned to the music creator.

2. The system of claim 1.

5. The reception unit Estimates the user's emotions and adjusts the timing of lyrics input based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit Analyzes the user's past lyric input history and selects the most suitable input method 2. The system of claim 1.

7. The reception unit Filter lyrics as you type based on your current projects and interests 2. The system of claim 1.

8. The reception unit Estimate the user's emotions and prioritize the lyrics to be input based on the estimated user emotions.

2. The system of claim 1.

Images