system

Abstract

The system according to this embodiment aims to appropriately distribute revenue for songs that use the voice of a singer replicated by AI. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, a contract generation unit, and a distribution unit. The collection unit collects information for a composer or lyricist to create a song. Based on the information collected by the collection unit, the generation unit uses AI to replicate the original singer's voice and record the song. Based on the sound source generated by the generation unit, the contract generation unit automatically generates a contract regarding the use of the voice replicated by the AI. The distribution unit distributes the revenue based on the contract generated by the contract generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a risk that the revenue distribution for a music piece using the voice of a singer replicated by AI is not properly carried out.

[0005] The system according to the embodiment aims to properly perform the revenue distribution for a music piece using the voice of a singer replicated by AI.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, a contract generation unit, and a distribution unit. The collection unit collects information for a composer or lyricist to create a song. Based on the information collected by the collection unit, the generation unit uses AI to replicate the original singer's voice and record the song. Based on the sound source generated by the generation unit, the contract generation unit automatically generates a contract regarding the use of the voice replicated by the AI. The distribution unit distributes revenue based on the contract generated by the contract generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can appropriately distribute revenue for songs that use the voice of a singer replicated by AI. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​composition platform according to an embodiment of the present invention is a system that allows singers to earn revenue even when using sound sources that utilize the singer's voice replicated using AI. In this AI composition platform, composers and lyricists create songs on the platform, the AI ​​replicates the original singer's voice, and the song is recorded using that voice. At this time, a contract regarding the use of the voice replicated by the AI ​​is automatically generated so that the copyright of the original singer's voice is protected. Furthermore, if the song is sold, the revenue is distributed to the composer, lyricist, original singer, and the singer who provided the voice replicated by the AI. This mechanism strengthens the copyright of the singer's voice and provides the original singer with opportunities for sustainable revenue generation. In addition, the commercial use of generative AI will increase and new markets will be created. For example, composers and lyricists can create songs using the tools provided on the platform. They can create melodies and lyrics for songs using composition tools and lyric writing tools. Next, the AI ​​replicates the original singer's voice and records the song using that voice. The AI ​​analyzes the data of the original singer's voice and replicates it. In this process, the AI ​​learns the characteristics of the original singer's voice and can reproduce them. For example, it can reproduce the tone, rhythm, and pronunciation of the original singer's voice. Furthermore, a contract regarding the use of the AI-replicated voice is automatically generated. This contract outlines the terms of use for the original singer's voice and how revenue will be shared. For example, it specifies the royalties for using the original singer's voice and the percentage of revenue sharing. Finally, when a song is sold, the revenue is distributed to the composer, lyricist, original singer, and the singer who provided the AI-replicated voice. In this case, the revenue distribution is done automatically based on the contract. For example, 50% of the revenue is distributed to the composer and lyricist, 30% to the original singer, and 20% to the singer who provided the AI-replicated voice. This mechanism strengthens the copyright of the singer's voice and provides the original singer with opportunities for sustainable revenue generation. It also increases the commercial use of generative AI and creates new markets. For example, even after the original singer retires, their voice can be used to create new songs and generate revenue. Moreover, using generative AI enables new forms of musical expression that were not possible with conventional methods.This means that even if an AI composition platform uses an AI-generated singer's voice in the audio, the singer can still earn revenue.

[0029] The AI ​​composition platform according to this embodiment comprises a collection unit, a generation unit, a contract generation unit, and a distribution unit. The collection unit collects information for composers and lyricists to create songs. This information includes, but is not limited to, audio data, lyrics, and melodies. The collection unit can collect information directly from composers and lyricists, for example, through interviews. The collection unit can also collect existing song data and lyric data through database searches. Furthermore, the collection unit can collect information on the latest music trends and popular music genres through online surveys. For example, the collection unit can analyze song data previously created by composers and lyricists to collect information on similar musical styles. The collection unit can also analyze the themes of lyrics previously written by lyricists to collect information on related themes. The generation unit uses AI to replicate the original singer's voice and record the song based on the information collected by the collection unit. The generation unit uses, for example, speech synthesis technology to analyze the original singer's voice data and replicate that voice. The generation unit can learn the characteristics of the original singer's voice and reproduce those characteristics. For example, the generation unit can reproduce the tone, rhythm, and pronunciation of the original singer's voice. For example, the generation unit can perform spectral analysis on the data of the original singer's voice and extract acoustic features. The generation unit can also use machine learning algorithms to learn the characteristics of the original singer's voice and reproduce those characteristics. Furthermore, based on the data of the original singer's voice, the generation unit can use speech synthesis technology to replicate the original singer's voice with high accuracy. The contract generation unit automatically generates a contract regarding the use of the voice replicated by the AI ​​based on the sound source generated by the generation unit. The contract generation unit can generate specific contract details, such as licensing and revenue sharing ratios. For example, the contract generation unit can use AI to automatically generate a contract regarding the use of the original singer's voice. For example, the contract generation unit can specify the usage fee and revenue sharing ratio of the original singer's voice. For example, the contract generation unit can optimize the content of the contract using a contract generation algorithm. The distribution unit distributes revenue based on the contract generated by the contract generation unit.The distribution unit can, for example, clarify the specific types of revenue and the distribution methods. The distribution unit can, for example, clarify the specific types of revenue, such as streaming revenue and download revenue. The distribution unit can, for example, automatically calculate the revenue distribution ratio based on the contract and distribute the revenue. The distribution unit can, for example, optimize the revenue distribution method using AI. As a result, the AI ​​composition platform according to the embodiment allows the singer to earn revenue even when using sound sources that utilize the singer's voice replicated using AI.

[0030] The data collection unit can collect information for composers and lyricists to create songs. For example, the unit can collect information directly from composers and lyricists through interviews. For instance, it can ask composers and lyricists questions about the song's theme, melody, and lyrics, and collect their answers. The unit can also collect existing song and lyric data through database searches. For example, it can search music databases to collect information about specific music genres and artists. Furthermore, the unit can collect information about the latest music trends and popular music genres through online surveys. For example, it can collect information about the latest music trends and popular songs through music streaming services and social media. This allows composers and lyricists to efficiently collect information for creating songs. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input interview audio data into a generating AI, which can then convert the interview content into text data.

[0031] The generation unit can analyze the original singer's voice data using AI and replicate that voice. For example, the generation unit can use speech synthesis technology to analyze the original singer's voice data and replicate that voice. For example, the generation unit can perform spectral analysis on the original singer's voice data and extract acoustic features. Furthermore, the generation unit can use machine learning algorithms to learn the characteristics of the original singer's voice and reproduce those characteristics. For example, the generation unit can reproduce the tone, rhythm, and pronunciation of the original singer's voice. In addition, the generation unit can replicate the original singer's voice with high accuracy using speech synthesis technology based on the original singer's voice data. This allows for high-precision replication of the original singer's voice. Some or all of the above-described processes in the generation unit may be performed using AI, or without AI. For example, the generation unit can input the original singer's voice data into a generation AI, which can then replicate the original singer's voice.

[0032] The generation unit can learn the characteristics of the original singer's voice and reproduce those characteristics. The generation unit can learn the characteristics of the original singer's voice, for example, using a machine learning algorithm. For example, the generation unit can learn characteristics such as the tone, rhythm, and pronunciation of the original singer's voice. The generation unit can also reproduce the characteristics of the original singer's voice using speech synthesis technology. For example, the generation unit can reproduce the timbre, pitch, and rhythm of the original singer's voice. Furthermore, the generation unit can faithfully reproduce the original singer's voice using speech synthesis technology based on the data of the original singer's voice. This allows for a faithful reproduction of the characteristics of the original singer's voice. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the data of the original singer's voice into a generation AI, which can learn the characteristics of the original singer's voice and reproduce those characteristics.

[0033] The contract generation unit can automatically generate contracts regarding the use of a replicated voice using AI. For example, the contract generation unit can automatically generate a contract regarding the use of the original singer's voice using AI. For example, the contract generation unit can generate specific contract details such as licensing and revenue sharing ratios. For example, the contract generation unit can specify royalties and revenue sharing ratios for the use of the original singer's voice. For example, the contract generation unit can optimize the contract content using a contract generation algorithm. This enables the automatic generation of contracts regarding the use of a replicated voice. Some or all of the above processing in the contract generation unit may be performed using AI, for example, or without AI. For example, the contract generation unit can input the contract details regarding the use of the original singer's voice into a generation AI, and the generation AI can automatically generate the contract details.

[0034] The contract generation unit can specify the royalties for using the original singer's voice and the revenue sharing ratio. For example, the contract generation unit can specify the royalties for using the original singer's voice and the revenue sharing ratio. For example, the contract generation unit can calculate the royalties for using the original singer's voice based on the number of plays or downloads. The contract generation unit can also determine the revenue sharing ratio based on contract terms and contributions. For example, the contract generation unit can distribute 50% of the revenue to the composer and lyricist, 30% to the original singer, and 20% to the singer who provided the AI-generated voice. This allows for a clear specification of the royalties for using the original singer's voice and the revenue sharing ratio. Some or all of the above processing in the contract generation unit may be performed using AI, for example, or not. For example, the contract generation unit can input the royalties for using the original singer's voice and the revenue sharing ratio into a generating AI, and the generating AI can automatically generate the contract details.

[0035] The distribution unit can distribute revenue based on the contract. The distribution unit can, for example, clarify the specific types of revenue and the distribution method. For example, the distribution unit can clarify the specific types of revenue, such as streaming revenue and download revenue. The distribution unit can also automatically calculate the revenue distribution ratio based on the contract and distribute the revenue. For example, the distribution unit can distribute 50% of the revenue to the composer and lyricist, 30% to the original singer, and 20% to the singer who provided the AI-generated voice. Furthermore, the distribution unit can use AI to optimize the revenue distribution method. For example, the distribution unit can refer to past revenue distribution data and apply the optimal distribution method. This allows for accurate revenue distribution based on the contract. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input the revenue distribution method into a generating AI, and the generating AI can automatically distribute the revenue.

[0036] The data collection unit can analyze the past works of composers and lyricists and select the most suitable information collection method. For example, the data collection unit can analyze the past works of composers and lyricists and select the most suitable information collection method. For example, the data collection unit can analyze the past hit songs of a composer and collect information on similar musical styles. The data collection unit can also analyze the themes of the past lyrics of a lyricist and collect information on related themes. Furthermore, the data collection unit can analyze the past collaboration history of composers and lyricists and collect information on compatible artists. This allows for the selection of the most suitable information collection method by analyzing past works. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the past works of composers and lyricists into a generating AI, which can then select the most suitable information collection method.

[0037] The data collection unit can filter information based on the composer's or lyricist's current projects and areas of interest. For example, the data collection unit can prioritize collecting information on music genres related to the composer's current project. The data collection unit can also collect information on themes related to social issues that the lyricist is currently interested in. Furthermore, the data collection unit can collect information related to the instruments and technologies that the composer or lyricist is currently using. This allows the information to be filtered based on the current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on the composer's or lyricist's current projects and areas of interest into a generating AI, and the generating AI can filter the information.

[0038] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of composers and lyricists. For example, the data collection unit can prioritize the collection of highly relevant information by considering the geographical location of composers and lyricists. For example, the data collection unit can collect information related to the music scene in the area where the composer lives. The data collection unit can also collect information related to the culture and music of the region where the lyricist is traveling. Furthermore, the data collection unit can collect information related to the music of places that the composer or lyricist has visited in the past. This allows for the collection of highly relevant information by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location of composers and lyricists into a generating AI, which can then prioritize the collection of highly relevant information.

[0039] The data collection unit can analyze the social media activities of composers and lyricists and collect relevant information. For example, the data collection unit can collect information related to music shared by composers on social media. The data collection unit can also collect information related to themes mentioned by lyricists on social media. Furthermore, the data collection unit can collect information related to artists followed by composers and lyricists. This allows for the analysis of social media activities and the collection of relevant information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data of composers and lyricists into a generating AI, which can then collect relevant information.

[0040] The generation unit can improve the accuracy of its analysis by referencing past audio data when analyzing the original singer's voice data. For example, the generation unit can refer to data from the original singer's past hit songs to analyze the characteristics of the voice. For example, the generation unit can analyze audio data from the original singer's past hit songs and extract features such as voice tone, rhythm, and pronunciation. The generation unit can also refer to data from the original singer's live performances to analyze changes in the voice. For example, the generation unit can analyze audio data from the original singer's live performances and extract features such as changes in vocal volume and tempo. Furthermore, the generation unit can refer to data from different albums by the original singer to analyze the evolution of the voice. For example, the generation unit can analyze audio data from different albums by the original singer to understand the process of changes and evolution in the voice. This allows for improved analysis accuracy by referencing past audio data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past audio data of the original singer into a generation AI, which can then improve the analysis accuracy.

[0041] The generation unit can apply a learning algorithm according to different music genres when learning the characteristics of the original singer's voice. For example, when learning the voice of a pop singer, the generation unit can consider the rhythm and melody unique to pop music. Also, when learning the voice of a jazz singer, the generation unit can consider the improvisation and swing unique to jazz. Furthermore, when learning the voice of a classical singer, the generation unit can consider the vocal techniques and expressions unique to classical music. This improves learning accuracy by applying a learning algorithm according to different music genres. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the data of the original singer's voice into a generation AI, and the generation AI can apply a learning algorithm according to different music genres.

[0042] The generation unit can analyze the original singer's voice data while considering the singer's geographical background information. For example, the generation unit can consider the dialect and accent of the region where the original singer lives. The generation unit can also consider the musical styles of the regions that influenced the original singer. For example, the generation unit can consider the musical styles and rhythms of the regions that influenced the original singer. Furthermore, the generation unit can also consider the musical culture of the regions the original singer visited on tour. For example, the generation unit can consider the musical culture and influence of the regions visited on tour when analyzing the original singer's voice data. This improves the accuracy of the analysis by considering geographical background information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the original singer's geographical background information into a generation AI and have the generation AI perform the analysis.

[0043] The generation unit can improve its learning accuracy by referencing relevant music theory when learning the characteristics of the original singer's voice. For example, the generation unit can learn by referencing basic scales and chord progressions in music theory. The generation unit can also learn by referencing the relationship between rhythm and melody in music theory. For example, the generation unit can learn by referencing the relationship between rhythm and melody in music theory when learning the characteristics of the original singer's voice. Furthermore, the generation unit can learn by referencing harmony and counterpoint in music theory. For example, the generation unit can learn by referencing harmony and counterpoint in music theory when learning the characteristics of the original singer's voice. This allows for improved learning accuracy by referencing relevant music theory. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data of the original singer's voice into a generation AI, which can then use music theory to improve learning accuracy.

[0044] The contract generation unit can optimize its contract generation algorithm by referring to past contract data during contract generation. For example, the contract generation unit can generate optimal contract content by referring to past successful contract data. For example, the contract generation unit can analyze past successful contract data and generate optimal contract content. The contract generation unit can also generate contract content that avoids risks by referring to past failed contract data. For example, the contract generation unit can analyze past failed contract data and generate contract content that avoids risks. Furthermore, the contract generation unit can analyze past contract data and generate contract content based on trends. For example, the contract generation unit can analyze past contract data and generate contract content based on the latest trends. This allows the contract generation algorithm to be optimized by referring to past contract data. Some or all of the above processing in the contract generation unit may be performed using AI, for example, or without AI. For example, the contract generation unit can input past contract data into a generation AI, and the generation AI can optimize the contract generation algorithm.

[0045] The contract generation unit can customize contract terms according to different music industry regulations when generating contracts. For example, the contract generation unit can customize contract terms based on domestic music industry regulations. For example, the contract generation unit can adjust licensing and revenue sharing terms based on domestic music industry regulations. The contract generation unit can also customize contract terms based on international music industry regulations. For example, the contract generation unit can adjust contract terms based on international music industry regulations. Furthermore, the contract generation unit can also customize contract terms based on regulations for specific music genres. For example, the contract generation unit can adjust contract terms based on regulations for specific music genres. This allows for customization of contract terms according to different music industry regulations. Some or all of the above processing in the contract generation unit may be performed using AI, for example, or without AI. For example, the contract generation unit can input data on music industry regulations into a generation AI, and the generation AI can customize the contract terms.

[0046] The contract generation unit can adjust the contract terms when generating a contract, taking into account the singer's geographical background information. For example, the contract generation unit can adjust the contract terms based on the laws of the region where the singer lives. For example, the contract generation unit can adjust the terms of licensing and revenue sharing based on the laws of the region where the singer lives. The contract generation unit can also adjust the contract terms based on the regulations of the music industry in the region where the singer is active. For example, the contract generation unit can adjust the contract terms based on the regulations of the music industry in the region where the singer is active. Furthermore, the contract generation unit can adjust the contract terms based on the laws of the regions the singer visits on tour. For example, the contract generation unit can adjust the contract terms based on the laws of the regions the singer visits on tour. This allows the contract terms to be adjusted taking geographical background information into account. Some or all of the above processing in the contract generation unit may be performed using AI, for example, or not using AI. For example, the contract generation unit can input the singer's geographical background information into a generation AI, and the generation AI can adjust the contract terms.

[0047] The contract generation unit can improve the accuracy of the contract content by referring to relevant legal literature during contract generation. For example, the contract generation unit can update the contract content by referring to the latest legal literature. For example, the contract generation unit can update the contract content based on the latest laws by referring to the latest legal literature. The contract generation unit can also generate contract content that avoids risks by referring to past precedents. For example, the contract generation unit can generate contract content that avoids risks by referring to past precedents. Furthermore, the contract generation unit can improve the accuracy of the contract content by referring to expert opinions. For example, the contract generation unit can improve the accuracy of the contract content by referring to the opinions of legal experts. This allows for improvement of the accuracy of the contract content by referring to relevant legal literature. Some or all of the above processing in the contract generation unit may be performed using AI, for example, or without AI. For example, the contract generation unit can input relevant legal literature into a generation AI, and the generation AI can improve the accuracy of the contract content.

[0048] The distribution unit can optimize its distribution algorithm by referring to past revenue distribution data during distribution. For example, the distribution unit can refer to past successful revenue distribution data and apply the optimal distribution method. For example, the distribution unit can analyze past successful revenue distribution data and apply the optimal distribution method. The distribution unit can also refer to past unsuccessful revenue distribution data and apply a distribution method to mitigate risk. For example, the distribution unit can analyze past unsuccessful revenue distribution data and apply a distribution method to mitigate risk. Furthermore, the distribution unit can analyze past revenue distribution data and apply a distribution method based on trends. For example, the distribution unit can analyze past revenue distribution data and apply a distribution method based on the latest trends. This allows the distribution algorithm to be optimized by referring to past revenue distribution data. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input past revenue distribution data into a generating AI, and the generating AI can optimize the distribution algorithm.

[0049] The distribution unit can customize the distribution method according to different music industry regulations at the time of distribution. For example, the distribution unit can customize the distribution method based on domestic music industry regulations. For example, the distribution unit can adjust the revenue distribution conditions based on domestic music industry regulations. The distribution unit can also customize the distribution method based on international music industry regulations. For example, the distribution unit can adjust the distribution method based on international music industry regulations. Furthermore, the distribution unit can also customize the distribution method based on regulations for a specific music genre. For example, the distribution unit can adjust the distribution method based on regulations for a specific music genre. This allows the distribution method to be customized according to different music industry regulations. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input data on music industry regulations into a generating AI, and the generating AI can customize the distribution method.

[0050] The distribution unit can adjust the distribution method when distributing revenue, taking into account the singer's geographical background information. For example, the distribution unit can adjust the distribution method based on the laws of the region where the singer lives. For example, the distribution unit can adjust the terms of revenue distribution based on the laws of the region where the singer lives. The distribution unit can also adjust the distribution method based on the regulations of the music industry in the region where the singer is active. For example, the distribution unit can adjust the distribution method based on the regulations of the music industry in the region where the singer is active. Furthermore, the distribution unit can adjust the distribution method based on the laws of the regions the singer visits on tour. For example, the distribution unit can adjust the distribution method based on the laws of the regions the singer visits on tour. This allows the distribution method to be adjusted taking geographical background information into account. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the singer's geographical background information into a generating AI, and the generating AI can adjust the distribution method.

[0051] The distribution unit can improve the accuracy of its distribution method by referring to relevant economic data during distribution. For example, the distribution unit can adjust the ratio of profit distribution by referring to the latest economic data. For example, the distribution unit can adjust the ratio of profit distribution based on the latest economic conditions by referring to the latest economic data. The distribution unit can also apply a risk-averse distribution method by referring to historical economic data. For example, the distribution unit can apply a risk-averse distribution method by referring to historical economic data. Furthermore, the distribution unit can improve the accuracy of profit distribution by referring to expert opinions. For example, the distribution unit can improve the accuracy of profit distribution by referring to the opinions of economic experts. This allows the accuracy of the distribution method to be improved by referring to relevant economic data. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input relevant economic data into a generating AI, and the generating AI can improve the accuracy of the distribution method.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The data collection unit can analyze the user's musical preferences and determine the type of information to collect based on those preferences. For example, the data collection unit can prioritize collecting information on music genres and artists that the user likes. It can also analyze data on songs the user has previously played and collect information on similar songs. Furthermore, it can collect information on related artists and genres based on information about music events the user has attended. This allows the data collection unit to determine the type of information based on the user's musical preferences. Some or all of the above processing in the data collection unit may be performed using AI, or it may be performed without AI.

[0054] The generation unit can analyze the original singer's voice data while taking the singer's health condition into consideration. For example, the generation unit can exclude data from periods when the singer's throat was in poor condition and prioritize the analysis of data from the best period. Furthermore, if the original singer has a specific health problem, the generation unit can consider its impact during the analysis. Additionally, if the singer's voice characteristics change in response to fluctuations in their health, the generation unit can consider these fluctuations during the analysis. This improves the accuracy of the analysis by considering the singer's health condition. Some or all of the above-described processes in the generation unit may be performed using AI, or they may not.

[0055] The generation unit can improve the accuracy of its analysis by referencing past audio data when analyzing the original singer's voice data. For example, the generation unit can refer to data from the original singer's past hit songs to analyze the characteristics of the voice. For example, the generation unit can analyze audio data from the original singer's past hit songs and extract features such as voice tone, rhythm, and pronunciation. The generation unit can also refer to data from the original singer's live performances to analyze changes in the voice. For example, the generation unit can analyze audio data from the original singer's live performances and extract features such as changes in vocal volume and tempo. Furthermore, the generation unit can refer to data from different albums by the original singer to analyze the evolution of the voice. For example, the generation unit can analyze audio data from different albums by the original singer to understand the process of changes and evolution in the voice. This allows for improved analysis accuracy by referencing past audio data. Some or all of the above-described processes in the generation unit may be performed using AI or not.

[0056] The contract generation unit can optimize its contract generation algorithm by referring to past contract data during contract generation. For example, the contract generation unit can generate optimal contract terms by referring to past successful contract data. For example, the contract generation unit can analyze past successful contract data to generate optimal contract terms. The contract generation unit can also generate contract terms that mitigate risks by referring to past failed contract data. For example, the contract generation unit can analyze past failed contract data to generate contract terms that mitigate risks. Furthermore, the contract generation unit can analyze past contract data to generate contract terms based on trends. For example, the contract generation unit can analyze past contract data to generate contract terms based on the latest trends. This allows the contract generation algorithm to be optimized by referring to past contract data. Some or all of the above processes in the contract generation unit may be performed using AI or not.

[0057] The distribution unit can customize the distribution method according to different music industry regulations at the time of distribution. For example, the distribution unit can customize the distribution method based on domestic music industry regulations. For example, the distribution unit can adjust the terms of revenue distribution based on domestic music industry regulations. The distribution unit can also customize the distribution method based on international music industry regulations. For example, the distribution unit can adjust the distribution method based on international music industry regulations. Furthermore, the distribution unit can also customize the distribution method based on regulations for a specific music genre. For example, the distribution unit can adjust the distribution method based on regulations for a specific music genre. This allows the distribution method to be customized according to different music industry regulations. Some or all of the above processing in the distribution unit may be performed using AI or not.

[0058] The generation unit can apply learning algorithms according to different musical genres when learning the characteristics of the original singer's voice. For example, when learning the voice of a pop singer, the generation unit can consider the rhythm and melody specific to pop music. Similarly, when learning the voice of a jazz singer, it can consider the improvisation and swing specific to jazz. Furthermore, when learning the voice of a classical singer, it can consider the vocal techniques and expressions specific to classical music. This improves learning accuracy by applying learning algorithms according to different musical genres. Some or all of the above processing in the generation unit may be performed using AI, or it may be performed without AI.

[0059] The data collection unit can prioritize the collection of highly relevant information, taking into account the geographical location of composers and lyricists. For example, the data collection unit can collect information related to the music scene in the area where the composer lives. It can also collect information related to the culture and music of the region where the lyricist is traveling. Furthermore, the data collection unit can collect information related to the music of places that the composer or lyricist has visited in the past. This allows for the collection of highly relevant information while considering geographical location. Some or all of the processing described above in the data collection unit may be performed using AI or not.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit gathers information for composers and lyricists to create songs. This information includes audio data, lyrics, melodies, etc. The data collection unit can gather information through interviews, database searches, online surveys, etc. For example, they might analyze themes from previously created songs and lyrics to collect relevant information. Step 2: The generation unit uses AI to replicate the original singer's voice and record the song based on the information collected by the collection unit. The generation unit uses speech synthesis technology and machine learning algorithms to learn the characteristics of the original singer's voice and reproduce those characteristics. For example, it reproduces the tone, rhythm, and pronunciation of the original singer's voice. Step 3: The contract generation unit automatically generates a contract regarding the use of the AI-generated voice based on the sound source generated by the generation unit. The contract generation unit generates the specific details of the contract, such as the licensing agreement and the revenue sharing ratio. The contract generation algorithm can also be used to optimize the content of the contract. Step 4: The distribution unit distributes revenue based on the contracts generated by the contract generation unit. The distribution unit clarifies the specific types of revenue and distribution methods, automatically calculates the revenue distribution ratios based on the contracts, and distributes the revenue. AI can also be used to optimize the revenue distribution method.

[0062] (Example of form 2) The AI ​​composition platform according to an embodiment of the present invention is a system that allows singers to earn revenue even when using sound sources that utilize the singer's voice replicated using AI. In this AI composition platform, composers and lyricists create songs on the platform, the AI ​​replicates the original singer's voice, and the song is recorded using that voice. At this time, a contract regarding the use of the voice replicated by the AI ​​is automatically generated so that the copyright of the original singer's voice is protected. Furthermore, if the song is sold, the revenue is distributed to the composer, lyricist, original singer, and the singer who provided the voice replicated by the AI. This mechanism strengthens the copyright of the singer's voice and provides the original singer with opportunities for sustainable revenue generation. In addition, the commercial use of generative AI will increase and new markets will be created. For example, composers and lyricists can create songs using the tools provided on the platform. They can create melodies and lyrics for songs using composition tools and lyric writing tools. Next, the AI ​​replicates the original singer's voice and records the song using that voice. The AI ​​analyzes the data of the original singer's voice and replicates it. In this process, the AI ​​learns the characteristics of the original singer's voice and can reproduce them. For example, it can reproduce the tone, rhythm, and pronunciation of the original singer's voice. Furthermore, a contract regarding the use of the AI-replicated voice is automatically generated. This contract outlines the terms of use for the original singer's voice and how revenue will be shared. For example, it specifies the royalties for using the original singer's voice and the percentage of revenue sharing. Finally, when a song is sold, the revenue is distributed to the composer, lyricist, original singer, and the singer who provided the AI-replicated voice. In this case, the revenue distribution is done automatically based on the contract. For example, 50% of the revenue is distributed to the composer and lyricist, 30% to the original singer, and 20% to the singer who provided the AI-replicated voice. This mechanism strengthens the copyright of the singer's voice and provides the original singer with opportunities for sustainable revenue generation. It also increases the commercial use of generative AI and creates new markets. For example, even after the original singer retires, their voice can be used to create new songs and generate revenue. Moreover, using generative AI enables new forms of musical expression that were not possible with conventional methods.This means that even if an AI composition platform uses an AI-generated singer's voice in the audio, the singer can still earn revenue.

[0063] The AI ​​composition platform according to this embodiment comprises a collection unit, a generation unit, a contract generation unit, and a distribution unit. The collection unit collects information for composers and lyricists to create songs. This information includes, but is not limited to, audio data, lyrics, and melodies. The collection unit can collect information directly from composers and lyricists, for example, through interviews. The collection unit can also collect existing song data and lyric data through database searches. Furthermore, the collection unit can collect information on the latest music trends and popular music genres through online surveys. For example, the collection unit can analyze song data previously created by composers and lyricists to collect information on similar musical styles. The collection unit can also analyze the themes of lyrics previously written by lyricists to collect information on related themes. The generation unit uses AI to replicate the original singer's voice and record the song based on the information collected by the collection unit. The generation unit uses, for example, speech synthesis technology to analyze the original singer's voice data and replicate that voice. The generation unit can learn the characteristics of the original singer's voice and reproduce those characteristics. For example, the generation unit can reproduce the tone, rhythm, and pronunciation of the original singer's voice. For example, the generation unit can perform spectral analysis on the data of the original singer's voice and extract acoustic features. The generation unit can also use machine learning algorithms to learn the characteristics of the original singer's voice and reproduce those characteristics. Furthermore, based on the data of the original singer's voice, the generation unit can use speech synthesis technology to replicate the original singer's voice with high accuracy. The contract generation unit automatically generates a contract regarding the use of the voice replicated by the AI ​​based on the sound source generated by the generation unit. The contract generation unit can generate specific contract details, such as licensing and revenue sharing ratios. For example, the contract generation unit can use AI to automatically generate a contract regarding the use of the original singer's voice. For example, the contract generation unit can specify the usage fee and revenue sharing ratio of the original singer's voice. For example, the contract generation unit can optimize the content of the contract using a contract generation algorithm. The distribution unit distributes revenue based on the contract generated by the contract generation unit.The distribution unit can, for example, clarify the specific types of revenue and the distribution methods. The distribution unit can, for example, clarify the specific types of revenue, such as streaming revenue and download revenue. The distribution unit can, for example, automatically calculate the revenue distribution ratio based on the contract and distribute the revenue. The distribution unit can, for example, optimize the revenue distribution method using AI. As a result, the AI ​​composition platform according to the embodiment allows the singer to earn revenue even when using sound sources that utilize the singer's voice replicated using AI.

[0064] The data collection unit can collect information for composers and lyricists to create songs. For example, the unit can collect information directly from composers and lyricists through interviews. For instance, it can ask composers and lyricists questions about the song's theme, melody, and lyrics, and collect their answers. The unit can also collect existing song and lyric data through database searches. For example, it can search music databases to collect information about specific music genres and artists. Furthermore, the unit can collect information about the latest music trends and popular music genres through online surveys. For example, it can collect information about the latest music trends and popular songs through music streaming services and social media. This allows composers and lyricists to efficiently collect information for creating songs. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input interview audio data into a generating AI, which can then convert the interview content into text data.

[0065] The generation unit can analyze the original singer's voice data using AI and replicate that voice. For example, the generation unit can use speech synthesis technology to analyze the original singer's voice data and replicate that voice. For example, the generation unit can perform spectral analysis on the original singer's voice data and extract acoustic features. Furthermore, the generation unit can use machine learning algorithms to learn the characteristics of the original singer's voice and reproduce those characteristics. For example, the generation unit can reproduce the tone, rhythm, and pronunciation of the original singer's voice. In addition, the generation unit can replicate the original singer's voice with high accuracy using speech synthesis technology based on the original singer's voice data. This allows for high-precision replication of the original singer's voice. Some or all of the above-described processes in the generation unit may be performed using AI, or without AI. For example, the generation unit can input the original singer's voice data into a generation AI, which can then replicate the original singer's voice.

[0066] The generation unit can learn the characteristics of the original singer's voice and reproduce those characteristics. The generation unit can learn the characteristics of the original singer's voice, for example, using a machine learning algorithm. For example, the generation unit can learn characteristics such as the tone, rhythm, and pronunciation of the original singer's voice. The generation unit can also reproduce the characteristics of the original singer's voice using speech synthesis technology. For example, the generation unit can reproduce the timbre, pitch, and rhythm of the original singer's voice. Furthermore, the generation unit can faithfully reproduce the original singer's voice using speech synthesis technology based on the data of the original singer's voice. This allows for a faithful reproduction of the characteristics of the original singer's voice. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the data of the original singer's voice into a generation AI, which can learn the characteristics of the original singer's voice and reproduce those characteristics.

[0067] The contract generation unit can automatically generate contracts regarding the use of a replicated voice using AI. For example, the contract generation unit can automatically generate a contract regarding the use of the original singer's voice using AI. For example, the contract generation unit can generate specific contract details such as licensing and revenue sharing ratios. For example, the contract generation unit can specify royalties and revenue sharing ratios for the use of the original singer's voice. For example, the contract generation unit can optimize the contract content using a contract generation algorithm. This enables the automatic generation of contracts regarding the use of a replicated voice. Some or all of the above processing in the contract generation unit may be performed using AI, for example, or without AI. For example, the contract generation unit can input the contract details regarding the use of the original singer's voice into a generation AI, and the generation AI can automatically generate the contract details.

[0068] The contract generation unit can specify the royalties for using the original singer's voice and the revenue sharing ratio. For example, the contract generation unit can specify the royalties for using the original singer's voice and the revenue sharing ratio. For example, the contract generation unit can calculate the royalties for using the original singer's voice based on the number of plays or downloads. The contract generation unit can also determine the revenue sharing ratio based on contract terms and contributions. For example, the contract generation unit can distribute 50% of the revenue to the composer and lyricist, 30% to the original singer, and 20% to the singer who provided the AI-generated voice. This allows for a clear specification of the royalties for using the original singer's voice and the revenue sharing ratio. Some or all of the above processing in the contract generation unit may be performed using AI, for example, or not. For example, the contract generation unit can input the royalties for using the original singer's voice and the revenue sharing ratio into a generating AI, and the generating AI can automatically generate the contract details.

[0069] The distribution unit can distribute revenue based on the contract. The distribution unit can, for example, clarify the specific types of revenue and the distribution method. For example, the distribution unit can clarify the specific types of revenue, such as streaming revenue and download revenue. The distribution unit can also automatically calculate the revenue distribution ratio based on the contract and distribute the revenue. For example, the distribution unit can distribute 50% of the revenue to the composer and lyricist, 30% to the original singer, and 20% to the singer who provided the AI-generated voice. Furthermore, the distribution unit can use AI to optimize the revenue distribution method. For example, the distribution unit can refer to past revenue distribution data and apply the optimal distribution method. This allows for accurate revenue distribution based on the contract. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input the revenue distribution method into a generating AI, and the generating AI can automatically distribute the revenue.

[0070] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting information on relaxing music genres. If the user is excited, the data collection unit can prioritize collecting information on energetic music genres. Furthermore, if the user is sad, the data collection unit can prioritize collecting information on soothing music genres. This allows the data collection unit to prioritize information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI, have the generative AI estimate the emotions, and determine the priority of information based on the results.

[0071] The data collection unit can analyze the past works of composers and lyricists and select the most suitable information collection method. For example, the data collection unit can analyze the past works of composers and lyricists and select the most suitable information collection method. For example, the data collection unit can analyze the past hit songs of a composer and collect information on similar musical styles. The data collection unit can also analyze the themes of the past lyrics of a lyricist and collect information on related themes. Furthermore, the data collection unit can analyze the past collaboration history of composers and lyricists and collect information on compatible artists. This allows for the selection of the most suitable information collection method by analyzing past works. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the past works of composers and lyricists into a generating AI, which can then select the most suitable information collection method.

[0072] The data collection unit can filter information based on the composer's or lyricist's current projects and areas of interest. For example, the data collection unit can prioritize collecting information on music genres related to the composer's current project. The data collection unit can also collect information on themes related to social issues that the lyricist is currently interested in. Furthermore, the data collection unit can collect information related to the instruments and technologies that the composer or lyricist is currently using. This allows the information to be filtered based on the current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on the composer's or lyricist's current projects and areas of interest into a generating AI, and the generating AI can filter the information.

[0073] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, the data collection unit can collect information on relaxing music when the user is relaxed at night. It can also collect information on energetic music when the user is active during the day. Furthermore, it can collect information on music that resonates with the user's emotions immediately after an emotional event. This allows the timing of information collection to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the timing of information collection can be adjusted based on the results.

[0074] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of composers and lyricists. For example, the data collection unit can prioritize the collection of highly relevant information by considering the geographical location of composers and lyricists. For example, the data collection unit can collect information related to the music scene in the area where the composer lives. The data collection unit can also collect information related to the culture and music of the region where the lyricist is traveling. Furthermore, the data collection unit can collect information related to the music of places that the composer or lyricist has visited in the past. This allows for the collection of highly relevant information by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location of composers and lyricists into a generating AI, which can then prioritize the collection of highly relevant information.

[0075] The data collection unit can analyze the social media activities of composers and lyricists and collect relevant information. For example, the data collection unit can collect information related to music shared by composers on social media. The data collection unit can also collect information related to themes mentioned by lyricists on social media. Furthermore, the data collection unit can collect information related to artists followed by composers and lyricists. This allows for the analysis of social media activities and the collection of relevant information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data of composers and lyricists into a generating AI, which can then collect relevant information.

[0076] The generation unit can estimate the user's emotions and adjust the expression of the generated sound source based on the estimated user emotions. For example, the generation unit can generate a sound source with a calm tone when the user is relaxed. It can also generate a sound source with an energetic tone when the user is excited. Furthermore, it can generate a sound source with a soothing tone when the user is sad. This allows the expression of the sound source to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into a generation AI, have the generation AI estimate the emotions, and adjust the expression of the sound source based on the result.

[0077] The generation unit can improve the accuracy of its analysis by referencing past audio data when analyzing the original singer's voice data. For example, the generation unit can refer to data from the original singer's past hit songs to analyze the characteristics of the voice. For example, the generation unit can analyze audio data from the original singer's past hit songs and extract features such as voice tone, rhythm, and pronunciation. The generation unit can also refer to data from the original singer's live performances to analyze changes in the voice. For example, the generation unit can analyze audio data from the original singer's live performances and extract features such as changes in vocal volume and tempo. Furthermore, the generation unit can refer to data from different albums by the original singer to analyze the evolution of the voice. For example, the generation unit can analyze audio data from different albums by the original singer to understand the process of changes and evolution in the voice. This allows for improved analysis accuracy by referencing past audio data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past audio data of the original singer into a generation AI, which can then improve the analysis accuracy.

[0078] The generation unit can apply a learning algorithm according to different music genres when learning the characteristics of the original singer's voice. For example, when learning the voice of a pop singer, the generation unit can consider the rhythm and melody unique to pop music. Also, when learning the voice of a jazz singer, the generation unit can consider the improvisation and swing unique to jazz. Furthermore, when learning the voice of a classical singer, the generation unit can consider the vocal techniques and expressions unique to classical music. This improves learning accuracy by applying a learning algorithm according to different music genres. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the data of the original singer's voice into a generation AI, and the generation AI can apply a learning algorithm according to different music genres.

[0079] The generation unit can estimate the user's emotions and adjust the length of the generated audio based on the estimated emotions. For example, the generation unit can generate a longer audio if the user is relaxed. It can also generate a shorter audio if the user is in a hurry. Furthermore, it can generate an audio of an appropriate length if the user is focused. This allows the length of the audio to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input user emotion data into a generation AI, the generation AI can estimate the emotions, and the length of the audio can be adjusted based on the result.

[0080] The generation unit can analyze the original singer's voice data while considering the singer's geographical background information. For example, the generation unit can consider the dialect and accent of the region where the original singer lives. The generation unit can also consider the musical styles of the regions that influenced the original singer. For example, the generation unit can consider the musical styles and rhythms of the regions that influenced the original singer. Furthermore, the generation unit can also consider the musical culture of the regions the original singer visited on tour. For example, the generation unit can consider the musical culture and influence of the regions visited on tour when analyzing the original singer's voice data. This improves the accuracy of the analysis by considering geographical background information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the original singer's geographical background information into a generation AI and have the generation AI perform the analysis.

[0081] The generation unit can improve its learning accuracy by referencing relevant music theory when learning the characteristics of the original singer's voice. For example, the generation unit can learn by referencing basic scales and chord progressions in music theory. The generation unit can also learn by referencing the relationship between rhythm and melody in music theory. For example, the generation unit can learn by referencing the relationship between rhythm and melody in music theory when learning the characteristics of the original singer's voice. Furthermore, the generation unit can learn by referencing harmony and counterpoint in music theory. For example, the generation unit can learn by referencing harmony and counterpoint in music theory when learning the characteristics of the original singer's voice. This allows for improved learning accuracy by referencing relevant music theory. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data of the original singer's voice into a generation AI, which can then use music theory to improve learning accuracy.

[0082] The contract generation unit can estimate the user's emotions and adjust the way the contract content is expressed based on the estimated emotions. For example, if the user is nervous, the contract generation unit can provide simple and clear contract content. If the user is relaxed, the contract generation unit can provide detailed contract content. Furthermore, if the user is in a hurry, the contract generation unit can provide concise contract content. This allows the way the contract content is expressed to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the contract generation unit may be performed using AI or not using AI. For example, the contract generation unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the way the contract content is expressed based on the result.

[0083] The contract generation unit can optimize its contract generation algorithm by referring to past contract data during contract generation. For example, the contract generation unit can generate optimal contract content by referring to past successful contract data. For example, the contract generation unit can analyze past successful contract data and generate optimal contract content. The contract generation unit can also generate contract content that avoids risks by referring to past failed contract data. For example, the contract generation unit can analyze past failed contract data and generate contract content that avoids risks. Furthermore, the contract generation unit can analyze past contract data and generate contract content based on trends. For example, the contract generation unit can analyze past contract data and generate contract content based on the latest trends. This allows the contract generation algorithm to be optimized by referring to past contract data. Some or all of the above processing in the contract generation unit may be performed using AI, for example, or without AI. For example, the contract generation unit can input past contract data into a generation AI, and the generation AI can optimize the contract generation algorithm.

[0084] The contract generation unit can customize contract terms according to different music industry regulations when generating contracts. For example, the contract generation unit can customize contract terms based on domestic music industry regulations. For example, the contract generation unit can adjust licensing and revenue sharing terms based on domestic music industry regulations. The contract generation unit can also customize contract terms based on international music industry regulations. For example, the contract generation unit can adjust contract terms based on international music industry regulations. Furthermore, the contract generation unit can also customize contract terms based on regulations for specific music genres. For example, the contract generation unit can adjust contract terms based on regulations for specific music genres. This allows for customization of contract terms according to different music industry regulations. Some or all of the above processing in the contract generation unit may be performed using AI, for example, or without AI. For example, the contract generation unit can input data on music industry regulations into a generation AI, and the generation AI can customize the contract terms.

[0085] The contract generation unit can estimate the user's emotions and determine contract priorities based on the estimated emotions. For example, if the user is stressed, the contract generation unit can prioritize generating important contracts. If the user is relaxed, the contract generation unit can prioritize generating detailed contracts. Furthermore, if the user is in a hurry, the contract generation unit can prioritize generating contracts that can be generated quickly. This allows for the determination of contract priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the contract generation unit may be performed using AI or not. For example, the contract generation unit can input user emotion data into a generation AI, the generation AI can estimate the emotions, and the contract priorities can be determined based on the results.

[0086] The contract generation unit can adjust the contract terms when generating a contract, taking into account the singer's geographical background information. For example, the contract generation unit can adjust the contract terms based on the laws of the region where the singer lives. For example, the contract generation unit can adjust the terms of licensing and revenue sharing based on the laws of the region where the singer lives. The contract generation unit can also adjust the contract terms based on the regulations of the music industry in the region where the singer is active. For example, the contract generation unit can adjust the contract terms based on the regulations of the music industry in the region where the singer is active. Furthermore, the contract generation unit can adjust the contract terms based on the laws of the regions the singer visits on tour. For example, the contract generation unit can adjust the contract terms based on the laws of the regions the singer visits on tour. This allows the contract terms to be adjusted taking geographical background information into account. Some or all of the above processing in the contract generation unit may be performed using AI, for example, or not using AI. For example, the contract generation unit can input the singer's geographical background information into a generation AI, and the generation AI can adjust the contract terms.

[0087] The contract generation unit can improve the accuracy of the contract content by referring to relevant legal literature during contract generation. For example, the contract generation unit can update the contract content by referring to the latest legal literature. For example, the contract generation unit can update the contract content based on the latest laws by referring to the latest legal literature. The contract generation unit can also generate contract content that avoids risks by referring to past precedents. For example, the contract generation unit can generate contract content that avoids risks by referring to past precedents. Furthermore, the contract generation unit can improve the accuracy of the contract content by referring to expert opinions. For example, the contract generation unit can improve the accuracy of the contract content by referring to the opinions of legal experts. This allows for improvement of the accuracy of the contract content by referring to relevant legal literature. Some or all of the above processing in the contract generation unit may be performed using AI, for example, or without AI. For example, the contract generation unit can input relevant legal literature into a generation AI, and the generation AI can improve the accuracy of the contract content.

[0088] The distribution unit can estimate the user's emotions and adjust the revenue distribution method based on the estimated user emotions. For example, the distribution unit can provide a detailed revenue distribution explanation if the user is relaxed. It can also provide a simple and clear revenue distribution explanation if the user is stressed. Furthermore, the distribution unit can distribute revenue quickly if the user is in a hurry. This allows the revenue distribution method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the distribution unit may be performed using AI or not using AI. For example, the distribution unit can input user emotion data into a generative AI, have the generative AI estimate the emotions, and adjust the revenue distribution method based on the result.

[0089] The distribution unit can optimize its distribution algorithm by referring to past revenue distribution data during distribution. For example, the distribution unit can refer to past successful revenue distribution data and apply the optimal distribution method. For example, the distribution unit can analyze past successful revenue distribution data and apply the optimal distribution method. The distribution unit can also refer to past unsuccessful revenue distribution data and apply a distribution method to mitigate risk. For example, the distribution unit can analyze past unsuccessful revenue distribution data and apply a distribution method to mitigate risk. Furthermore, the distribution unit can analyze past revenue distribution data and apply a distribution method based on trends. For example, the distribution unit can analyze past revenue distribution data and apply a distribution method based on the latest trends. This allows the distribution algorithm to be optimized by referring to past revenue distribution data. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input past revenue distribution data into a generating AI, and the generating AI can optimize the distribution algorithm.

[0090] The distribution unit can customize the distribution method according to different music industry regulations at the time of distribution. For example, the distribution unit can customize the distribution method based on domestic music industry regulations. For example, the distribution unit can adjust the revenue distribution conditions based on domestic music industry regulations. The distribution unit can also customize the distribution method based on international music industry regulations. For example, the distribution unit can adjust the distribution method based on international music industry regulations. Furthermore, the distribution unit can also customize the distribution method based on regulations for a specific music genre. For example, the distribution unit can adjust the distribution method based on regulations for a specific music genre. This allows the distribution method to be customized according to different music industry regulations. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input data on music industry regulations into a generating AI, and the generating AI can customize the distribution method.

[0091] The distribution unit can estimate the user's emotions and determine the priority of revenue distribution based on the estimated user emotions. For example, the distribution unit can prioritize important revenue distributions if the user is stressed. It can also prioritize detailed revenue distributions if the user is relaxed. Furthermore, it can distribute revenues quickly if the user is in a hurry. This allows the distribution unit to determine the priority of revenue distribution based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the distribution unit may be performed using AI or not using AI. For example, the distribution unit can input user emotion data into a generative AI, have the generative AI estimate the emotions, and determine the priority of revenue distribution based on the results.

[0092] The distribution unit can adjust the distribution method when distributing revenue, taking into account the singer's geographical background information. For example, the distribution unit can adjust the distribution method based on the laws of the region where the singer lives. For example, the distribution unit can adjust the terms of revenue distribution based on the laws of the region where the singer lives. The distribution unit can also adjust the distribution method based on the regulations of the music industry in the region where the singer is active. For example, the distribution unit can adjust the distribution method based on the regulations of the music industry in the region where the singer is active. Furthermore, the distribution unit can adjust the distribution method based on the laws of the regions the singer visits on tour. For example, the distribution unit can adjust the distribution method based on the laws of the regions the singer visits on tour. This allows the distribution method to be adjusted taking geographical background information into account. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the singer's geographical background information into a generating AI, and the generating AI can adjust the distribution method.

[0093] The distribution unit can improve the accuracy of its distribution method by referring to relevant economic data during distribution. For example, the distribution unit can adjust the ratio of profit distribution by referring to the latest economic data. For example, the distribution unit can adjust the ratio of profit distribution based on the latest economic conditions by referring to the latest economic data. The distribution unit can also apply a risk-averse distribution method by referring to historical economic data. For example, the distribution unit can apply a risk-averse distribution method by referring to historical economic data. Furthermore, the distribution unit can improve the accuracy of profit distribution by referring to expert opinions. For example, the distribution unit can improve the accuracy of profit distribution by referring to the opinions of economic experts. This allows the accuracy of the distribution method to be improved by referring to relevant economic data. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input relevant economic data into a generating AI, and the generating AI can improve the accuracy of the distribution method. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, generation unit, contract generation unit, and distribution unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects information for composers and lyricists to create music. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and uses AI to replicate the original singer's voice and record the music. The contract generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and automatically generates a contract regarding the use of the voice replicated by the AI. The distribution unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and distributes the revenue. The collection unit, for example, estimates the user's emotions and determines the priority of information to collect based on the estimated user emotions. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, generation unit, contract generation unit, and distribution unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information for composers and lyricists to create music. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and uses AI to replicate the original singer's voice and record the music. The contract generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and automatically generates a contract regarding the use of the voice replicated by the AI. The distribution unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and distributes the revenue. The collection unit estimates the user's emotions and determines the priority of information to collect based on the estimated user emotions. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, generation unit, contract generation unit, and distribution unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects information for composers and lyricists to create songs. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and uses AI to replicate the original singer's voice and record the song. The contract generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and automatically generates a contract regarding the use of the voice replicated by the AI. The distribution unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and distributes the revenue. The collection unit estimates the user's emotions and determines the priority of information to collect based on the estimated user emotions. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, generation unit, contract generation unit, and distribution unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects information for composers and lyricists to create songs. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and uses AI to replicate the original singer's voice and record the song. The contract generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and automatically generates a contract regarding the use of the voice replicated by the AI. The distribution unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and distributes the revenue. The collection unit, for example, estimates the user's emotions and determines the priority of information to collect based on the estimated user emotions.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The data collection unit can analyze the user's musical preferences and determine the type of information to collect based on those preferences. For example, the data collection unit can prioritize collecting information on music genres and artists that the user likes. It can also analyze data on songs the user has previously played and collect information on similar songs. Furthermore, it can collect information on related artists and genres based on information about music events the user has attended. This allows the data collection unit to determine the type of information based on the user's musical preferences. Some or all of the above processing in the data collection unit may be performed using AI, or it may be performed without AI.

[0096] The generation unit can analyze the original singer's voice data while taking the singer's health condition into consideration. For example, the generation unit can exclude data from periods when the singer's throat was in poor condition and prioritize the analysis of data from the best period. Furthermore, if the original singer has a specific health problem, the generation unit can consider its impact during the analysis. Additionally, if the singer's voice characteristics change in response to fluctuations in their health, the generation unit can consider these fluctuations during the analysis. This improves the accuracy of the analysis by considering the singer's health condition. Some or all of the above-described processes in the generation unit may be performed using AI, or they may not.

[0097] The contract generation unit can estimate the user's emotions and adjust the way the contract content is presented based on the estimated emotions. For example, if the user is nervous, the contract generation unit can provide a simple and clear contract. If the user is relaxed, it can provide a detailed contract. Furthermore, if the user is in a hurry, it can provide a contract that gets straight to the point. This allows the way the contract content is presented to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the contract generation unit may be performed using AI or not.

[0098] The distribution unit can estimate the user's emotions and adjust the revenue distribution method based on the estimated emotions. For example, if the user is relaxed, the distribution unit can provide a detailed explanation of revenue distribution. If the user is stressed, the distribution unit can provide a simple and clear explanation of revenue distribution. Furthermore, if the user is in a hurry, the distribution unit can distribute revenue quickly. This allows the revenue distribution method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the distribution unit may be performed using AI or not.

[0099] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting information on relaxing music genres. Similarly, if the user is excited, the data collection unit can prioritize collecting information on energetic music genres. Furthermore, if the user is sad, the data collection unit can prioritize collecting information on soothing music genres. This allows for the prioritization of information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not.

[0100] The generation unit can improve the accuracy of its analysis by referencing past audio data when analyzing the original singer's voice data. For example, the generation unit can refer to data from the original singer's past hit songs to analyze the characteristics of the voice. For example, the generation unit can analyze audio data from the original singer's past hit songs and extract features such as voice tone, rhythm, and pronunciation. The generation unit can also refer to data from the original singer's live performances to analyze changes in the voice. For example, the generation unit can analyze audio data from the original singer's live performances and extract features such as changes in vocal volume and tempo. Furthermore, the generation unit can refer to data from different albums by the original singer to analyze the evolution of the voice. For example, the generation unit can analyze audio data from different albums by the original singer to understand the process of changes and evolution in the voice. This allows for improved analysis accuracy by referencing past audio data. Some or all of the above-described processes in the generation unit may be performed using AI or not.

[0101] The contract generation unit can optimize its contract generation algorithm by referring to past contract data during contract generation. For example, the contract generation unit can generate optimal contract terms by referring to past successful contract data. For example, the contract generation unit can analyze past successful contract data to generate optimal contract terms. The contract generation unit can also generate contract terms that mitigate risks by referring to past failed contract data. For example, the contract generation unit can analyze past failed contract data to generate contract terms that mitigate risks. Furthermore, the contract generation unit can analyze past contract data to generate contract terms based on trends. For example, the contract generation unit can analyze past contract data to generate contract terms based on the latest trends. This allows the contract generation algorithm to be optimized by referring to past contract data. Some or all of the above processes in the contract generation unit may be performed using AI or not.

[0102] The distribution unit can customize the distribution method according to different music industry regulations at the time of distribution. For example, the distribution unit can customize the distribution method based on domestic music industry regulations. For example, the distribution unit can adjust the terms of revenue distribution based on domestic music industry regulations. The distribution unit can also customize the distribution method based on international music industry regulations. For example, the distribution unit can adjust the distribution method based on international music industry regulations. Furthermore, the distribution unit can also customize the distribution method based on regulations for a specific music genre. For example, the distribution unit can adjust the distribution method based on regulations for a specific music genre. This allows the distribution method to be customized according to different music industry regulations. Some or all of the above processing in the distribution unit may be performed using AI or not.

[0103] The generation unit can apply learning algorithms according to different musical genres when learning the characteristics of the original singer's voice. For example, when learning the voice of a pop singer, the generation unit can consider the rhythm and melody specific to pop music. Similarly, when learning the voice of a jazz singer, it can consider the improvisation and swing specific to jazz. Furthermore, when learning the voice of a classical singer, it can consider the vocal techniques and expressions specific to classical music. This improves learning accuracy by applying learning algorithms according to different musical genres. Some or all of the above processing in the generation unit may be performed using AI, or it may be performed without AI.

[0104] The data collection unit can prioritize the collection of highly relevant information, taking into account the geographical location of composers and lyricists. For example, the data collection unit can collect information related to the music scene in the area where the composer lives. It can also collect information related to the culture and music of the region where the lyricist is traveling. Furthermore, the data collection unit can collect information related to the music of places that the composer or lyricist has visited in the past. This allows for the collection of highly relevant information while considering geographical location. Some or all of the processing described above in the data collection unit may be performed using AI or not.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The data collection unit gathers information for composers and lyricists to create songs. This information includes audio data, lyrics, melodies, etc. The data collection unit can gather information through interviews, database searches, online surveys, etc. For example, they might analyze themes from previously created songs and lyrics to collect relevant information. Step 2: The generation unit uses AI to replicate the original singer's voice and record the song based on the information collected by the collection unit. The generation unit uses speech synthesis technology and machine learning algorithms to learn the characteristics of the original singer's voice and reproduce those characteristics. For example, it reproduces the tone, rhythm, and pronunciation of the original singer's voice. Step 3: The contract generation unit automatically generates a contract regarding the use of the AI-generated voice based on the sound source generated by the generation unit. The contract generation unit generates the specific details of the contract, such as the licensing agreement and the revenue sharing ratio. The contract generation algorithm can also be used to optimize the content of the contract. Step 4: The distribution unit distributes revenue based on the contracts generated by the contract generation unit. The distribution unit clarifies the specific types of revenue and distribution methods, automatically calculates the revenue distribution ratios based on the contracts, and distributes the revenue. AI can also be used to optimize the revenue distribution method.

[0107] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0108] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0109] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0110] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] As shown in Figure 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.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0118] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, 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 specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, 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 specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0151] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, 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 specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0160] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0162] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0163] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0164] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0168] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0169] 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.

[0170] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0171] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0172] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0173] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0175] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0176] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0177] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0178] [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects information for composers or lyricists to create music, Based on the information collected by the aforementioned collection unit, the generation unit uses AI to replicate the original singer's voice and record the song. A contract generation unit automatically generates a contract regarding the use of the voice replicated by the AI ​​based on the sound source generated by the generation unit, The system comprises a distribution unit that distributes revenue based on contracts generated by the contract generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Composers and lyricists gather information to create songs. The system according to feature 1.

3. The generating unit is Using AI, the original singer's voice data is analyzed, and that voice is replicated. The system according to feature 1.

4. The generating unit is It learns the vocal characteristics of the original singer and reproduces those characteristics. The system according to feature 1.

5. The aforementioned contract generation unit, Automatically generate contracts regarding the use of voices replicated using AI. The system according to feature 1.

6. The aforementioned contract generation unit, The original singer's voice usage fees and revenue sharing ratios should be listed. The system according to feature 1.

7. The aforementioned distribution unit is Revenue will be distributed according to the contract. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze the past works of composers and lyricists and select appropriate information gathering methods. The system according to feature 1.

10. The aforementioned collection unit is Filter based on the composer's or lyricist's current projects and areas of interest. The system according to feature 1.

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