system

Abstract

The system according to this embodiment aims to enable users to generate and produce original AI wrappers. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a control unit. The reception unit receives input from the user. The generation unit generates the personality, music, and appearance of an AI rapper based on the information received by the reception unit. The control unit uses the AI ​​rapper generated by the generation unit for battles or music production.

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 conventional technology, it is difficult for a user to generate and produce an original AI wrapper, and there is room for improvement in providing a new form of entertainment.

[0005] The system according to the embodiment aims to enable a user to generate and produce an original AI wrapper.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, and a control unit. The reception unit receives input from the user. The generation unit generates the personality, music, and appearance of an AI rapper based on the information received by the reception unit. The control unit uses the AI ​​rapper generated by the generation unit for battles or music production. [Effects of the Invention]

[0007] The system according to this embodiment allows users to generate and produce original AI wrappers. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 music entertainment platform according to an embodiment of the present invention is a system that enables users to generate and produce original AI rappers. This system allows users to generate the personality, music, and appearance of an AI rapper, and to nurture them through battles with other AI rappers and music production. Users can level up their AI rappers using items supervised by real rappers or famous AI rappers. Co-production is also possible through the DAO community. A distinctive feature is the adoption of a "Produce To Earn" model, enabling monetization by secondary distribution of popular AI rappers as NFTs. The business model consists of NFT sales, item sales, and monthly subscriptions, aiming for profitability in the third year. Technically, multiple AIs are packaged to constitute an AI rapper, and DAO operation is conducted using smart contracts. Furthermore, considering copyright issues, the service is positioned as an AI rapper creation support service, and measures are taken to avoid legal risks, such as limiting music listening time and issuing disclaimers that explicitly designate the user as the author. For example, AI rappers compete against each other on a battlefield, with the winner determined by audience votes. Next, elements of oneself and one's opponent are read to generate music. Furthermore, AI rappers can be leveled up using items. It's also possible to form a fandom and co-produce. Finally, AI rappers who consistently win battles gain popularity and can be monetized through secondary marketplaces as NFTs. This allows music entertainment platforms to enable users to create and produce their own original AI rappers.

[0029] The music entertainment platform according to this embodiment comprises a reception unit, a generation unit, and a control unit. The reception unit receives input from the user. User input includes, but is not limited to, text input, voice input, and image input. The reception unit provides, for example, an interface for receiving text input. The reception unit may also be equipped with a microphone for receiving voice input. Furthermore, the reception unit may be equipped with a camera for receiving image input. The generation unit generates the personality, music, and appearance of an AI rapper based on the information received by the reception unit. For example, the generation unit considers personality traits, speaking style, behavioral patterns, etc., in order to generate the AI ​​rapper's personality. The generation unit may also consider beats, lyrics, melodies, etc., in order to generate the AI ​​rapper's music. Furthermore, the generation unit may also consider clothing, facial features, body type, etc., in order to generate the AI ​​rapper's appearance. The control unit uses the AI ​​rapper generated by the generation unit for battles and music production. For example, the control unit sets the rules for battles and provides a venue for AI rappers to compete against each other. Furthermore, the control unit can manage the music production process and provide an environment for the AI ​​rapper to create music. This enables the music entertainment platform according to the embodiment to allow users to generate and produce their own original AI rappers.

[0030] The reception unit receives input from the user. User input includes, but is not limited to, text input, voice input, and image input. The reception unit provides, for example, an interface for receiving text input. Specifically, it allows users to input text using a keyboard or touchscreen. The reception unit may also be equipped with a microphone for receiving voice input. In the case of voice input, the user can give instructions to the system by speaking into the microphone. Speech recognition technology is used to convert the user's voice into text, and natural language processing technology is used to analyze the content. Furthermore, the reception unit may be equipped with a camera for receiving image input. In the case of image input, the user can provide images or photos to the system through the camera. Image recognition technology is used to extract and analyze the necessary information from the provided images. In this way, the reception unit supports diverse input methods, allowing users to interact with the system intuitively and flexibly. Furthermore, the reception unit plays a role in centrally managing this input data and appropriately handing it over to the generation unit and control unit. For example, if the user specifies the personality and appearance characteristics of an AI wrapper in text, that information is sent to the generation unit and used to generate the AI ​​wrapper. Similarly, voice and image inputs are converted into appropriate formats and provided to the generation unit. This allows the reception unit to respond to diverse user needs and improve the overall operability and convenience of the system.

[0031] The generation unit generates the personality, music, and appearance of the AI ​​rapper based on the information received by the reception unit. Specifically, to generate the AI ​​rapper's personality, it considers personality traits, speaking style, behavioral patterns, etc. For example, it determines what kind of personality the AI ​​rapper will have based on the personality traits specified by the user. Personality traits include, for example, cheerful, calm, and passionate. Regarding speaking style, it determines what kind of tone and word choice the AI ​​rapper will use based on the user's specifications. Regarding behavioral patterns, it sets how the AI ​​rapper will behave in different situations. The generation unit can also consider beats, lyrics, melodies, etc., to generate the AI ​​rapper's music. For example, it determines the rhythm of the AI ​​rapper's music based on the beat specified by the user. Regarding lyrics, it determines what kind of lyrics the AI ​​rapper will sing based on the user's specifications. Regarding melody, it determines the melody of the AI ​​rapper's music based on the user's specifications. Furthermore, the generation unit can also consider clothing, facial features, body type, etc., to generate the AI ​​rapper's appearance. For example, it determines what kind of clothes the AI ​​rapper will wear based on the clothing specified by the user. Regarding facial features, the AI ​​rapper's face shape and expression are determined based on user specifications. Similarly, the AI ​​rapper's body size and shape are determined based on user specifications. This allows the generation unit to create original AI rappers based on user specifications. Furthermore, the generation unit can save the generated AI rapper data and reuse it as needed. For example, users can later edit or share the AI ​​rappers they have generated with other users. This provides the generation unit with an environment where users can freely customize and enjoy AI rappers.

[0032] The control unit uses the AI ​​rappers generated by the generation unit for battles and music production. Specifically, it sets the rules for battles and provides a platform for AI rappers to compete against each other. For example, it sets the format, duration, and evaluation criteria for battles, and the AI ​​rappers compete according to those rules. Battle evaluations are conducted, for example, by audience votes or expert judging. The control unit can also manage the music production process and provide an environment for AI rappers to create music. Specifically, it sets the theme, genre, and production schedule for songs, and the AI ​​rappers create songs according to those instructions. The control unit monitors the production process and can make corrections or provide advice as needed. Furthermore, the control unit saves the generated songs and battle results so that users can review them later. For example, users can review the results of past battles or play generated songs. The control unit can also collect user feedback and use it to improve the system. For example, it collects user opinions on battles and music production and uses that feedback to review the system's functions and rules. This allows the control unit to optimize the system to make it more enjoyable for users and improve the entertainment experience. In addition, the control unit also provides functions to facilitate collaboration with other users. For example, multiple users can collaboratively produce an AI rapper or participate in battles. This allows the control unit to facilitate interaction among users and support community building.

[0033] The music entertainment platform includes a level-up section that allows users to level up AI rappers using specific items. The level-up section can, for example, use virtual currency to level up AI rappers. For instance, a user can purchase specific items using virtual currency and then use those items to level up the AI ​​rapper. The level-up section can also level up AI rappers using points. For example, a user can earn points through battles and music production and then use those points to level up the AI ​​rapper. Furthermore, the level-up section can also level up AI rappers using specific objects. For example, a user can collect specific objects and then use those objects to level up the AI ​​rapper. This enables the AI ​​rapper to level up. Some or all of the above processes in the level-up section may be performed using AI, or not. For example, the level-up section can input data on items purchased by the user into a generating AI and have the generating AI perform the level-up process.

[0034] The music entertainment platform includes a fandom section for forming fandoms and co-producing music. The fandom section can, for example, set community size limits and clearly define participation requirements. For instance, it might form a fandom if a certain number of participants are reached. It could also restrict participation to users who meet specific criteria, such as only users who own certain items. Furthermore, the fandom section can define activities and provide an environment for co-producing music. For example, it could regularly host battles or music production events, allowing participants to co-produce. This enables the formation of a fandom and co-production. Some or all of the above processes within the fandom section may be performed using AI, or not. For example, the fandom section could input community participant data into a generating AI, allowing the AI ​​to execute the fandom formation and co-production processes.

[0035] The music entertainment platform includes a ranking section for reflecting battle results in the rankings. The ranking section can, for example, set evaluation criteria for determining the winner and loser of a battle. For example, the ranking section can determine the winner and loser based on audience voting results. The ranking section can also implement a point system and award points according to the battle results. For example, the ranking section can award higher points to winning AI rappers and lower points to losing AI rappers. Furthermore, the ranking section can build a system for reflecting battle results in the rankings. For example, the ranking section can reflect battle results in the rankings in real time, allowing users to check the rankings. This makes it possible to reflect battle results in the rankings. Some or all of the above processing in the ranking section may be performed using AI, for example, or not using AI. For example, the ranking section can input battle result data into a generating AI and have the generating AI update the rankings.

[0036] The music entertainment platform includes a monetization unit for secondary distribution of AI rappers as NFTs. The monetization unit can, for example, provide a trading platform, allowing users to buy and sell AI rappers as NFTs. For instance, the monetization unit could partner with a specific trading platform, allowing users to list AI rappers as NFTs for purchase by other users. The monetization unit can also set transaction fees and collect them upon successful completion of a transaction. For example, the monetization unit could collect a percentage of the transaction amount as a fee. Furthermore, the monetization unit can set transaction conditions to ensure user security. For example, to ensure transaction security, the monetization unit could record transaction history and allow users to review transaction details. This enables the secondary distribution of AI rappers as NFTs. Some or all of the above processes in the monetization unit may be performed using AI, or not. For example, the monetization unit could input data from the trading platform into a generating AI and have the generating AI manage transactions.

[0037] The generation unit can generate the personality, music, and appearance of an AI rapper based on user input. For example, the generation unit can generate the AI ​​rapper's personality based on text data entered by the user. For example, the generation unit can analyze data on personality traits and speaking style entered by the user to form the AI ​​rapper's personality. The generation unit can also generate music for the AI ​​rapper based on voice data entered by the user. For example, the generation unit can analyze data on beats and lyrics entered by the user to create music for the AI ​​rapper. Furthermore, the generation unit can generate the AI ​​rapper's appearance based on image data entered by the user. For example, the generation unit can analyze data on clothing and facial features entered by the user to design the AI ​​rapper's appearance. This makes it possible to generate the AI ​​rapper's personality, music, and appearance based on user input. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user-entered data into a generation AI and have the generation AI perform the generation of the AI ​​rapper.

[0038] The control unit can use the generated AI rappers for battles and music production. For example, the control unit can use the generated AI rappers in battles. For example, the control unit sets the rules of the battle and provides a venue for the AI ​​rappers to compete against each other. The control unit can also use the generated AI rappers for music production. For example, the control unit manages the music production process and provides an environment for the AI ​​rappers to create music. Furthermore, the control unit can share the generated AI rappers with other users. For example, the control unit can publish the generated AI rappers on an online platform so that other users can view them. This makes it possible to use the generated AI rappers for battles and music production. Some or all of the above processes in the control unit may be performed using AI, for example, or not using AI. For example, the control unit can input the data of the generated AI rappers into a generating AI and have the generating AI execute the control of battles and music production.

[0039] The fandom department can enable community participants to vote and automatically implement the resolutions using smart contracts. For example, the fandom department can provide an interface for community participants to vote. For example, the fandom department can build a voting system on an online platform to allow participants to vote easily. The fandom department can also use smart contracts to automatically implement voting results. For example, the fandom department can input voting results into a smart contract and automatically implement the resolutions. Furthermore, to ensure voting transparency, the fandom department can record voting history and allow participants to review the details of their votes. This makes it possible for community participants to vote and for the resolutions to be automatically implemented using smart contracts. Some or all of the above processes in the fandom department may be performed using AI, for example, or not. For example, the fandom department can input voting data into a generating AI and have the generating AI implement the voting results.

[0040] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can store the user's past input history in a database and analyze the input methods that were frequently used. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, if the reception desk frequently uses voice input during a particular time period, it will suggest voice input during that time. Furthermore, the reception desk can automatically display input candidates based on what the user has entered in the past. For example, the reception desk can display relevant input candidates based on keywords the user has entered in the past. This makes it possible to analyze the user's past input history and select the optimal input method. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0041] The reception unit can filter input based on the user's current interests and preferences. For example, the reception unit can display relevant input suggestions based on keywords the user has recently searched for. For example, the reception unit can analyze the user's search history and extract relevant keywords. The reception unit can also filter input suggestions based on topics the user has shown interest in in the past. For example, the reception unit can analyze data on articles and videos the user has viewed in the past and extract relevant topics. Furthermore, the reception unit can analyze the user's social media activity and display relevant input suggestions. For example, the reception unit can analyze data on accounts the user follows and groups they participate in on social media and display relevant information. This makes it possible to filter based on the user's current interests and preferences. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's search history data into a generating AI and have the generating AI perform the filtering process.

[0042] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving input. For example, if the user is in a specific region, the reception unit will prioritize displaying information related to that region. For example, the reception unit can obtain the user's location information from GPS data and filter information related to that region. The reception unit can also prioritize displaying information related to the user's travel destination if the user is traveling. For example, the reception unit can analyze the user's travel destination location information and display relevant tourist information or event information. Furthermore, if the reception unit is participating in a specific event, it can prioritize displaying information related to that event. For example, the reception unit can analyze the user's event participation information and filter information related to that event. This makes it possible to prioritize receiving highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's location information data into a generating AI and have the generating AI perform the filtering of highly relevant information.

[0043] The reception unit can analyze the user's social media activity and receive relevant information when input is received. For example, the reception unit can display relevant input suggestions based on the user's recent posts. For example, the reception unit can analyze the user's social media post data and extract relevant keywords. The reception unit can also analyze the activity of accounts the user follows and display relevant information. For example, the reception unit can analyze the content of posts from accounts the user follows and filter relevant information. Furthermore, the reception unit can display relevant information based on the activity of groups and communities the user participates in. For example, the reception unit can analyze the activity data of groups the user participates in and filter relevant information. This makes it possible to analyze the user's social media activity and receive relevant information. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI perform the filtering of relevant information.

[0044] The generation unit can adjust the level of detail of the AI ​​rapper during generation based on its importance. For example, the generation unit can give important AI rappers detailed backstories and personalities. For example, the generation unit can set detailed personality traits and behavioral patterns for important AI rappers. The generation unit can also give general AI rappers only basic information. For example, the generation unit can set basic appearance and musical elements for general AI rappers. Furthermore, the generation unit can give event-related detailed information for AI rappers designed for specific events. For example, the generation unit can set appearance and music based on the event theme for event-specific AI rappers. This makes it possible to adjust the level of detail of the generation based on the importance of the AI ​​rapper. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input AI rapper importance data into the generation AI and have the generation AI perform the adjustment of the level of detail of the generation.

[0045] The generation unit can apply different generation algorithms depending on the category of the AI ​​rapper during generation. For example, in the case of an AI rapper for battle, the generation unit can apply an algorithm that emphasizes aggressive expressions. For example, the generation unit can generate aggressive lyrics and beats for an AI rapper for battle. The generation unit can also apply an algorithm that emphasizes creative expressions in the case of an AI rapper for music production. For example, the generation unit can generate creative melodies and lyrics for an AI rapper for music production. Furthermore, the generation unit can apply an algorithm that emphasizes approachable expressions in the case of an AI rapper for fandom. For example, the generation unit can generate an approachable appearance and speaking style for an AI rapper for fandom. This makes it possible to apply different generation algorithms depending on the category of the AI ​​rapper. 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 AI rapper category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0046] The generation unit can determine the generation priority based on the submission timing of the AI ​​wrappers during generation. For example, the generation unit will prioritize generation when the deadline is approaching. For example, the generation unit will prioritize the generation of AI wrappers with an approaching submission date. The generation unit can also postpone generation when the submission date is far away. For example, the generation unit will postpone the generation of AI wrappers with a distant submission date. Furthermore, if the submission date is unknown, the generation unit can determine the priority by considering other factors. For example, the generation unit will determine the priority by considering the importance and relevance of the AI ​​wrappers. This makes it possible to determine the generation priority based on the submission timing of the AI ​​wrappers. 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 AI wrapper submission date data into a generation AI and have the generation AI perform the determination of generation priority.

[0047] The generation unit can adjust the generation order based on the relevance of the AI ​​wrappers during generation. For example, the generation unit can prioritize the generation of AI wrappers with high relevance. For example, the generation unit can prioritize the generation of highly relevant AI wrappers. The generation unit can also postpone the generation of AI wrappers with low relevance. For example, the generation unit can postpone the generation of less relevant AI wrappers. Furthermore, if the relevance is unknown, the generation unit can determine the generation order by considering other factors. For example, the generation unit can determine the generation order by considering the importance and submission timing of the AI ​​wrappers. This makes it possible to adjust the generation order based on the relevance of the AI ​​wrappers. 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 AI wrapper relevance data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0048] The control unit can analyze the AI ​​rapper's past battle results and select an appropriate control method during control. For example, the control unit can reuse strategies that have won in past battles. For example, the control unit can save past battle results in a database and analyze winning strategies. The control unit can also improve strategies that have lost in past battles. For example, the control unit can identify areas for improvement in losing strategies based on past battle results. Furthermore, the control unit can propose new strategies based on past battle results. For example, the control unit can analyze past battle results and generate new strategies. This makes it possible to analyze the AI ​​rapper's past battle results and select the optimal control method. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input past battle result data into a generating AI and have the generating AI select the optimal control method.

[0049] The control unit can customize the control means based on the current state of the AI ​​rapper during control. For example, if the AI ​​rapper is tired, the control unit can suggest a rest. For example, the control unit can analyze the AI ​​rapper's performance data and detect the fatigue state. The control unit can also suggest an aggressive battle if the AI ​​rapper is energetic. For example, the control unit can analyze the AI ​​rapper's energy level and detect the energetic state. Furthermore, the control unit can suggest adjustments if the AI ​​rapper is not feeling well. For example, the control unit can analyze the AI ​​rapper's performance data and detect the poor state. This makes it possible to customize the control means based on the current state of the AI ​​rapper. Some or all of the above processing in the control unit may be performed using AI, for example, or not using AI. For example, the control unit can input the AI ​​rapper's performance data into a generating AI and have the generating AI perform the customization of the control means.

[0050] The control unit can select the optimal control method during control, taking into account the geographical location information of the AI ​​rapper. For example, if the AI ​​rapper is in a specific region, the control unit can suggest battles and music production related to that region. For example, the control unit can obtain the AI ​​rapper's location information from GPS data and filter battles and music production related to that region. The control unit can also suggest battles and music production related to the travel destination if the AI ​​rapper is traveling. For example, the control unit can analyze the AI ​​rapper's travel destination location information and suggest relevant battles and music production. Furthermore, if the AI ​​rapper is participating in a specific event, the control unit can also suggest battles and music production related to that event. For example, the control unit can analyze the AI ​​rapper's event participation information and suggest relevant battles and music production. This makes it possible to select the optimal control method while taking into account the AI ​​rapper's geographical location information. Some or all of the above processing in the control unit may be performed using AI, or not using AI. For example, the control unit can input the AI ​​rapper's location information data into a generating AI and have the generating AI select the optimal control method.

[0051] The control unit can analyze the AI ​​rapper's social media activity during control and propose control measures. For example, the control unit can propose relevant battles and music productions based on the AI ​​rapper's recent posts. For example, the control unit can analyze the AI ​​rapper's social media post data and extract relevant keywords. The control unit can also analyze the activity of accounts the AI ​​rapper follows and propose relevant battles and music productions. For example, the control unit can analyze the posts of accounts the AI ​​rapper follows and filter for relevant battles and music productions. Furthermore, the control unit can propose relevant battles and music productions based on the activity of groups and communities the AI ​​rapper participates in. For example, the control unit can analyze the activity data of groups the AI ​​rapper participates in and filter for relevant battles and music productions. This makes it possible to analyze the AI ​​rapper's social media activity and propose control measures. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the AI ​​rapper's social media data into a generating AI and have the generating AI execute the proposal of control measures.

[0052] The level-up unit can analyze the AI ​​wrapper's past growth history to select the optimal level-up method during the level-up process. For example, the level-up unit can propose the optimal level-up method based on past growth history. For example, the level-up unit can save the AI ​​wrapper's growth history data to a database and analyze the optimal level-up method. The level-up unit can also select an effective level-up method from past growth history. For example, the level-up unit can identify an effective level-up method based on the AI ​​wrapper's growth history. Furthermore, the level-up unit can analyze past growth history and propose a new level-up method. For example, the level-up unit can analyze the AI ​​wrapper's growth history and generate a new level-up method. This makes it possible to select the optimal level-up method by analyzing the AI ​​wrapper's past growth history. Some or all of the above processes in the level-up unit may be performed using AI, or not using AI. For example, the level-up unit can input the AI ​​wrapper's growth history data into a generating AI and have the generating AI select the optimal level-up method.

[0053] The level-up unit can select the optimal level-up method when leveling up, taking into account the geographical location information of the AI ​​wrapper. For example, if the AI ​​wrapper is in a specific region, the level-up unit can propose a level-up method related to that region. For example, the level-up unit can obtain the AI ​​wrapper's location information from GPS data and filter level-up methods related to that region. The level-up unit can also propose a level-up method related to the travel destination if the AI ​​wrapper is traveling. For example, the level-up unit can analyze the AI ​​wrapper's travel destination location information and propose a relevant level-up method. Furthermore, if the AI ​​wrapper is participating in a specific event, the level-up unit can propose a level-up method related to that event. For example, the level-up unit can analyze the AI ​​wrapper's event participation information and propose a relevant level-up method. This makes it possible to select the optimal level-up method while taking into account the AI ​​wrapper's geographical location information. Some or all of the above processing in the level-up unit may be performed using AI, or not using AI. For example, the level-up unit can input the AI ​​wrapper's location information data into a generating AI and have the generating AI select the optimal level-up method.

[0054] The fandom unit can analyze a user's past participation history to select the optimal fandom formation method. For example, the fandom unit can propose the optimal fandom formation method based on past participation history. For example, the fandom unit can store the user's past participation history data in a database and analyze the optimal fandom formation method. The fandom unit can also select an effective fandom formation method from past participation history. For example, the fandom unit can identify an effective fandom formation method based on the user's past participation history. Furthermore, the fandom unit can analyze past participation history and propose a new fandom formation method. For example, the fandom unit can analyze the user's past participation history and generate a new fandom formation method. This makes it possible to select the optimal formation method by analyzing the user's past participation history. Some or all of the above processes in the fandom unit may be performed using AI, for example, or without AI. For example, the fandom unit can input the user's past participation history data into a generating AI and have the generating AI select the optimal fandom formation method.

[0055] The fandom unit can select the optimal fandom formation method by considering the user's geographical location information during fandom formation. For example, if the user is in a specific region, the fandom unit can propose a fandom formation method related to that region. For example, the fandom unit can obtain the user's location information from GPS data and filter fandom formation methods related to that region. The fandom unit can also propose a fandom formation method related to the travel destination if the user is traveling. For example, the fandom unit can analyze the user's travel destination location information and propose a relevant fandom formation method. Furthermore, if the fandom unit is participating in a specific event, it can propose a fandom formation method related to that event. For example, the fandom unit can analyze the user's event participation information and propose a relevant fandom formation method. This makes it possible to select the optimal formation method by considering the user's geographical location information. Some or all of the above processing in the fandom unit may be performed using AI, for example, or without AI. For example, the fandom unit can input the user's location information data into a generating AI and have the generating AI select the optimal fandom formation method.

[0056] The ranking unit can analyze the AI ​​rapper's past battle results to select the optimal display method when displaying rankings. For example, the ranking unit can propose the optimal ranking display method based on past battle results. For example, the ranking unit can store the AI ​​rapper's past battle result data in a database and analyze the optimal ranking display method. The ranking unit can also select an effective ranking display method from past battle results. For example, the ranking unit can identify an effective ranking display method based on the AI ​​rapper's past battle results. Furthermore, the ranking unit can analyze past battle results and propose a new ranking display method. For example, the ranking unit can analyze the AI ​​rapper's past battle results and generate a new ranking display method. This makes it possible to analyze the AI ​​rapper's past battle results and select the optimal display method. Some or all of the above processing in the ranking unit may be performed using AI, or not using AI. For example, the ranking unit can input the AI ​​rapper's past battle result data into a generating AI and have the generating AI select the optimal ranking display method.

[0057] The ranking unit can select the optimal display method when displaying rankings, taking into account the geographical location information of the AI ​​rapper. For example, if the AI ​​rapper is in a specific region, the ranking unit can suggest a ranking display method related to that region. For example, the ranking unit can obtain the AI ​​rapper's location information from GPS data and filter the ranking display methods related to that region. The ranking unit can also suggest a ranking display method related to the travel destination if the AI ​​rapper is traveling. For example, the ranking unit can analyze the AI ​​rapper's travel destination location information and suggest a relevant ranking display method. Furthermore, if the AI ​​rapper is participating in a specific event, the ranking unit can suggest a ranking display method related to that event. For example, the ranking unit can analyze the AI ​​rapper's event participation information and suggest a relevant ranking display method. This makes it possible to select the optimal display method while taking into account the geographical location information of the AI ​​rapper. Some or all of the above processing in the ranking unit may be performed using AI, for example, or without AI. For example, the ranking unit can input the AI ​​rapper's location information data into a generating AI and have the generating AI select the optimal ranking display method.

[0058] The monetization unit can analyze the AI ​​wrapper's past transaction history to select the optimal monetization method during the monetization process. For example, the monetization unit can propose the optimal monetization method based on past transaction history. For example, the monetization unit can store the AI ​​wrapper's past transaction history data in a database and analyze the optimal monetization method. The monetization unit can also select an effective monetization method from past transaction history. For example, the monetization unit can identify an effective monetization method based on the AI ​​wrapper's past transaction history. Furthermore, the monetization unit can analyze past transaction history and propose a new monetization method. For example, the monetization unit can analyze the AI ​​wrapper's past transaction history and generate a new monetization method. This makes it possible to select the optimal monetization method by analyzing the AI ​​wrapper's past transaction history. Some or all of the above processes in the monetization unit may be performed using AI, or not using AI. For example, the monetization unit can input the AI ​​wrapper's past transaction history data into a generating AI and have the generating AI select the optimal monetization method.

[0059] The monetization unit can select the optimal monetization method when monetizing, taking into account the geographical location information of the AI ​​rapper. For example, if the AI ​​rapper is in a specific region, the monetization unit can suggest a monetization method related to that region. For example, the monetization unit can obtain the AI ​​rapper's location information from GPS data and filter monetization methods related to that region. The monetization unit can also suggest a monetization method related to the travel destination if the AI ​​rapper is traveling. For example, the monetization unit can analyze the AI ​​rapper's travel destination location information and suggest a relevant monetization method. Furthermore, if the AI ​​rapper is participating in a specific event, the monetization unit can suggest a monetization method related to that event. For example, the monetization unit can analyze the AI ​​rapper's event participation information and suggest a relevant monetization method. This makes it possible to select the optimal monetization method while taking into account the AI ​​rapper's geographical location information. Some or all of the above processing in the monetization unit may be performed using AI, for example, or not using AI. For example, the monetization unit can input the AI ​​rapper's location information data into a generating AI and have the generating AI select the optimal monetization method.

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

[0061] The music entertainment platform can customize the content of AI rappers by considering the user's past preferences when generating them. For example, the generation unit can create songs for the AI ​​rapper based on the style and themes of music the user has liked in the past. It can also customize the appearance of the AI ​​rapper based on the physical characteristics the user has chosen in the past. Furthermore, it can set the personality of the AI ​​rapper based on the personality traits the user has preferred in the past. This makes it possible to generate AI rappers based on the user's past preferences.

[0062] The music entertainment platform can incorporate user feedback in real time during the generation of AI rappers. For example, the generation unit can adjust the music and appearance based on the feedback provided by the user during the generation process. It can also adjust personality traits based on the user's feedback. Furthermore, it can adjust the entire generation process based on the user's feedback. This makes it possible to generate AI rappers based on real-time user feedback.

[0063] The music entertainment platform can customize the content of AI rappers by taking into account the user's geographical location. For example, if the user is in a specific region, the generation unit can create songs that reflect the culture and musical style of that region. If the user is traveling, it can also generate songs that reflect the culture and musical style of their destination. Furthermore, if the user is attending a specific event, it can generate songs that reflect the themes and styles associated with that event. This enables the generation of AI rappers based on the user's geographical location.

[0064] The music entertainment platform can analyze a user's social media activity to customize the content generated when creating an AI rapper. For example, the generation unit can generate relevant songs and appearances based on a user's recent posts. It can also generate relevant songs and appearances based on the activity of accounts the user follows. Furthermore, it can generate relevant songs and appearances based on the activity of groups and communities the user participates in. This makes it possible to generate AI rappers based on the user's social media activity.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk receives input from the user. User input includes text input, voice input, and image input. The reception desk may be equipped with an interface for receiving text input, a microphone for receiving voice input, and a camera for receiving image input. Step 2: The generation unit generates the personality, music, and appearance of the AI ​​rapper based on the information received by the reception unit. The generation unit considers personality traits, speaking style, and behavioral patterns to generate the AI ​​rapper's personality, beats, lyrics, and melodies to generate the music, and clothing, facial features, and body type to generate the appearance. Step 3: The control unit uses the AI ​​rappers generated by the generation unit for battles and music production. The control unit sets the rules for battles, provides a venue for AI rappers to compete against each other, manages the music production process, and provides an environment for AI rappers to create music.

[0067] (Example of form 2) The music entertainment platform according to an embodiment of the present invention is a system that enables users to generate and produce original AI rappers. This system allows users to generate the personality, music, and appearance of an AI rapper, and to nurture them through battles with other AI rappers and music production. Users can level up their AI rappers using items supervised by real rappers or famous AI rappers. Co-production is also possible through the DAO community. A distinctive feature is the adoption of a "Produce To Earn" model, enabling monetization by secondary distribution of popular AI rappers as NFTs. The business model consists of NFT sales, item sales, and monthly subscriptions, aiming for profitability in the third year. Technically, multiple AIs are packaged to constitute an AI rapper, and DAO operation is conducted using smart contracts. Furthermore, considering copyright issues, the service is positioned as an AI rapper creation support service, and measures are taken to avoid legal risks, such as limiting music listening time and issuing disclaimers that explicitly designate the user as the author. For example, AI rappers compete against each other on a battlefield, with the winner determined by audience votes. Next, elements of oneself and one's opponent are read to generate music. Furthermore, AI rappers can be leveled up using items. It's also possible to form a fandom and co-produce. Finally, AI rappers who consistently win battles gain popularity and can be monetized through secondary marketplaces as NFTs. This allows music entertainment platforms to enable users to create and produce their own original AI rappers.

[0068] The music entertainment platform according to this embodiment comprises a reception unit, a generation unit, and a control unit. The reception unit receives input from the user. User input includes, but is not limited to, text input, voice input, and image input. The reception unit provides, for example, an interface for receiving text input. The reception unit may also be equipped with a microphone for receiving voice input. Furthermore, the reception unit may be equipped with a camera for receiving image input. The generation unit generates the personality, music, and appearance of an AI rapper based on the information received by the reception unit. For example, the generation unit considers personality traits, speaking style, behavioral patterns, etc., in order to generate the AI ​​rapper's personality. The generation unit may also consider beats, lyrics, melodies, etc., in order to generate the AI ​​rapper's music. Furthermore, the generation unit may also consider clothing, facial features, body type, etc., in order to generate the AI ​​rapper's appearance. The control unit uses the AI ​​rapper generated by the generation unit for battles and music production. For example, the control unit sets the rules for battles and provides a venue for AI rappers to compete against each other. Furthermore, the control unit can manage the music production process and provide an environment for the AI ​​rapper to create music. This enables the music entertainment platform according to the embodiment to allow users to generate and produce their own original AI rappers.

[0069] The reception unit receives input from the user. User input includes, but is not limited to, text input, voice input, and image input. The reception unit provides, for example, an interface for receiving text input. Specifically, it allows users to input text using a keyboard or touchscreen. The reception unit may also be equipped with a microphone for receiving voice input. In the case of voice input, the user can give instructions to the system by speaking into the microphone. Speech recognition technology is used to convert the user's voice into text, and natural language processing technology is used to analyze the content. Furthermore, the reception unit may be equipped with a camera for receiving image input. In the case of image input, the user can provide images or photos to the system through the camera. Image recognition technology is used to extract and analyze the necessary information from the provided images. In this way, the reception unit supports diverse input methods, allowing users to interact with the system intuitively and flexibly. Furthermore, the reception unit plays a role in centrally managing this input data and appropriately handing it over to the generation unit and control unit. For example, if the user specifies the personality and appearance characteristics of an AI wrapper in text, that information is sent to the generation unit and used to generate the AI ​​wrapper. Similarly, voice and image inputs are converted into appropriate formats and provided to the generation unit. This allows the reception unit to respond to diverse user needs and improve the overall operability and convenience of the system.

[0070] The generation unit generates the personality, music, and appearance of the AI ​​rapper based on the information received by the reception unit. Specifically, to generate the AI ​​rapper's personality, it considers personality traits, speaking style, behavioral patterns, etc. For example, it determines what kind of personality the AI ​​rapper will have based on the personality traits specified by the user. Personality traits include, for example, cheerful, calm, and passionate. Regarding speaking style, it determines what kind of tone and word choice the AI ​​rapper will use based on the user's specifications. Regarding behavioral patterns, it sets how the AI ​​rapper will behave in different situations. The generation unit can also consider beats, lyrics, melodies, etc., to generate the AI ​​rapper's music. For example, it determines the rhythm of the AI ​​rapper's music based on the beat specified by the user. Regarding lyrics, it determines what kind of lyrics the AI ​​rapper will sing based on the user's specifications. Regarding melody, it determines the melody of the AI ​​rapper's music based on the user's specifications. Furthermore, the generation unit can also consider clothing, facial features, body type, etc., to generate the AI ​​rapper's appearance. For example, it determines what kind of clothes the AI ​​rapper will wear based on the clothing specified by the user. Regarding facial features, the AI ​​rapper's face shape and expression are determined based on user specifications. Similarly, the AI ​​rapper's body size and shape are determined based on user specifications. This allows the generation unit to create original AI rappers based on user specifications. Furthermore, the generation unit can save the generated AI rapper data and reuse it as needed. For example, users can later edit or share the AI ​​rappers they have generated with other users. This provides the generation unit with an environment where users can freely customize and enjoy AI rappers.

[0071] The control unit uses the AI ​​rappers generated by the generation unit for battles and music production. Specifically, it sets the rules for battles and provides a platform for AI rappers to compete against each other. For example, it sets the format, duration, and evaluation criteria for battles, and the AI ​​rappers compete according to those rules. Battle evaluations are conducted, for example, by audience votes or expert judging. The control unit can also manage the music production process and provide an environment for AI rappers to create music. Specifically, it sets the theme, genre, and production schedule for songs, and the AI ​​rappers create songs according to those instructions. The control unit monitors the production process and can make corrections or provide advice as needed. Furthermore, the control unit saves the generated songs and battle results so that users can review them later. For example, users can review the results of past battles or play generated songs. The control unit can also collect user feedback and use it to improve the system. For example, it collects user opinions on battles and music production and uses that feedback to review the system's functions and rules. This allows the control unit to optimize the system to make it more enjoyable for users and improve the entertainment experience. In addition, the control unit also provides functions to facilitate collaboration with other users. For example, multiple users can collaboratively produce an AI rapper or participate in battles. This allows the control unit to facilitate interaction among users and support community building.

[0072] The music entertainment platform includes a level-up section that allows users to level up AI rappers using specific items. The level-up section can, for example, use virtual currency to level up AI rappers. For instance, a user can purchase specific items using virtual currency and then use those items to level up the AI ​​rapper. The level-up section can also level up AI rappers using points. For example, a user can earn points through battles and music production and then use those points to level up the AI ​​rapper. Furthermore, the level-up section can also level up AI rappers using specific objects. For example, a user can collect specific objects and then use those objects to level up the AI ​​rapper. This enables the AI ​​rapper to level up. Some or all of the above processes in the level-up section may be performed using AI, or not. For example, the level-up section can input data on items purchased by the user into a generating AI and have the generating AI perform the level-up process.

[0073] The music entertainment platform includes a fandom section for forming fandoms and co-producing music. The fandom section can, for example, set community size limits and clearly define participation requirements. For instance, it might form a fandom if a certain number of participants are reached. It could also restrict participation to users who meet specific criteria, such as only users who own certain items. Furthermore, the fandom section can define activities and provide an environment for co-producing music. For example, it could regularly host battles or music production events, allowing participants to co-produce. This enables the formation of a fandom and co-production. Some or all of the above processes within the fandom section may be performed using AI, or not. For example, the fandom section could input community participant data into a generating AI, allowing the AI ​​to execute the fandom formation and co-production processes.

[0074] The music entertainment platform includes a ranking section for reflecting battle results in the rankings. The ranking section can, for example, set evaluation criteria for determining the winner and loser of a battle. For example, the ranking section can determine the winner and loser based on audience voting results. The ranking section can also implement a point system and award points according to the battle results. For example, the ranking section can award higher points to winning AI rappers and lower points to losing AI rappers. Furthermore, the ranking section can build a system for reflecting battle results in the rankings. For example, the ranking section can reflect battle results in the rankings in real time, allowing users to check the rankings. This makes it possible to reflect battle results in the rankings. Some or all of the above processing in the ranking section may be performed using AI, for example, or not using AI. For example, the ranking section can input battle result data into a generating AI and have the generating AI update the rankings.

[0075] The music entertainment platform includes a monetization unit for secondary distribution of AI rappers as NFTs. The monetization unit can, for example, provide a trading platform, allowing users to buy and sell AI rappers as NFTs. For instance, the monetization unit could partner with a specific trading platform, allowing users to list AI rappers as NFTs for purchase by other users. The monetization unit can also set transaction fees and collect them upon successful completion of a transaction. For example, the monetization unit could collect a percentage of the transaction amount as a fee. Furthermore, the monetization unit can set transaction conditions to ensure user security. For example, to ensure transaction security, the monetization unit could record transaction history and allow users to review transaction details. This enables the secondary distribution of AI rappers as NFTs. Some or all of the above processes in the monetization unit may be performed using AI, or not. For example, the monetization unit could input data from the trading platform into a generating AI and have the generating AI manage transactions.

[0076] The generation unit can generate the personality, music, and appearance of an AI rapper based on user input. For example, the generation unit can generate the AI ​​rapper's personality based on text data entered by the user. For example, the generation unit can analyze data on personality traits and speaking style entered by the user to form the AI ​​rapper's personality. The generation unit can also generate music for the AI ​​rapper based on voice data entered by the user. For example, the generation unit can analyze data on beats and lyrics entered by the user to create music for the AI ​​rapper. Furthermore, the generation unit can generate the AI ​​rapper's appearance based on image data entered by the user. For example, the generation unit can analyze data on clothing and facial features entered by the user to design the AI ​​rapper's appearance. This makes it possible to generate the AI ​​rapper's personality, music, and appearance based on user input. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user-entered data into a generation AI and have the generation AI perform the generation of the AI ​​rapper.

[0077] The control unit can use the generated AI rappers for battles and music production. For example, the control unit can use the generated AI rappers in battles. For example, the control unit sets the rules of the battle and provides a venue for the AI ​​rappers to compete against each other. The control unit can also use the generated AI rappers for music production. For example, the control unit manages the music production process and provides an environment for the AI ​​rappers to create music. Furthermore, the control unit can share the generated AI rappers with other users. For example, the control unit can publish the generated AI rappers on an online platform so that other users can view them. This makes it possible to use the generated AI rappers for battles and music production. Some or all of the above processes in the control unit may be performed using AI, for example, or not using AI. For example, the control unit can input the data of the generated AI rappers into a generating AI and have the generating AI execute the control of battles and music production.

[0078] The fandom department can enable community participants to vote and automatically implement the resolutions using smart contracts. For example, the fandom department can provide an interface for community participants to vote. For example, the fandom department can build a voting system on an online platform to allow participants to vote easily. The fandom department can also use smart contracts to automatically implement voting results. For example, the fandom department can input voting results into a smart contract and automatically implement the resolutions. Furthermore, to ensure voting transparency, the fandom department can record voting history and allow participants to review the details of their votes. This makes it possible for community participants to vote and for the resolutions to be automatically implemented using smart contracts. Some or all of the above processes in the fandom department may be performed using AI, for example, or not. For example, the fandom department can input voting data into a generating AI and have the generating AI implement the voting results.

[0079] The reception unit can estimate the user's emotions and adjust the timing of input acceptance based on the estimated emotions. For example, the reception unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on changes in facial expressions and adjust the timing of input acceptance. The reception unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the timing of input acceptance. Furthermore, the reception unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on fluctuations in heart rate and adjust the timing of input acceptance. This makes it possible to adjust the timing of input acceptance 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user image data captured by a camera into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0080] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can store the user's past input history in a database and analyze the input methods that were frequently used. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, if the reception desk frequently uses voice input during a particular time period, it will suggest voice input during that time. Furthermore, the reception desk can automatically display input candidates based on what the user has entered in the past. For example, the reception desk can display relevant input candidates based on keywords the user has entered in the past. This makes it possible to analyze the user's past input history and select the optimal input method. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0081] The reception unit can filter input based on the user's current interests and preferences. For example, the reception unit can display relevant input suggestions based on keywords the user has recently searched for. For example, the reception unit can analyze the user's search history and extract relevant keywords. The reception unit can also filter input suggestions based on topics the user has shown interest in in the past. For example, the reception unit can analyze data on articles and videos the user has viewed in the past and extract relevant topics. Furthermore, the reception unit can analyze the user's social media activity and display relevant input suggestions. For example, the reception unit can analyze data on accounts the user follows and groups they participate in on social media and display relevant information. This makes it possible to filter based on the user's current interests and preferences. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's search history data into a generating AI and have the generating AI perform the filtering process.

[0082] The reception unit can estimate the user's emotions and determine the priority of input content based on the estimated emotions. For example, the reception unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on changes in facial expressions and determine the priority of input content. The reception unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice, calculate an emotion score, and determine the priority of input content. Furthermore, the reception unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on fluctuations in heart rate and determine the priority of input content. This makes it possible to determine the priority of input content based on the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user image data captured by a camera into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0083] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving input. For example, if the user is in a specific region, the reception unit will prioritize displaying information related to that region. For example, the reception unit can obtain the user's location information from GPS data and filter information related to that region. The reception unit can also prioritize displaying information related to the user's travel destination if the user is traveling. For example, the reception unit can analyze the user's travel destination location information and display relevant tourist information or event information. Furthermore, if the reception unit is participating in a specific event, it can prioritize displaying information related to that event. For example, the reception unit can analyze the user's event participation information and filter information related to that event. This makes it possible to prioritize receiving highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's location information data into a generating AI and have the generating AI perform the filtering of highly relevant information.

[0084] The reception unit can analyze the user's social media activity and receive relevant information when input is received. For example, the reception unit can display relevant input suggestions based on the user's recent posts. For example, the reception unit can analyze the user's social media post data and extract relevant keywords. The reception unit can also analyze the activity of accounts the user follows and display relevant information. For example, the reception unit can analyze the content of posts from accounts the user follows and filter relevant information. Furthermore, the reception unit can display relevant information based on the activity of groups and communities the user participates in. For example, the reception unit can analyze the activity data of groups the user participates in and filter relevant information. This makes it possible to analyze the user's social media activity and receive relevant information. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI perform the filtering of relevant information.

[0085] The generation unit can estimate the user's emotions and adjust the expression method of the generated AI wrapper based on the estimated user emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on changes in facial expressions and adjust the expression method of the AI ​​wrapper. The generation unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the expression method of the AI ​​wrapper. Furthermore, the generation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on fluctuations in heart rate and adjust the expression method of the AI ​​wrapper. This makes it possible to adjust the expression method of the generated AI wrapper 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 generative AI may be a text-generating AI (e.g., LLM) or a multimodal generative AI, but is not limited to such examples. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0086] The generation unit can adjust the level of detail of the AI ​​rapper during generation based on its importance. For example, the generation unit can give important AI rappers detailed backstories and personalities. For example, the generation unit can set detailed personality traits and behavioral patterns for important AI rappers. The generation unit can also give general AI rappers only basic information. For example, the generation unit can set basic appearance and musical elements for general AI rappers. Furthermore, the generation unit can give event-related detailed information for AI rappers designed for specific events. For example, the generation unit can set appearance and music based on the event theme for event-specific AI rappers. This makes it possible to adjust the level of detail of the generation based on the importance of the AI ​​rapper. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input AI rapper importance data into the generation AI and have the generation AI perform the adjustment of the level of detail of the generation.

[0087] The generation unit can apply different generation algorithms depending on the category of the AI ​​rapper during generation. For example, in the case of an AI rapper for battle, the generation unit can apply an algorithm that emphasizes aggressive expressions. For example, the generation unit can generate aggressive lyrics and beats for an AI rapper for battle. The generation unit can also apply an algorithm that emphasizes creative expressions in the case of an AI rapper for music production. For example, the generation unit can generate creative melodies and lyrics for an AI rapper for music production. Furthermore, the generation unit can apply an algorithm that emphasizes approachable expressions in the case of an AI rapper for fandom. For example, the generation unit can generate an approachable appearance and speaking style for an AI rapper for fandom. This makes it possible to apply different generation algorithms depending on the category of the AI ​​rapper. 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 AI rapper category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0088] The generation unit can estimate the user's emotions and adjust the length of the AI ​​wrapper generated based on the estimated emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on changes in facial expressions and adjust the length of the AI ​​wrapper. The generation unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the length of the AI ​​wrapper. Furthermore, the generation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on fluctuations in heart rate and adjust the length of the AI ​​wrapper. This makes it possible to adjust the length of the AI ​​wrapper generated 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user image data captured by a camera into a generation AI and have the generation AI perform the estimation of the user's emotions.

[0089] The generation unit can determine the generation priority based on the submission timing of the AI ​​wrappers during generation. For example, the generation unit will prioritize generation when the deadline is approaching. For example, the generation unit will prioritize the generation of AI wrappers with an approaching submission date. The generation unit can also postpone generation when the submission date is far away. For example, the generation unit will postpone the generation of AI wrappers with a distant submission date. Furthermore, if the submission date is unknown, the generation unit can determine the priority by considering other factors. For example, the generation unit will determine the priority by considering the importance and relevance of the AI ​​wrappers. This makes it possible to determine the generation priority based on the submission timing of the AI ​​wrappers. 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 AI wrapper submission date data into a generation AI and have the generation AI perform the determination of generation priority.

[0090] The generation unit can adjust the generation order based on the relevance of the AI ​​wrappers during generation. For example, the generation unit can prioritize the generation of AI wrappers with high relevance. For example, the generation unit can prioritize the generation of highly relevant AI wrappers. The generation unit can also postpone the generation of AI wrappers with low relevance. For example, the generation unit can postpone the generation of less relevant AI wrappers. Furthermore, if the relevance is unknown, the generation unit can determine the generation order by considering other factors. For example, the generation unit can determine the generation order by considering the importance and submission timing of the AI ​​wrappers. This makes it possible to adjust the generation order based on the relevance of the AI ​​wrappers. 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 AI wrapper relevance data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0091] The control unit can estimate the user's emotions and adjust the timing of battles and music production based on the estimated emotions. For example, the control unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the control unit can calculate an emotion score based on changes in facial expressions and adjust the timing of battles and music production. The control unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the control unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the timing of battles and music production. Furthermore, the control unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the control unit can calculate an emotion score based on fluctuations in heart rate and adjust the timing of battles and music production. This makes it possible to adjust the timing of battles and music production 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-described processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0092] The control unit can analyze the AI ​​rapper's past battle results and select an appropriate control method during control. For example, the control unit can reuse strategies that have won in past battles. For example, the control unit can save past battle results in a database and analyze winning strategies. The control unit can also improve strategies that have lost in past battles. For example, the control unit can identify areas for improvement in losing strategies based on past battle results. Furthermore, the control unit can propose new strategies based on past battle results. For example, the control unit can analyze past battle results and generate new strategies. This makes it possible to analyze the AI ​​rapper's past battle results and select the optimal control method. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input past battle result data into a generating AI and have the generating AI select the optimal control method.

[0093] The control unit can customize the control means based on the current state of the AI ​​rapper during control. For example, if the AI ​​rapper is tired, the control unit can suggest a rest. For example, the control unit can analyze the AI ​​rapper's performance data and detect the fatigue state. The control unit can also suggest an aggressive battle if the AI ​​rapper is energetic. For example, the control unit can analyze the AI ​​rapper's energy level and detect the energetic state. Furthermore, the control unit can suggest adjustments if the AI ​​rapper is not feeling well. For example, the control unit can analyze the AI ​​rapper's performance data and detect the poor state. This makes it possible to customize the control means based on the current state of the AI ​​rapper. Some or all of the above processing in the control unit may be performed using AI, for example, or not using AI. For example, the control unit can input the AI ​​rapper's performance data into a generating AI and have the generating AI perform the customization of the control means.

[0094] The control unit can estimate the user's emotions and determine the priority of battles and music production based on the estimated emotions. For example, the control unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the control unit can calculate an emotion score based on changes in facial expressions and determine the priority of battles and music production. The control unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the control unit can analyze the tone and speed of the voice, calculate an emotion score, and determine the priority of battles and music production. Furthermore, the control unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the control unit can calculate an emotion score based on fluctuations in heart rate and determine the priority of battles and music production. This makes it possible to determine the priority of battles and music production based on the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0095] The control unit can select the optimal control method during control, taking into account the geographical location information of the AI ​​rapper. For example, if the AI ​​rapper is in a specific region, the control unit can suggest battles and music production related to that region. For example, the control unit can obtain the AI ​​rapper's location information from GPS data and filter battles and music production related to that region. The control unit can also suggest battles and music production related to the travel destination if the AI ​​rapper is traveling. For example, the control unit can analyze the AI ​​rapper's travel destination location information and suggest relevant battles and music production. Furthermore, if the AI ​​rapper is participating in a specific event, the control unit can also suggest battles and music production related to that event. For example, the control unit can analyze the AI ​​rapper's event participation information and suggest relevant battles and music production. This makes it possible to select the optimal control method while taking into account the AI ​​rapper's geographical location information. Some or all of the above processing in the control unit may be performed using AI, or not using AI. For example, the control unit can input the AI ​​rapper's location information data into a generating AI and have the generating AI select the optimal control method.

[0096] The control unit can analyze the AI ​​rapper's social media activity during control and propose control measures. For example, the control unit can propose relevant battles and music productions based on the AI ​​rapper's recent posts. For example, the control unit can analyze the AI ​​rapper's social media post data and extract relevant keywords. The control unit can also analyze the activity of accounts the AI ​​rapper follows and propose relevant battles and music productions. For example, the control unit can analyze the posts of accounts the AI ​​rapper follows and filter for relevant battles and music productions. Furthermore, the control unit can propose relevant battles and music productions based on the activity of groups and communities the AI ​​rapper participates in. For example, the control unit can analyze the activity data of groups the AI ​​rapper participates in and filter for relevant battles and music productions. This makes it possible to analyze the AI ​​rapper's social media activity and propose control measures. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the AI ​​rapper's social media data into a generating AI and have the generating AI execute the proposal of control measures.

[0097] The level-up unit can estimate the user's emotions and adjust the level-up method based on the estimated emotions. For example, the level-up unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the level-up unit can calculate an emotion score based on changes in facial expressions and adjust the level-up method. The level-up unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the level-up unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the level-up method. Furthermore, the level-up unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the level-up unit can calculate an emotion score based on fluctuations in heart rate and adjust the level-up method. This makes it possible to adjust the level-up method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be a text-generating AI (e.g., LLM) or a multimodal generative AI, but is not limited to such examples. Some or all of the processing described above in the level-up unit may be performed using AI, or not using AI. For example, the level-up unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0098] The level-up unit can analyze the AI ​​wrapper's past growth history to select the optimal level-up method during the level-up process. For example, the level-up unit can propose the optimal level-up method based on past growth history. For example, the level-up unit can save the AI ​​wrapper's growth history data to a database and analyze the optimal level-up method. The level-up unit can also select an effective level-up method from past growth history. For example, the level-up unit can identify an effective level-up method based on the AI ​​wrapper's growth history. Furthermore, the level-up unit can analyze past growth history and propose a new level-up method. For example, the level-up unit can analyze the AI ​​wrapper's growth history and generate a new level-up method. This makes it possible to select the optimal level-up method by analyzing the AI ​​wrapper's past growth history. Some or all of the above processes in the level-up unit may be performed using AI, or not using AI. For example, the level-up unit can input the AI ​​wrapper's growth history data into a generating AI and have the generating AI select the optimal level-up method.

[0099] The level-up unit can estimate the user's emotions and determine the priority of level-ups based on the estimated emotions. For example, the level-up unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the level-up unit can calculate an emotion score based on changes in facial expressions and determine the priority of level-ups. The level-up unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the level-up unit can analyze the tone and speed of the voice, calculate an emotion score, and determine the priority of level-ups. Furthermore, the level-up unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the level-up unit can calculate an emotion score based on fluctuations in heart rate and determine the priority of level-ups. This makes it possible to determine the priority of level-ups based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be a text-generating AI (e.g., LLM) or a multimodal generative AI, but is not limited to such examples. Some or all of the processing described above in the level-up unit may be performed using AI, or not using AI. For example, the level-up unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0100] The level-up unit can select the optimal level-up method when leveling up, taking into account the geographical location information of the AI ​​wrapper. For example, if the AI ​​wrapper is in a specific region, the level-up unit can propose a level-up method related to that region. For example, the level-up unit can obtain the AI ​​wrapper's location information from GPS data and filter level-up methods related to that region. The level-up unit can also propose a level-up method related to the travel destination if the AI ​​wrapper is traveling. For example, the level-up unit can analyze the AI ​​wrapper's travel destination location information and propose a relevant level-up method. Furthermore, if the AI ​​wrapper is participating in a specific event, the level-up unit can propose a level-up method related to that event. For example, the level-up unit can analyze the AI ​​wrapper's event participation information and propose a relevant level-up method. This makes it possible to select the optimal level-up method while taking into account the AI ​​wrapper's geographical location information. Some or all of the above processing in the level-up unit may be performed using AI, or not using AI. For example, the level-up unit can input the AI ​​wrapper's location information data into a generating AI and have the generating AI select the optimal level-up method.

[0101] The fandom unit can estimate the user's emotions and adjust the fandom formation method based on the estimated user emotions. For example, the fandom unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the fandom unit can calculate an emotion score based on changes in facial expressions and adjust the fandom formation method. The fandom unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the fandom unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the fandom formation method. Furthermore, the fandom unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the fandom unit can calculate an emotion score based on fluctuations in heart rate and adjust the fandom formation method. This makes it possible to adjust the fandom formation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the fandom section may be performed using AI, or not using AI. For example, the fandom section may input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0102] The fandom unit can analyze a user's past participation history to select the optimal fandom formation method. For example, the fandom unit can propose the optimal fandom formation method based on past participation history. For example, the fandom unit can store the user's past participation history data in a database and analyze the optimal fandom formation method. The fandom unit can also select an effective fandom formation method from past participation history. For example, the fandom unit can identify an effective fandom formation method based on the user's past participation history. Furthermore, the fandom unit can analyze past participation history and propose a new fandom formation method. For example, the fandom unit can analyze the user's past participation history and generate a new fandom formation method. This makes it possible to select the optimal formation method by analyzing the user's past participation history. Some or all of the above processes in the fandom unit may be performed using AI, for example, or without AI. For example, the fandom unit can input the user's past participation history data into a generating AI and have the generating AI select the optimal fandom formation method.

[0103] The fandom unit can estimate a user's emotions and determine the priority of fandoms based on those estimated emotions. For example, the fandom unit can capture a user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. For example, the fandom unit can calculate an emotion score based on changes in facial expression and determine the priority of fandoms. The fandom unit can also record a user's voice and estimate their emotions using voice analysis technology. For example, the fandom unit can analyze the tone and speed of the voice, calculate an emotion score, and determine the priority of fandoms. Furthermore, the fandom unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the fandom unit can calculate an emotion score based on fluctuations in heart rate and determine the priority of fandoms. This makes it possible to determine the priority of fandoms based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the fandom section may be performed using AI, or not using AI. For example, the fandom section may input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0104] The fandom unit can select the optimal fandom formation method by considering the user's geographical location information during fandom formation. For example, if the user is in a specific region, the fandom unit can propose a fandom formation method related to that region. For example, the fandom unit can obtain the user's location information from GPS data and filter fandom formation methods related to that region. The fandom unit can also propose a fandom formation method related to the travel destination if the user is traveling. For example, the fandom unit can analyze the user's travel destination location information and propose a relevant fandom formation method. Furthermore, if the fandom unit is participating in a specific event, it can propose a fandom formation method related to that event. For example, the fandom unit can analyze the user's event participation information and propose a relevant fandom formation method. This makes it possible to select the optimal formation method by considering the user's geographical location information. Some or all of the above processing in the fandom unit may be performed using AI, for example, or without AI. For example, the fandom unit can input the user's location information data into a generating AI and have the generating AI select the optimal fandom formation method.

[0105] The ranking unit can estimate the user's emotions and adjust the ranking display method based on the estimated emotions. For example, the ranking unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the ranking unit can calculate an emotion score based on changes in facial expressions and adjust the ranking display method. The ranking unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the ranking unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the ranking display method. Furthermore, the ranking unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the ranking unit can calculate an emotion score based on fluctuations in heart rate and adjust the ranking display method. This makes it possible to adjust the ranking display method 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the ranking section may be performed using AI, for example, or without AI. For example, the ranking section can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0106] The ranking unit can analyze the AI ​​rapper's past battle results to select the optimal display method when displaying rankings. For example, the ranking unit can propose the optimal ranking display method based on past battle results. For example, the ranking unit can store the AI ​​rapper's past battle result data in a database and analyze the optimal ranking display method. The ranking unit can also select an effective ranking display method from past battle results. For example, the ranking unit can identify an effective ranking display method based on the AI ​​rapper's past battle results. Furthermore, the ranking unit can analyze past battle results and propose a new ranking display method. For example, the ranking unit can analyze the AI ​​rapper's past battle results and generate a new ranking display method. This makes it possible to analyze the AI ​​rapper's past battle results and select the optimal display method. Some or all of the above processing in the ranking unit may be performed using AI, or not using AI. For example, the ranking unit can input the AI ​​rapper's past battle result data into a generating AI and have the generating AI select the optimal ranking display method.

[0107] The ranking unit can estimate the user's emotions and determine ranking priorities based on those estimated emotions. For example, the ranking unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the ranking unit can calculate an emotion score based on changes in facial expressions and determine ranking priorities. The ranking unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the ranking unit can analyze the tone and speed of the voice, calculate an emotion score, and determine ranking priorities. Furthermore, the ranking unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the ranking unit can calculate an emotion score based on fluctuations in heart rate and determine ranking priorities. This makes it possible to determine ranking priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the ranking section may be performed using AI, for example, or without AI. For example, the ranking section can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0108] The ranking unit can select the optimal display method when displaying rankings, taking into account the geographical location information of the AI ​​rapper. For example, if the AI ​​rapper is in a specific region, the ranking unit can suggest a ranking display method related to that region. For example, the ranking unit can obtain the AI ​​rapper's location information from GPS data and filter the ranking display methods related to that region. The ranking unit can also suggest a ranking display method related to the travel destination if the AI ​​rapper is traveling. For example, the ranking unit can analyze the AI ​​rapper's travel destination location information and suggest a relevant ranking display method. Furthermore, if the AI ​​rapper is participating in a specific event, the ranking unit can suggest a ranking display method related to that event. For example, the ranking unit can analyze the AI ​​rapper's event participation information and suggest a relevant ranking display method. This makes it possible to select the optimal display method while taking into account the geographical location information of the AI ​​rapper. Some or all of the above processing in the ranking unit may be performed using AI, for example, or without AI. For example, the ranking unit can input the AI ​​rapper's location information data into a generating AI and have the generating AI select the optimal ranking display method.

[0109] The monetization unit can estimate the user's emotions and adjust the monetization method based on the estimated emotions. For example, the monetization unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the monetization unit can calculate an emotion score based on changes in facial expressions and adjust the monetization method. The monetization unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the monetization unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the monetization method. Furthermore, the monetization unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the monetization unit can calculate an emotion score based on fluctuations in heart rate and adjust the monetization method. This makes it possible to adjust the monetization method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the above-described processes in the monetization unit may be performed using AI, or not using AI. For example, the monetization unit may input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0110] The monetization unit can analyze the AI ​​wrapper's past transaction history to select the optimal monetization method during the monetization process. For example, the monetization unit can propose the optimal monetization method based on past transaction history. For example, the monetization unit can store the AI ​​wrapper's past transaction history data in a database and analyze the optimal monetization method. The monetization unit can also select an effective monetization method from past transaction history. For example, the monetization unit can identify an effective monetization method based on the AI ​​wrapper's past transaction history. Furthermore, the monetization unit can analyze past transaction history and propose a new monetization method. For example, the monetization unit can analyze the AI ​​wrapper's past transaction history and generate a new monetization method. This makes it possible to select the optimal monetization method by analyzing the AI ​​wrapper's past transaction history. Some or all of the above processes in the monetization unit may be performed using AI, or not using AI. For example, the monetization unit can input the AI ​​wrapper's past transaction history data into a generating AI and have the generating AI select the optimal monetization method.

[0111] The monetization unit can estimate a user's emotions and determine monetization priorities based on those estimated emotions. For example, the monetization unit can capture a user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. For example, the monetization unit can calculate an emotion score based on changes in facial expression and determine monetization priorities. The monetization unit can also record a user's voice and estimate their emotions using voice analysis technology. For example, the monetization unit can analyze the tone and speed of the voice, calculate an emotion score, and determine monetization priorities. Furthermore, the monetization unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the monetization unit can calculate an emotion score based on fluctuations in heart rate and determine monetization priorities. This makes it possible to determine monetization priorities based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the above-described processes in the monetization unit may be performed using AI, or not using AI. For example, the monetization unit may input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0112] The monetization unit can select the optimal monetization method when monetizing, taking into account the geographical location information of the AI ​​rapper. For example, if the AI ​​rapper is in a specific region, the monetization unit can suggest a monetization method related to that region. For example, the monetization unit can obtain the AI ​​rapper's location information from GPS data and filter monetization methods related to that region. The monetization unit can also suggest a monetization method related to the travel destination if the AI ​​rapper is traveling. For example, the monetization unit can analyze the AI ​​rapper's travel destination location information and suggest a relevant monetization method. Furthermore, if the AI ​​rapper is participating in a specific event, the monetization unit can suggest a monetization method related to that event. For example, the monetization unit can analyze the AI ​​rapper's event participation information and suggest a relevant monetization method. This makes it possible to select the optimal monetization method while taking into account the AI ​​rapper's geographical location information. Some or all of the above processing in the monetization unit may be performed using AI, for example, or not using AI. For example, the monetization unit can input the AI ​​rapper's location information data into a generating AI and have the generating AI select the optimal monetization method.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] The music entertainment platform can estimate the user's emotions and customize the AI ​​rapper generation process based on those emotions. For example, if the user is excited, the generation unit can generate more energetic music and appearance. If the user is relaxed, it can generate calmer music and appearance. Furthermore, if the user is sad, it can generate music and appearance that are more emotionally resonant. This makes it possible to generate AI rappers that respond to the user's emotions.

[0115] The music entertainment platform can customize the content of AI rappers by considering the user's past preferences when generating them. For example, the generation unit can create songs for the AI ​​rapper based on the style and themes of music the user has liked in the past. It can also customize the appearance of the AI ​​rapper based on the physical characteristics the user has chosen in the past. Furthermore, it can set the personality of the AI ​​rapper based on the personality traits the user has preferred in the past. This makes it possible to generate AI rappers based on the user's past preferences.

[0116] The music entertainment platform can incorporate user feedback in real time during the generation of AI rappers. For example, the generation unit can adjust the music and appearance based on the feedback provided by the user during the generation process. It can also adjust personality traits based on the user's feedback. Furthermore, it can adjust the entire generation process based on the user's feedback. This makes it possible to generate AI rappers based on real-time user feedback.

[0117] The music entertainment platform can customize the content of AI rappers by taking into account the user's geographical location. For example, if the user is in a specific region, the generation unit can create songs that reflect the culture and musical style of that region. If the user is traveling, it can also generate songs that reflect the culture and musical style of their destination. Furthermore, if the user is attending a specific event, it can generate songs that reflect the themes and styles associated with that event. This enables the generation of AI rappers based on the user's geographical location.

[0118] The music entertainment platform can analyze a user's social media activity to customize the content generated when creating an AI rapper. For example, the generation unit can generate relevant songs and appearances based on a user's recent posts. It can also generate relevant songs and appearances based on the activity of accounts the user follows. Furthermore, it can generate relevant songs and appearances based on the activity of groups and communities the user participates in. This makes it possible to generate AI rappers based on the user's social media activity.

[0119] The music entertainment platform can estimate the user's emotions and adjust the AI ​​rapper's battle strategy based on those emotions. For example, the control unit can adopt a more aggressive battle strategy if the user is excited, a more defensive battle strategy if the user is relaxed, and a more empathetic battle strategy if the user is sad. This allows for the adjustment of the battle strategy according to the user's emotions.

[0120] The music entertainment platform can estimate the user's emotions and customize the AI ​​rapper's music production process based on those estimated emotions. For example, the control unit can create more energetic music if the user is excited, and calmer music if the user is relaxed. Furthermore, if the user is sad, it can create music that is more empathetic. This allows for the customization of music production according to the user's emotions.

[0121] The music entertainment platform can estimate the user's emotions and customize the AI ​​rapper's level-up process based on those emotions. For example, if the user is excited, the level-up section can suggest more challenging levels. If the user is relaxed, it can suggest gentler levels. Furthermore, if the user is sad, it can suggest levels that are more empathetic to their emotions. This allows for the customization of the level-up process according to the user's emotions.

[0122] Music entertainment platforms can estimate users' emotions and adjust fandom activities based on those estimates. For example, a fandom can suggest more active activities if a user is excited, more calming activities if a user is relaxed, and activities that are more empathetic if a user is sad. This allows for the adjustment of fandom activities to match the user's emotions.

[0123] Music entertainment platforms can estimate users' emotions and customize monetization methods based on those estimates. For example, if a user is excited, the monetization unit can suggest more aggressive monetization methods. If a user is relaxed, it can suggest more gentle monetization methods. Furthermore, if a user is sad, it can suggest monetization methods that are more empathetic. This allows for the customization of monetization methods according to the user's emotions.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The reception desk receives input from the user. User input includes text input, voice input, and image input. The reception desk may be equipped with an interface for receiving text input, a microphone for receiving voice input, and a camera for receiving image input. Step 2: The generation unit generates the personality, music, and appearance of the AI ​​rapper based on the information received by the reception unit. The generation unit considers personality traits, speaking style, and behavioral patterns to generate the AI ​​rapper's personality, beats, lyrics, and melodies to generate the music, and clothing, facial features, and body type to generate the appearance. Step 3: The control unit uses the AI ​​rappers generated by the generation unit for battles and music production. The control unit sets the rules for battles, provides a venue for AI rappers to compete against each other, manages the music production process, and provides an environment for AI rappers to create music.

[0126] 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.

[0127] 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 text generation AI, image generation AI, and multimodal generation AI. 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 with 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 from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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 various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts 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.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0128] 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.

[0129] For example, the reception unit receives input from the user via the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing unit 12. The generation unit generates the personality, music, and appearance of the AI ​​rapper, for example, using the specific processing unit 290 of the data processing unit 12. The control unit manages battles and music production, for example, using the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The level-up unit levels up the AI ​​rapper using items, for example, using the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The fandom unit forms a fandom and engages in co-production, for example, using the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The ranking unit reflects battle results in the ranking, for example, using the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The monetization unit, for example, uses the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 to secondary distribution of the AI ​​wrapper as an NFT. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0144] 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.

[0145] For example, the reception unit receives input from the user via the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing unit 12. The generation unit generates the personality, music, and appearance of the AI ​​rapper, for example, using the specific processing unit 290 of the data processing unit 12. The control unit manages battles and music production, for example, using the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The level-up unit levels up the AI ​​rapper using items, for example, using the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The fandom unit forms a fandom and engages in co-production, for example, using the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The ranking unit reflects battle results in the ranking, for example, using the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The monetization unit, for example, uses the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 to secondary distribution of the AI ​​wrapper as an NFT. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0160] 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.

[0161] For example, the reception unit receives input from the user via the microphone 238 of the headset terminal 314 and the communication I / F 26 of the data processing unit 12. The generation unit generates the personality, music, and appearance of the AI ​​rapper, for example, using the specific processing unit 290 of the data processing unit 12. The control unit manages battles and music production, for example, using the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12. The level-up unit levels up the AI ​​rapper using items, for example, using the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12. The fandom unit forms a fandom and engages in co-production, for example, using the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12. The ranking unit reflects battle results in the ranking, for example, using the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12. The monetization unit, for example, uses the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12 to secondary distribution of the AI ​​wrapper as an NFT. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.).

[0175] 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.

[0176] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0177] 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.

[0178] For example, the reception unit receives input from the user via the microphone 238 of the robot 414 or the communication I / F 26 of the data processing unit 12. The generation unit generates the personality, music, and appearance of the AI ​​rapper, for example, using the specific processing unit 290 of the data processing unit 12. The control unit manages battles and music production, for example, using the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The level-up unit levels up the AI ​​rapper using items, for example, using the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The fandom unit forms a fandom and engages in co-production, for example, using the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The ranking unit reflects battle results in the ranking, for example, using the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The monetization unit, for example, uses the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 to secondary distribution of the AI ​​wrapper as an NFT. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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."

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] (Note 1) A reception area that receives input from users, A generation unit generates the personality, music, and appearance of an AI rapper based on the information received by the aforementioned reception unit, The system includes a control unit that uses the AI ​​rapper generated by the generation unit for battles or music production. A system characterized by the following features. (Note 2) It features a level-up section that allows you to level up the AI ​​rapper using specific items. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a fandom department for forming a fandom and co-producing projects. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a ranking section to reflect battle results in the rankings. The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes a monetization section for secondary distribution of AI wrappers as NFTs. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generates the personality, music, and appearance of an AI rapper based on user input. The system described in Appendix 1, characterized by the features described herein. (Note 7) The control unit, The generated AI rappers can be used in battles and music production. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned fandom section is, Community participants vote, and the resolution is automatically reflected in a smart contract. The system described in Appendix 3, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Analyze the user's past input history and select the appropriate input method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving input, filtering is performed based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When receiving input, the system prioritizes receiving information that is highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When receiving input, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts how the AI ​​wrapper is represented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, adjust the level of detail based on the importance of the AI ​​wrapper. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, different generation algorithms are applied depending on the category of the AI ​​wrapper. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts the length of the AI ​​wrapper generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, the generation priority is determined based on when the AI ​​wrappers were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the generation order is adjusted based on the relevance of the AI ​​wrappers. The system described in Appendix 1, characterized by the features described herein. (Note 21) The control unit, The system estimates the user's emotions and adjusts the timing of battles and music production based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The control unit, During control, the AI ​​rapper's past battle results are analyzed to select the appropriate control method. The system described in Appendix 1, characterized by the features described herein. (Note 23) The control unit, During control, the means of control are customized based on the current state of the AI ​​wrapper. The system described in Appendix 1, characterized by the features described herein. (Note 24) The control unit, The system estimates the user's emotions and determines the priority of battles and music production based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The control unit, During control, the optimal control method is selected by considering the geographical location information of the AI ​​wrapper. The system described in Appendix 1, characterized by the features described herein. (Note 26) The control unit, During control, the AI ​​rapper's social media activity is analyzed to propose control methods. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned level-up unit is It estimates the user's emotions and adjusts the level-up method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned level-up unit is When leveling up, the AI ​​rapper's past growth history is analyzed to select the optimal leveling method. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned level-up unit is It estimates the user's emotions and determines the priority of level-ups based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned level-up unit is When leveling up, the system selects the optimal leveling method by considering the geographical location of the AI ​​rapper. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned fandom section is, We estimate user sentiment and adjust the fandom formation process based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned fandom section is, When forming a fandom, the optimal formation method is selected by analyzing the users' past participation history. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned fandom section is, It estimates user sentiment and determines fandom priorities based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned fandom section is, When forming a fandom, the optimal formation method is selected by considering the geographical location information of the users. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned ranking section is, The system estimates user sentiment and adjusts how rankings are displayed based on that estimated sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned ranking section is, When displaying rankings, the AI ​​rapper's past battle results are analyzed to select the optimal display method. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned ranking section is, It estimates user sentiment and determines ranking priorities based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned ranking section is, When displaying rankings, the optimal display method is selected by considering the geographical location information of the AI ​​rapper. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned revenue generation unit is, It estimates user sentiment and adjusts monetization methods based on the estimated user sentiment. The system described in Appendix 5, characterized by the features described herein. (Note 40) The aforementioned revenue generation unit is, When monetizing, the AI ​​rapper's past transaction history is analyzed to select the optimal monetization method. The system described in Appendix 5, characterized by the features described herein. (Note 41) The aforementioned revenue generation unit is, It estimates user sentiment and prioritizes monetization based on the estimated user sentiment. The system described in Appendix 5, characterized by the features described herein. (Note 42) The aforementioned revenue generation unit is, When monetizing, the optimal monetization method is selected by considering the geographical location information of the AI ​​rapper. The system described in Appendix 5, characterized by the features described herein. [Explanation of Symbols]

[0198] 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 reception area that receives input from users, A generation unit generates the personality, music, and appearance of an AI rapper based on the information received by the aforementioned reception unit. The system includes a control unit that uses the AI ​​rapper generated by the generation unit for battles or music production. A system characterized by the following features.

2. It features a level-up section that allows you to level up the AI ​​rapper using specific items. The system according to feature 1.

3. It has a fandom department for forming a fandom and co-producing projects. The system according to feature 1.

4. It includes a ranking section to reflect battle results in the rankings. The system according to feature 1.

5. It includes a monetization section for secondary distribution of AI wrappers as NFTs. The system according to feature 1.

6. The generating unit is Based on user input, it generates the personality, music, and appearance of an AI rapper. The system according to feature 1.

7. The control unit, The generated AI rappers will be used in battles and music production. The system according to feature 1.

8. The aforementioned fandom section is, Community participants vote, and the resolution is automatically reflected in a smart contract. The system according to claim 3.

9. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system according to feature 1.

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