system

Abstract

The system according to this embodiment aims to maintain the value of talent and maximize advertising strategies. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a reproduction unit, an advertising unit, and an NFT conversion unit. The collection unit collects past data of the talent. The analysis unit analyzes the data collected by the collection unit. The reproduction unit reproduces the appearance and voice of the talent at a specific time based on the data analyzed by the analysis unit. The advertising unit uses the talent data reproduced by the reproduction unit to carry out advertising strategies. The NFT conversion unit converts the talent data used by the advertising unit into NFTs.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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, there is a risk that the value of a talent may decline due to aging or death.

[0005] The system according to the embodiment aims to maintain the value of a talent and maximize an advertising strategy.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a reproduction unit, an advertising unit, and an NFT conversion unit. The data collection unit collects past data of the talent. The analysis unit analyzes the data collected by the data collection unit. The reproduction unit reproduces the appearance and voice of the talent at a specific time based on the data analyzed by the analysis unit. The advertising unit uses the talent data reproduced by the reproduction unit to implement advertising strategies. The NFT conversion unit converts the talent data used by the advertising unit into NFTs. [Effects of the Invention]

[0007] The system according to this embodiment can maintain the value of talent and maximize advertising strategies. [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, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that uses AI to completely recreate a talent's peak period in order to prevent the decline in talent value due to aging or death. This system collects past video, photograph, and audio data of the talent and analyzes it using AI. Next, based on the analysis results, it recreates the appearance and voice of the talent during their peak period. Using this recreated talent data, the system maximizes the talent's value as an advertising strategy. It also makes established story-type commercials universal and solidifies the brand image. Furthermore, by converting talent value into NFTs, it creates new business models. For example, it collects past video, photograph, and audio data of the talent. In this case, it focuses on collecting data from the talent's peak period. For example, it collects television appearances, magazine photos, and audio data from radio appearances from when the talent was at their most popular. This allows for a detailed understanding of the talent's appearance and voice during their peak period. Next, the collected data is analyzed using AI. AI analyzes collected data to build models that recreate the appearance and voice of a talent at their peak. For example, the AI ​​analyzes video data to learn the talent's facial features and expressions. It also analyzes audio data to learn the talent's voice characteristics and speaking style. This allows for highly accurate recreation of the talent's appearance and voice at their peak. The recreated talent data is then used to maximize the talent's value as an advertising strategy. For example, new commercials are produced using the recreated footage of the talent. Because these commercials recreate the talent's appearance and voice at their peak, they leave a strong impression on viewers. Furthermore, established story-based commercials become universal, solidifying the brand image. For example, continuously broadcasting a series of commercials featuring the talent can strongly imprint the brand image on viewers. In addition, new business models are created by converting talent value into NFTs. For example, the recreated talent data is sold as an NFT. Because this NFT recreates the talent's appearance and voice at their peak, it becomes extremely valuable to fans. Furthermore, the sale of NFTs can secure a new source of revenue.This prevents a decline in talent value and allows for the creation of a sustainable business model.

[0029] The talent value reproduction system according to this embodiment comprises a collection unit, an analysis unit, a reproduction unit, an advertising unit, and an NFT conversion unit. The collection unit collects past data of the talent. For example, the collection unit can collect television appearances, magazine photos, and audio data from radio appearances during the talent's peak period. For example, the collection unit can focus on collecting data from the period when the talent was most popular. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can use AI to analyze the collected data and learn the talent's facial features, facial expressions, voice characteristics, and speaking style. For example, the analysis unit can use AI to analyze video data and learn the talent's facial features and facial expressions. The analysis unit can also analyze audio data and learn the talent's voice characteristics and speaking style. The reproduction unit reproduces the appearance and voice of the talent during their peak period based on the data analyzed by the analysis unit. The reproduction unit can, for example, construct an AI model to accurately reproduce the appearance and voice of a talent at their peak. The reproduction unit can, for example, use an AI model to accurately reproduce the appearance and voice of a talent at their peak. The advertising unit uses the talent data reproduced by the reproduction unit to develop advertising strategies. The advertising unit can, for example, produce new commercials using the reproduced footage of the talent. The advertising unit can, for example, produce new commercials using the reproduced footage of the talent. The NFT unit converts the talent data used by the advertising unit into NFTs. The NFT unit can, for example, sell the reproduced talent data as NFTs. As a result, the talent value reproduction system according to this embodiment can reproduce the talent at their peak and maximize talent value through advertising strategies and NFT conversion.

[0030] The data collection unit can collect television appearances, magazine photos, and audio data from radio appearances of a talent from specific periods. For example, the data collection unit can collect television appearances from when the talent was at their peak. The data collection unit can also collect magazine photos from when the talent was at their peak. For example, the data collection unit can collect magazine photos from when the talent was at their peak. The data collection unit can also collect audio data from radio appearances from when the talent was at their peak. For example, the data collection unit can collect audio data from radio appearances from when the talent was at their peak. This improves the accuracy of the reproduction by focusing on collecting data from when the talent was at their peak. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can use AI to automate data collection in order to collect data from when the talent was at their peak.

[0031] The analysis unit can use AI to analyze collected data and learn the facial features, facial expressions, voice characteristics, and speaking style of the talent. For example, the analysis unit can use AI to analyze collected data and learn the facial features and facial expressions of the talent. For example, the analysis unit can use AI to analyze video data and learn the facial features and facial expressions of the talent. The analysis unit can also analyze audio data and learn the voice characteristics and speaking style of the talent. For example, the analysis unit can use AI to analyze audio data and learn the voice characteristics and speaking style of the talent. The analysis unit can also analyze collected data and build a model to reproduce the appearance and voice of the talent at their peak. For example, the analysis unit can use AI to analyze collected data and build a model to reproduce the appearance and voice of the talent at their peak. By using AI, the characteristics of the talent can be learned with high accuracy, and the accuracy of reproduction can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into the AI ​​and have the AI ​​learn the characteristics of the talent.

[0032] The reproduction unit can construct an AI model to accurately reproduce the appearance and voice of a talent at a specific time. For example, the reproduction unit can construct an AI model to accurately reproduce the appearance and voice of a talent at their peak. For example, the reproduction unit can accurately reproduce the appearance and voice of a talent at their peak using an AI model. The reproduction unit can also construct an AI model to reproduce the appearance and voice of a talent at their peak. For example, the reproduction unit can construct an AI model to reproduce the appearance and voice of a talent at their peak using AI. This makes it possible to accurately reproduce the appearance and voice of a talent at their peak using an AI model. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can construct an AI model to reproduce the appearance and voice of a talent at their peak and perform the reproduction using that model.

[0033] The advertising department can produce commercials using the recreated footage of the talent. The advertising department can, for example, produce new commercials using the recreated footage of the talent. The advertising department can also produce commercials that leave a strong impression on viewers using the recreated footage of the talent. The advertising department can produce commercials that leave a strong impression on viewers using the recreated footage of the talent. Some or all of the above processes in the advertising department may be performed using AI, for example, or not using AI. For example, the advertising department can input the recreated footage of the talent into AI and have AI produce the commercials.

[0034] The NFT creation unit can provide the recreated talent data as an NFT. The NFT creation unit can, for example, sell the recreated talent data as an NFT. The NFT creation unit can also secure a new revenue stream by providing the recreated talent data as an NFT. The NFT creation unit can secure a new revenue stream by providing the recreated talent data as an NFT. This allows it to secure a new revenue stream by selling the recreated talent data as an NFT. Some or all of the above processing in the NFT creation unit may be performed using AI, for example, or without AI. For example, the NFT creation unit can input the recreated talent data into AI and have AI perform the provision as an NFT.

[0035] The data collection unit can evaluate the reliability of data when collecting past data on a talent and prioritize the collection of highly reliable data. For example, the data collection unit can prioritize the collection of video data provided from public archives. The data collection unit can also prioritize the collection of posts from a talent's official social media accounts. The data collection unit can also prioritize the collection of interview articles provided from highly reliable media. By prioritizing the collection of highly reliable data, the accuracy of the reproduction is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI evaluate the reliability of the data and have AI collect highly reliable data.

[0036] The data collection unit can expand the types of data it collects, including interview articles of talents and records of fan interaction events. For example, the data collection unit can collect magazine interview articles of talents. For example, the data collection unit can collect magazine interview articles of talents. The data collection unit can also collect videos of fan meetings in which talents appeared. For example, the data collection unit can collect audio data of talents appearing on radio programs. For example, the data collection unit can collect audio data of talents appearing on radio programs. By expanding the types of data collected, a wider variety of data can be collected. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can have AI expand the types of data it collects, and have AI perform the collection of interview articles of talents and records of fan interaction events.

[0037] The data collection unit can collect data that reflects the popularity of a talent in different regions by considering regional variations in the data being collected. For example, the data collection unit can collect video footage of a talent appearing on a program overseas. For example, the data collection unit can collect video footage of a talent appearing on a program overseas. The data collection unit can also collect video footage of a talent performing live in different regions. For example, the data collection unit can collect video footage of a talent performing live in different regions. The data collection unit can also collect interview articles conducted by a talent in different regions. For example, the data collection unit can collect interview articles conducted by a talent in different regions. By considering regional variations, the data collection unit can collect data that reflects the popularity of a talent in different regions. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can have AI consider regional variations and have AI collect data that reflects the popularity of a talent in different regions.

[0038] The data collection unit can collect data throughout a talent's entire career by considering the temporal variations in the data being collected. For example, the data collection unit can collect video footage of a talent from the time of their debut. For example, the data collection unit can collect interview articles from the middle of a talent's career. For example, the data collection unit can collect data on a talent's current activities. For example, the data collection unit can collect data on a talent's current activities. In this way, by considering the temporal variations, data can be collected throughout a talent's entire career. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can have AI consider the temporal variations and have AI perform the collection of data throughout a talent's entire career.

[0039] The analysis unit can analyze changes in the talent's facial expressions and voice over time to identify specific periods. For example, the analysis unit can analyze the frequency of the talent's smiles over time. The analysis unit can also analyze changes in the talent's voice tone over time. The analysis unit can also analyze changes in the talent's facial expressions over time. By performing analysis along the time axis, the analysis unit can identify the talent's peak period. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI analyze changes in the talent's facial expressions and voice, and have the AI ​​identify the peak period.

[0040] The analysis unit can also analyze changes in the talent's fashion and style during the analysis process and reflect these changes in the reproduction. For example, the analysis unit can analyze the changes in the talent's costumes. The analysis unit can also analyze changes in the talent's hairstyles. The analysis unit can also analyze changes in the talent's makeup. By analyzing changes in fashion and style, a more realistic reproduction becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI analyze changes in the talent's fashion and style and reflect these changes in the reproduction.

[0041] The analysis unit can analyze the talent's social media activities during analysis and reflect the reactions of fans. For example, the analysis unit can analyze the reactions to the talent's tweets. The analysis unit can also analyze the reactions to the talent's Instagram posts. The analysis unit can also analyze the reactions to the talent's Facebook® posts. This makes it possible to reproduce the reactions of fans by analyzing social media activities. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI analyze the talent's social media activities and reflect the reactions of fans.

[0042] The analysis unit can analyze evaluation data of a talent's appearances in films and performances during the analysis process and reflect this in the reproduction. For example, the analysis unit can analyze evaluation data of a talent's film appearances. For example, the analysis unit can analyze evaluation data of a talent's drama appearances. For example, the analysis unit can analyze evaluation data of a talent's drama appearances. For example, the analysis unit can analyze evaluation data of a talent's live performances. For example, the analysis unit can analyze evaluation data of a talent's live performances. By analyzing evaluation data, a more accurate reproduction becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI analyze evaluation data of a talent's appearances in films and performances and reflect this in the reproduction.

[0043] The reproduction unit can reproduce the talent's movements and gestures during the reproduction process, thereby achieving a realistic reproduction. For example, the reproduction unit can reproduce the talent's hand movements. The reproduction unit can also reproduce the talent's walking style. The reproduction unit can also reproduce the talent's facial expressions. By reproducing the talent's movements and gestures, a more realistic reproduction becomes possible. Some or all of the above-described processes in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can have AI reproduce the talent's movements and gestures to achieve a realistic reproduction.

[0044] The recreation unit can recreate the atmosphere by recreating the talent's costume and background during the recreation process. For example, the recreation unit can recreate the costume worn by the talent. The recreation unit can also recreate the set of the program in which the talent appeared. The recreation unit can also recreate the background of the event in which the talent appeared. By recreating the talent's costume and background, the atmosphere of that time can be recreated. Some or all of the above processing in the recreation unit may be performed using AI, for example, or without AI. For example, the recreation unit can have AI recreate the talent's costume and background to recreate the atmosphere.

[0045] The reproduction unit can combine the appearance and voice of a talent from multiple periods during the reproduction process. For example, the reproduction unit can combine the appearance of a talent at the time of their debut with their current voice. The reproduction unit can also combine the appearance of a talent from their middle period with their early voice. For example, the reproduction unit can combine the appearance of a talent from their middle period with their early voice. The reproduction unit can also combine the appearance of a talent from their past period with their past voice. For example, the reproduction unit can combine the appearance and voice of a talent from different periods, enabling a wider variety of reproductions. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can have AI combine the appearance and voice of a talent from multiple periods and then perform the reproduction.

[0046] The reproduction unit can reproduce the appearance and voice of a talent in multiple situations during the reproduction process. For example, the reproduction unit can reproduce the appearance and voice of a talent during a live performance. For example, the reproduction unit can reproduce the appearance and voice of a talent during an interview. For example, the reproduction unit can reproduce the appearance and voice of a talent during a movie appearance. For example, the reproduction unit can reproduce the appearance and voice of a talent during a movie appearance. This allows for a wider variety of reproductions by reproducing appearances and voices in different situations. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can have AI reproduce the appearance and voice of a talent in different situations.

[0047] The advertising department can create multiple advertising variations using reconstructed data of the talent during the advertising production process and select the most suitable advertisement. For example, the advertising department can create advertising variations using the talent's appearance at different points in time. The advertising department can also create advertising variations using the talent's appearance in different situations. The advertising department can also create advertising variations using the talent's voice tone. By creating multiple advertising variations, the advertising department can select the most suitable advertisement. Some or all of the above processes in the advertising department may be performed using AI, for example, or not. For example, the advertising department can input reconstructed data of the talent into AI and have the AI ​​produce multiple advertising variations and select the most suitable advertisement.

[0048] The advertising department can create interactive advertisements using reconstructed data of talent during the advertising production process. For example, the advertising department can create interactive advertisements in which users can interact with talent. The advertising department can also create interactive advertisements in which users can select the actions of talent. For example, the advertising department can create interactive advertisements in which users can select the actions of talent. The advertising department can also create interactive advertisements in which users can select the voice of talent. This enables two-way communication with users by creating interactive advertisements. Some or all of the above processes in the advertising department may be performed using AI, for example, or not using AI. For example, the advertising department can input reconstructed data of talent into AI and have the AI ​​produce interactive advertisements.

[0049] The advertising department can create advertisements for multiple media using reconstructed data of talent during the advertising production process. For example, the advertising department can create advertisements for television commercials. For example, the advertising department can create advertisements for television commercials. For example, the advertising department can create advertisements for social media. For example, the advertising department can create advertisements for social media. For example, the advertising department can create advertisements for websites. For example, the advertising department can create advertisements for websites. This allows for a broader advertising campaign by creating advertisements for different media. Some or all of the above processes in the advertising department may be performed using AI, for example, or not using AI. For example, the advertising department can input reconstructed data of talent into AI and have the AI ​​produce advertisements for multiple media.

[0050] The advertising department can create advertisements for multiple target groups using reconstructed data of talent during the advertising production process. For example, the advertising department can create advertisements for young people. For example, the advertising department can create advertisements for young people. The advertising department can also create advertisements for middle-aged and older people. For example, the advertising department can create advertisements for middle-aged and older people. The advertising department can also create advertisements for fans. For example, the advertising department can create advertisements for fans. By creating advertisements for different target groups, a more effective advertising campaign becomes possible. Some or all of the above processes in the advertising department may be performed using AI, for example, or not using AI. For example, the advertising department can input reconstructed data of talent into AI and have the AI ​​produce advertisements for multiple target groups.

[0051] The NFT creation unit can add exclusive content and benefits in addition to the talent's reproduction data when creating an NFT. For example, the NFT creation unit can add a digital poster signed by the talent. The NFT creation unit can also add exclusive interview footage of the talent. The NFT creation unit can also add exclusive live performance footage of the talent. By adding exclusive content and benefits, the value of the NFT can be increased. Some or all of the above processing in the NFT creation unit may be performed using AI, for example, or without AI. For example, the NFT creation unit can have AI perform the addition of exclusive content and benefits in addition to the talent's reproduction data.

[0052] The NFT creation unit can, during the NFT creation process, hold online events using the talent's recreated data and offer participation rights as NFTs. For example, the NFT creation unit can sell participation rights to a talent's virtual fan meeting as an NFT. The NFT creation unit can also sell participation rights to a talent's virtual live performance as an NFT. The NFT creation unit can also sell participation rights to a talent's virtual interview as an NFT. This allows the NFT creation unit to secure a new revenue stream by selling participation rights to virtual events as NFTs. Some or all of the above-described processes in the NFT creation unit may be performed using AI, for example, or without AI. For example, the NFT creation unit can input data recreating a talent into an AI and have the AI ​​execute the creation of an online event and the conversion of participation rights into an NFT.

[0053] The NFT creation unit can sell NFTs on multiple platforms using the talent's reproduction data during the NFT creation process. For example, the NFT creation unit can sell NFTs on online marketplaces. For example, the NFT creation unit can sell NFTs on the talent's official website. For example, the NFT creation unit can sell NFTs on social media platforms. This allows for a broader sales strategy by selling on different platforms. Some or all of the above-described processes in the NFT creation unit may be performed using AI, for example, or not. For example, the NFT creation unit can input the talent's reproduction data into an AI and have the AI ​​execute sales on multiple platforms.

[0054] The NFT creation unit can provide multiple formats of NFTs (e.g., video, audio, 3D model) using the talent's reproduction data during the NFT creation process. For example, the NFT creation unit can provide a reproduction video of a talent as an NFT. The NFT creation unit can also provide a reproduction audio of a talent as an NFT. The NFT creation unit can also provide a 3D model of a talent as an NFT. This allows for the sale of a wider variety of NFTs by providing different formats. Some or all of the above-described processes in the NFT creation unit may be performed using AI, for example, or without AI. For example, the NFT creation unit can input the talent's reproduction data into AI and have the AI ​​perform the task of providing multiple formats of NFTs.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can evaluate the reliability of data when collecting past data on talents and prioritize the collection of highly reliable data. For example, the data collection unit can prioritize the collection of video data provided from public archives. It can also prioritize the collection of posts from talents' official social media accounts. Furthermore, it can prioritize the collection of interview articles provided by highly reliable media outlets. By prioritizing the collection of highly reliable data, the accuracy of the reconstructed data is improved.

[0057] The analysis unit can analyze changes in a talent's facial expressions and voice over time to identify specific periods. For example, it can analyze the frequency of a talent's smiles over time. It can also analyze changes in a talent's voice tone over time. Furthermore, it can analyze changes in a talent's facial expressions over time. By performing this analysis over time, it is possible to identify the talent's most peak period.

[0058] The reproduction unit can reproduce the talent's movements and gestures during the reproduction process, achieving a realistic representation. For example, it can reproduce the talent's hand movements. It can also reproduce the talent's walking style. Furthermore, it can reproduce changes in the talent's facial expressions. By reproducing the talent's movements and gestures, a more realistic representation becomes possible.

[0059] The advertising department can create interactive advertisements using recreated videos of celebrities. For example, they can create interactive advertisements where users can converse with the celebrity. They can also create interactive advertisements where users can select the celebrity's actions. Furthermore, they can create interactive advertisements where users can select the celebrity's voice. This enables two-way communication with users through the creation of interactive advertisements.

[0060] The data collection unit can collect data that reflects the popularity of a talent in different regions by considering the regional variations in the data being collected. For example, it can collect video footage of programs in which a talent has appeared overseas. It can also collect video footage of live performances that a talent has given in different regions. Furthermore, it can collect interview articles that a talent has given in different regions. In this way, by considering regional variations, it is possible to collect data that reflects the popularity of a talent in different regions.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects past data on the talent. For example, it can collect television appearances, magazine photos, and audio data from radio appearances during the talent's peak period. The data collection unit can focus on collecting data from the time when the talent was at their most popular. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can use AI to analyze the collected data and learn the facial features, facial movements, voice characteristics, and speaking style of the talent. The analysis unit can analyze video data to learn the facial features and facial movements of the talent, and analyze audio data to learn the voice characteristics and speaking style of the talent. Step 3: The reproduction unit recreates the appearance and voice of the talent at their peak based on the data analyzed by the analysis unit. For example, an AI model can be built to accurately recreate the appearance and voice of the talent at their peak. Step 4: The advertising department uses the talent data recreated by the recreation department to develop advertising strategies. For example, they can create new commercials using the recreated talent's footage. Step 5: The NFT creation department converts the talent data used by the advertising department into NFTs. For example, the recreated talent data can be sold as an NFT.

[0063] (Example of form 2) The system according to an embodiment of the present invention is a system that uses AI to completely recreate a talent's peak period in order to prevent the decline in talent value due to aging or death. This system collects past video, photograph, and audio data of the talent and analyzes it using AI. Next, based on the analysis results, it recreates the appearance and voice of the talent during their peak period. Using this recreated talent data, the system maximizes the talent's value as an advertising strategy. It also makes established story-type commercials universal and solidifies the brand image. Furthermore, by converting talent value into NFTs, it creates new business models. For example, it collects past video, photograph, and audio data of the talent. In this case, it focuses on collecting data from the talent's peak period. For example, it collects television appearances, magazine photos, and audio data from radio appearances from when the talent was at their most popular. This allows for a detailed understanding of the talent's appearance and voice during their peak period. Next, the collected data is analyzed using AI. AI analyzes collected data to build models that recreate the appearance and voice of a talent at their peak. For example, the AI ​​analyzes video data to learn the talent's facial features and expressions. It also analyzes audio data to learn the talent's voice characteristics and speaking style. This allows for highly accurate recreation of the talent's appearance and voice at their peak. The recreated talent data is then used to maximize the talent's value as an advertising strategy. For example, new commercials are produced using the recreated footage of the talent. Because these commercials recreate the talent's appearance and voice at their peak, they leave a strong impression on viewers. Furthermore, established story-based commercials become universal, solidifying the brand image. For example, continuously broadcasting a series of commercials featuring the talent can strongly imprint the brand image on viewers. In addition, new business models are created by converting talent value into NFTs. For example, the recreated talent data is sold as an NFT. Because this NFT recreates the talent's appearance and voice at their peak, it becomes extremely valuable to fans. Furthermore, the sale of NFTs can secure a new source of revenue.This prevents a decline in talent value and allows for the creation of a sustainable business model.

[0064] The talent value reproduction system according to this embodiment comprises a collection unit, an analysis unit, a reproduction unit, an advertising unit, and an NFT conversion unit. The collection unit collects past data of the talent. For example, the collection unit can collect television appearances, magazine photos, and audio data from radio appearances during the talent's peak period. For example, the collection unit can focus on collecting data from the period when the talent was most popular. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can use AI to analyze the collected data and learn the talent's facial features, facial expressions, voice characteristics, and speaking style. For example, the analysis unit can use AI to analyze video data and learn the talent's facial features and facial expressions. The analysis unit can also analyze audio data and learn the talent's voice characteristics and speaking style. The reproduction unit reproduces the appearance and voice of the talent during their peak period based on the data analyzed by the analysis unit. The reproduction unit can, for example, construct an AI model to accurately reproduce the appearance and voice of a talent at their peak. The reproduction unit can, for example, use an AI model to accurately reproduce the appearance and voice of a talent at their peak. The advertising unit uses the talent data reproduced by the reproduction unit to develop advertising strategies. The advertising unit can, for example, produce new commercials using the reproduced footage of the talent. The advertising unit can, for example, produce new commercials using the reproduced footage of the talent. The NFT unit converts the talent data used by the advertising unit into NFTs. The NFT unit can, for example, sell the reproduced talent data as NFTs. As a result, the talent value reproduction system according to this embodiment can reproduce the talent at their peak and maximize talent value through advertising strategies and NFT conversion.

[0065] The data collection unit can collect television appearances, magazine photos, and audio data from radio appearances of a talent from specific periods. For example, the data collection unit can collect television appearances from when the talent was at their peak. The data collection unit can also collect magazine photos from when the talent was at their peak. For example, the data collection unit can collect magazine photos from when the talent was at their peak. The data collection unit can also collect audio data from radio appearances from when the talent was at their peak. For example, the data collection unit can collect audio data from radio appearances from when the talent was at their peak. This improves the accuracy of the reproduction by focusing on collecting data from when the talent was at their peak. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can use AI to automate data collection in order to collect data from when the talent was at their peak.

[0066] The analysis unit can use AI to analyze collected data and learn the facial features, facial expressions, voice characteristics, and speaking style of the talent. For example, the analysis unit can use AI to analyze collected data and learn the facial features and facial expressions of the talent. For example, the analysis unit can use AI to analyze video data and learn the facial features and facial expressions of the talent. The analysis unit can also analyze audio data and learn the voice characteristics and speaking style of the talent. For example, the analysis unit can use AI to analyze audio data and learn the voice characteristics and speaking style of the talent. The analysis unit can also analyze collected data and build a model to reproduce the appearance and voice of the talent at their peak. For example, the analysis unit can use AI to analyze collected data and build a model to reproduce the appearance and voice of the talent at their peak. By using AI, the characteristics of the talent can be learned with high accuracy, and the accuracy of reproduction can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into the AI ​​and have the AI ​​learn the characteristics of the talent.

[0067] The reproduction unit can construct an AI model to accurately reproduce the appearance and voice of a talent at a specific time. For example, the reproduction unit can construct an AI model to accurately reproduce the appearance and voice of a talent at their peak. For example, the reproduction unit can accurately reproduce the appearance and voice of a talent at their peak using an AI model. The reproduction unit can also construct an AI model to reproduce the appearance and voice of a talent at their peak. For example, the reproduction unit can construct an AI model to reproduce the appearance and voice of a talent at their peak using AI. This makes it possible to accurately reproduce the appearance and voice of a talent at their peak using an AI model. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can construct an AI model to reproduce the appearance and voice of a talent at their peak and perform the reproduction using that model.

[0068] The advertising department can produce commercials using the recreated footage of the talent. The advertising department can, for example, produce new commercials using the recreated footage of the talent. The advertising department can also produce commercials that leave a strong impression on viewers using the recreated footage of the talent. The advertising department can produce commercials that leave a strong impression on viewers using the recreated footage of the talent. Some or all of the above processes in the advertising department may be performed using AI, for example, or not using AI. For example, the advertising department can input the recreated footage of the talent into AI and have AI produce the commercials.

[0069] The NFT creation unit can provide the recreated talent data as an NFT. The NFT creation unit can, for example, sell the recreated talent data as an NFT. The NFT creation unit can also secure a new revenue stream by providing the recreated talent data as an NFT. The NFT creation unit can secure a new revenue stream by providing the recreated talent data as an NFT. This allows it to secure a new revenue stream by selling the recreated talent data as an NFT. Some or all of the above processing in the NFT creation unit may be performed using AI, for example, or without AI. For example, the NFT creation unit can input the recreated talent data into AI and have AI perform the provision as an NFT.

[0070] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is feeling nostalgic, the data collection unit can prioritize collecting videos of popular past programs. For example, if the user is feeling nostalgic, the data collection unit can prioritize collecting videos of popular past programs. For example, if the user is excited, the data collection unit can prioritize collecting videos of live performances by celebrities. For example, if the user is relaxed, the data collection unit can prioritize collecting videos of interviews with celebrities. For example, if the user is relaxed, the data collection unit can prioritize collecting videos of interviews with celebrities. By prioritizing data based on the user's emotions, more appropriate data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotions into AI and have AI determine the priority of data to collect.

[0071] The data collection unit can evaluate the reliability of data when collecting past data on a talent and prioritize the collection of highly reliable data. For example, the data collection unit can prioritize the collection of video data provided from public archives. The data collection unit can also prioritize the collection of posts from a talent's official social media accounts. The data collection unit can also prioritize the collection of interview articles provided from highly reliable media. By prioritizing the collection of highly reliable data, the accuracy of the reproduction is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI evaluate the reliability of the data and have AI collect highly reliable data.

[0072] The data collection unit can expand the types of data it collects, including interview articles of talents and records of fan interaction events. For example, the data collection unit can collect magazine interview articles of talents. For example, the data collection unit can collect magazine interview articles of talents. The data collection unit can also collect videos of fan meetings in which talents appeared. For example, the data collection unit can collect audio data of talents appearing on radio programs. For example, the data collection unit can collect audio data of talents appearing on radio programs. By expanding the types of data collected, a wider variety of data can be collected. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can have AI expand the types of data it collects, and have AI perform the collection of interview articles of talents and records of fan interaction events.

[0073] The data collection unit can estimate the user's emotions and filter the data to be collected based on the estimated emotions. For example, if the user is feeling nostalgic, the data collection unit can filter and provide past video data. For example, if the user is feeling nostalgic, the data collection unit can filter and provide past video data. For example, if the user is excited, the data collection unit can filter and provide live performance videos. For example, if the user is relaxed, the data collection unit can filter and provide interview videos. For example, if the user is relaxed, the data collection unit can filter and provide interview videos. By filtering data based on the user's emotions, more appropriate data can be provided. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can have AI estimate the user's emotions and have AI perform the filtering of the data to be collected.

[0074] The data collection unit can collect data that reflects the popularity of a talent in different regions by considering regional variations in the data being collected. For example, the data collection unit can collect video footage of a talent appearing on a program overseas. For example, the data collection unit can collect video footage of a talent appearing on a program overseas. The data collection unit can also collect video footage of a talent performing live in different regions. For example, the data collection unit can collect video footage of a talent performing live in different regions. The data collection unit can also collect interview articles conducted by a talent in different regions. For example, the data collection unit can collect interview articles conducted by a talent in different regions. By considering regional variations, the data collection unit can collect data that reflects the popularity of a talent in different regions. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can have AI consider regional variations and have AI collect data that reflects the popularity of a talent in different regions.

[0075] The data collection unit can collect data throughout a talent's entire career by considering the temporal variations in the data being collected. For example, the data collection unit can collect video footage of a talent from the time of their debut. For example, the data collection unit can collect interview articles from the middle of a talent's career. For example, the data collection unit can collect data on a talent's current activities. For example, the data collection unit can collect data on a talent's current activities. In this way, by considering the temporal variations, data can be collected throughout a talent's entire career. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can have AI consider the temporal variations and have AI perform the collection of data throughout a talent's entire career.

[0076] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is feeling nostalgic, the analysis unit can analyze past data in detail. For example, if the user is feeling nostalgic, the analysis unit can analyze past data in detail. The analysis unit can also analyze live performance data in detail if the user is excited. For example, if the user is excited, the analysis unit can analyze live performance data in detail. The analysis unit can also analyze interview video data in detail if the user is relaxed. For example, if the user is relaxed, the analysis unit can analyze interview video data in detail. By adjusting the accuracy of the analysis based on the user's emotions, more appropriate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI estimate the user's emotions and have AI adjust the accuracy of the analysis.

[0077] The analysis unit can analyze changes in the talent's facial expressions and voice over time to identify specific periods. For example, the analysis unit can analyze the frequency of the talent's smiles over time. The analysis unit can also analyze changes in the talent's voice tone over time. The analysis unit can also analyze changes in the talent's facial expressions over time. By performing analysis along the time axis, the analysis unit can identify the talent's peak period. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI analyze changes in the talent's facial expressions and voice, and have the AI ​​identify the peak period.

[0078] The analysis unit can also analyze changes in the talent's fashion and style during the analysis process and reflect these changes in the reproduction. For example, the analysis unit can analyze the changes in the talent's costumes. The analysis unit can also analyze changes in the talent's hairstyles. The analysis unit can also analyze changes in the talent's makeup. By analyzing changes in fashion and style, a more realistic reproduction becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI analyze changes in the talent's fashion and style and reflect these changes in the reproduction.

[0079] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling nostalgic, the analysis unit can emphasize and display past footage. For example, if the user is feeling nostalgic, the analysis unit can emphasize and display past footage. For example, if the user is excited, the analysis unit can emphasize and display live performance footage. For example, if the user is relaxed, the analysis unit can emphasize and display interview footage. For example, if the user is relaxed, the analysis unit can emphasize and display interview footage. By adjusting the display method based on the user's emotions, a more appropriate display becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI estimate the user's emotions and have AI adjust the display method of the analysis results.

[0080] The analysis unit can analyze the talent's social media activities during analysis and reflect the reactions of fans. For example, the analysis unit can analyze the reactions to the talent's tweets. The analysis unit can also analyze the reactions to the talent's Instagram posts. The analysis unit can also analyze the reactions to the talent's Facebook posts. This makes it possible to reproduce the reactions of fans by analyzing social media activities. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI analyze the talent's social media activities and reflect the reactions of fans.

[0081] The analysis unit can analyze evaluation data of a talent's appearances in films and performances during the analysis process and reflect this in the reproduction. For example, the analysis unit can analyze evaluation data of a talent's film appearances. For example, the analysis unit can analyze evaluation data of a talent's drama appearances. For example, the analysis unit can analyze evaluation data of a talent's drama appearances. For example, the analysis unit can analyze evaluation data of a talent's live performances. For example, the analysis unit can analyze evaluation data of a talent's live performances. By analyzing evaluation data, a more accurate reproduction becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI analyze evaluation data of a talent's appearances in films and performances and reflect this in the reproduction.

[0082] The reproduction unit can estimate the user's emotions and adjust the details of the reproduced talent's facial expressions and voice based on the estimated emotions. For example, if the user is feeling nostalgic, the reproduction unit can emphasize the talent's smile. The reproduction unit can also raise the tone of the talent's voice if the user is excited. The reproduction unit can also set the talent's facial expression to be gentle if the user is relaxed. This allows for a more realistic reproduction by adjusting the details of the talent's facial expressions and voice based on the user's emotions. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can have AI estimate the user's emotions and have AI adjust the details of the reproduced talent's facial expressions and voice.

[0083] The reproduction unit can reproduce the talent's movements and gestures during the reproduction process, thereby achieving a realistic reproduction. For example, the reproduction unit can reproduce the talent's hand movements. The reproduction unit can also reproduce the talent's walking style. The reproduction unit can also reproduce the talent's facial expressions. By reproducing the talent's movements and gestures, a more realistic reproduction becomes possible. Some or all of the above-described processes in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can have AI reproduce the talent's movements and gestures to achieve a realistic reproduction.

[0084] The recreation unit can recreate the atmosphere by recreating the talent's costume and background during the recreation process. For example, the recreation unit can recreate the costume worn by the talent. The recreation unit can also recreate the set of the program in which the talent appeared. The recreation unit can also recreate the background of the event in which the talent appeared. By recreating the talent's costume and background, the atmosphere of that time can be recreated. Some or all of the above processing in the recreation unit may be performed using AI, for example, or without AI. For example, the recreation unit can have AI recreate the talent's costume and background to recreate the atmosphere.

[0085] The reproduction unit can estimate the user's emotions and provide variations in the appearance and voice of the talent to be reproduced based on the estimated user's emotions. For example, if the user is feeling nostalgic, the reproduction unit can reproduce a past appearance. For example, if the user is feeling nostalgic, the reproduction unit can reproduce a past appearance. For example, if the user is excited, the reproduction unit can reproduce a live performance. For example, if the user is excited, the reproduction unit can reproduce a live performance. For example, if the user is relaxed, the reproduction unit can reproduce an interview appearance. For example, if the user is relaxed, the reproduction unit can reproduce an interview appearance. This allows for a wider variety of reproductions by providing variations in the appearance and voice of the talent based on the user's emotions. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can have AI estimate the user's emotions and have AI provide variations in the appearance and voice of the talent to be reproduced.

[0086] The reproduction unit can combine the appearance and voice of a talent from multiple periods during the reproduction process. For example, the reproduction unit can combine the appearance of a talent at the time of their debut with their current voice. The reproduction unit can also combine the appearance of a talent from their middle period with their early voice. For example, the reproduction unit can combine the appearance of a talent from their middle period with their early voice. The reproduction unit can also combine the appearance of a talent from their past period with their past voice. For example, the reproduction unit can combine the appearance and voice of a talent from different periods, enabling a wider variety of reproductions. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can have AI combine the appearance and voice of a talent from multiple periods and then perform the reproduction.

[0087] The reproduction unit can reproduce the appearance and voice of a talent in multiple situations during the reproduction process. For example, the reproduction unit can reproduce the appearance and voice of a talent during a live performance. For example, the reproduction unit can reproduce the appearance and voice of a talent during an interview. For example, the reproduction unit can reproduce the appearance and voice of a talent during a movie appearance. For example, the reproduction unit can reproduce the appearance and voice of a talent during a movie appearance. This allows for a wider variety of reproductions by reproducing appearances and voices in different situations. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can have AI reproduce the appearance and voice of a talent in different situations.

[0088] The advertising department can estimate the user's emotions and adjust the content and presentation of the advertisement based on those estimated emotions. For example, if the user is feeling nostalgic, the advertising department can create an advertisement using footage from the past. For example, if the user is feeling nostalgic, the advertising department can create an advertisement using footage from the past. For example, if the user is feeling excited, the advertising department can create an advertisement using footage from a live performance. For example, if the user is feeling relaxed, the advertising department can create an advertisement using footage from an interview. For example, if the user is feeling relaxed, the advertising department can create an advertisement using footage from an interview. By adjusting the content and presentation of the advertisement based on the user's emotions, more effective advertisements can be produced. Some or all of the above processes in the advertising department may be performed using AI, for example, or not. For example, the advertising department can have AI estimate the user's emotions and have the AI ​​adjust the content and presentation of the advertisement.

[0089] The advertising department can create multiple advertising variations using reconstructed data of the talent during the advertising production process and select the most suitable advertisement. For example, the advertising department can create advertising variations using the talent's appearance at different points in time. The advertising department can also create advertising variations using the talent's appearance in different situations. The advertising department can also create advertising variations using the talent's voice tone. By creating multiple advertising variations, the advertising department can select the most suitable advertisement. Some or all of the above processes in the advertising department may be performed using AI, for example, or not. For example, the advertising department can input reconstructed data of the talent into AI and have the AI ​​produce multiple advertising variations and select the most suitable advertisement.

[0090] The advertising department can create interactive advertisements using reconstructed data of talent during the advertising production process. For example, the advertising department can create interactive advertisements in which users can interact with talent. The advertising department can also create interactive advertisements in which users can select the actions of talent. For example, the advertising department can create interactive advertisements in which users can select the actions of talent. The advertising department can also create interactive advertisements in which users can select the voice of talent. This enables two-way communication with users by creating interactive advertisements. Some or all of the above processes in the advertising department may be performed using AI, for example, or not using AI. For example, the advertising department can input reconstructed data of talent into AI and have the AI ​​produce interactive advertisements.

[0091] The advertising department can estimate the user's emotions and adjust the timing of ad delivery based on those emotions. For example, if the user is feeling nostalgic, the advertising department can deliver ads using past footage. For example, if the user is feeling nostalgic, the advertising department can deliver ads using past footage. For example, if the user is excited, the advertising department can deliver ads using live performance footage. For example, if the user is relaxed, the advertising department can deliver ads using interview footage. By adjusting the timing of ad delivery based on the user's emotions, more effective ad delivery becomes possible. Some or all of the above processes in the advertising department may be performed using AI, for example, or not. For example, the advertising department can have AI estimate the user's emotions and have the AI ​​adjust the timing of ad delivery.

[0092] The advertising department can create advertisements for multiple media using reconstructed data of talent during the advertising production process. For example, the advertising department can create advertisements for television commercials. For example, the advertising department can create advertisements for television commercials. For example, the advertising department can create advertisements for social media. For example, the advertising department can create advertisements for social media. For example, the advertising department can create advertisements for websites. For example, the advertising department can create advertisements for websites. This allows for a broader advertising campaign by creating advertisements for different media. Some or all of the above processes in the advertising department may be performed using AI, for example, or not using AI. For example, the advertising department can input reconstructed data of talent into AI and have the AI ​​produce advertisements for multiple media.

[0093] The advertising department can create advertisements for multiple target groups using reconstructed data of talent during the advertising production process. For example, the advertising department can create advertisements for young people. For example, the advertising department can create advertisements for young people. The advertising department can also create advertisements for middle-aged and older people. For example, the advertising department can create advertisements for middle-aged and older people. The advertising department can also create advertisements for fans. For example, the advertising department can create advertisements for fans. By creating advertisements for different target groups, a more effective advertising campaign becomes possible. Some or all of the above processes in the advertising department may be performed using AI, for example, or not using AI. For example, the advertising department can input reconstructed data of talent into AI and have the AI ​​produce advertisements for multiple target groups.

[0094] The NFT creation unit can estimate the user's emotions and evaluate the value of the NFT based on those estimated emotions. For example, if the user is feeling nostalgic, the NFT creation unit can highly value an NFT that uses past footage. For example, if the user is excited, the NFT creation unit can highly value an NFT that uses live performance footage. For example, if the user is relaxed, the NFT creation unit can highly value an NFT that uses interview footage. This allows for more appropriate valuation by evaluating the value of the NFT based on the user's emotions. Some or all of the above processing in the NFT creation unit may be performed using AI, for example, or not. For example, the NFT creation unit can have AI estimate the user's emotions and have AI perform the evaluation of the NFT's value.

[0095] The NFT creation unit can add exclusive content and benefits in addition to the talent's reproduction data when creating an NFT. For example, the NFT creation unit can add a digital poster signed by the talent. The NFT creation unit can also add exclusive interview footage of the talent. The NFT creation unit can also add exclusive live performance footage of the talent. By adding exclusive content and benefits, the value of the NFT can be increased. Some or all of the above processing in the NFT creation unit may be performed using AI, for example, or without AI. For example, the NFT creation unit can have AI perform the addition of exclusive content and benefits in addition to the talent's reproduction data.

[0096] The NFT creation unit can, during the NFT creation process, hold online events using the talent's recreated data and offer participation rights as NFTs. For example, the NFT creation unit can sell participation rights to a talent's virtual fan meeting as an NFT. The NFT creation unit can also sell participation rights to a talent's virtual live performance as an NFT. The NFT creation unit can also sell participation rights to a talent's virtual interview as an NFT. This allows the NFT creation unit to secure a new revenue stream by selling participation rights to virtual events as NFTs. Some or all of the above-described processes in the NFT creation unit may be performed using AI, for example, or without AI. For example, the NFT creation unit can input data recreating a talent into an AI and have the AI ​​execute the creation of an online event and the conversion of participation rights into an NFT.

[0097] The NFT creation unit can estimate the user's emotions and adjust the NFT sales strategy based on those emotions. For example, if the user is feeling nostalgic, the NFT creation unit can prioritize selling NFTs using past footage. For example, if the user is feeling nostalgic, the NFT creation unit can prioritize selling NFTs using past footage. For example, if the user is excited, the NFT creation unit can prioritize selling NFTs using live performance footage. For example, if the user is relaxed, the NFT creation unit can prioritize selling NFTs using interview footage. For example, if the user is relaxed, the NFT creation unit can prioritize selling NFTs using interview footage. By adjusting the NFT sales strategy based on the user's emotions, more effective sales become possible. Some or all of the above processing in the NFT creation unit may be performed using AI, for example, or not. For example, the NFT (Non-Functional Technology) unit can have AI estimate user emotions and then have the AI ​​adjust the NFT sales strategy.

[0098] The NFT creation unit can sell NFTs on multiple platforms using the talent's reproduction data during the NFT creation process. For example, the NFT creation unit can sell NFTs on online marketplaces. For example, the NFT creation unit can sell NFTs on the talent's official website. For example, the NFT creation unit can sell NFTs on social media platforms. This allows for a broader sales strategy by selling on different platforms. Some or all of the above-described processes in the NFT creation unit may be performed using AI, for example, or not. For example, the NFT creation unit can input the talent's reproduction data into an AI and have the AI ​​execute sales on multiple platforms.

[0099] The NFT creation unit can provide multiple formats of NFTs (e.g., video, audio, 3D model) using the talent's reproduction data during the NFT creation process. For example, the NFT creation unit can provide a reproduction video of a talent as an NFT. The NFT creation unit can also provide a reproduction audio of a talent as an NFT. The NFT creation unit can also provide a 3D model of a talent as an NFT. This allows for the sale of a wider variety of NFTs by providing different formats. Some or all of the above-described processes in the NFT creation unit may be performed using AI, for example, or without AI. For example, the NFT creation unit can input the talent's reproduction data into AI and have the AI ​​perform the task of providing multiple formats of NFTs. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, analysis unit, reproduction unit, advertising unit, and NFT conversion unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects past video and audio data of the talent using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and analyzes the collected data using AI. The reproduction unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and reproduces the appearance and voice of the talent at their peak based on the analysis results. The advertising unit is implemented, for example, in the control unit 46A of the smart device 14, and produces a new commercial using the reproduced talent data. The NFT conversion unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and sells the reproduced talent data as an NFT. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, analysis unit, reproduction unit, advertising unit, and NFT conversion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects past video and audio data of the talent using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The reproduction unit is implemented in the identification processing unit 290 of the data processing unit 12 and reproduces the appearance and voice of the talent at their peak based on the analysis results. The advertising unit is implemented in the control unit 46A of the smart glasses 214 and produces a new commercial using the reproduced talent data. The NFT conversion unit is implemented in the identification processing unit 290 of the data processing unit 12 and sells the reproduced talent data as an NFT. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, analysis unit, reproduction unit, advertising unit, and NFT conversion unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects past video and audio data of the talent using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data using AI. The reproduction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and reproduces the appearance and voice of the talent at their peak based on the analysis results. The advertising unit is implemented, for example, by the control unit 46A of the headset terminal 314, and produces a new commercial using the reproduced talent data. The NFT conversion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and sells the reproduced talent data as an NFT. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, analysis unit, reproduction unit, advertising unit, and NFT conversion unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects past video and audio data of the talent using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The reproduction unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and reproduces the appearance and voice of the talent at their peak based on the analysis results. The advertising unit is implemented by, for example, the control unit 46A of the robot 414 and produces a new commercial using the reproduced talent data. The NFT conversion unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and sells the reproduced talent data as an NFT.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The talent value reproduction system can further include a customization unit that estimates the user's emotions and customizes the talent's reproduction data based on those emotions. For example, if the user is feeling nostalgic, the customization unit can emphasize and reproduce past footage of the talent. If the user is excited, it can emphasize and reproduce live performance footage of the talent. Furthermore, if the user is relaxed, it can emphasize and reproduce interview footage of the talent. This allows for a more personalized experience by providing customized reproduction data that responds to the user's emotions.

[0102] The data collection unit can evaluate the reliability of data when collecting past data on talents and prioritize the collection of highly reliable data. For example, the data collection unit can prioritize the collection of video data provided from public archives. It can also prioritize the collection of posts from talents' official social media accounts. Furthermore, it can prioritize the collection of interview articles provided by highly reliable media outlets. By prioritizing the collection of highly reliable data, the accuracy of the reconstructed data is improved.

[0103] The analysis unit can analyze changes in a talent's facial expressions and voice over time to identify specific periods. For example, it can analyze the frequency of a talent's smiles over time. It can also analyze changes in a talent's voice tone over time. Furthermore, it can analyze changes in a talent's facial expressions over time. By performing this analysis over time, it is possible to identify the talent's most peak period.

[0104] The reproduction unit can reproduce the talent's movements and gestures during the reproduction process, achieving a realistic representation. For example, it can reproduce the talent's hand movements. It can also reproduce the talent's walking style. Furthermore, it can reproduce changes in the talent's facial expressions. By reproducing the talent's movements and gestures, a more realistic representation becomes possible.

[0105] The advertising department can create interactive advertisements using recreated videos of celebrities. For example, they can create interactive advertisements where users can converse with the celebrity. They can also create interactive advertisements where users can select the celebrity's actions. Furthermore, they can create interactive advertisements where users can select the celebrity's voice. This enables two-way communication with users through the creation of interactive advertisements.

[0106] The NFT creation unit can estimate the user's emotions and evaluate the value of the NFT based on those emotions. For example, if the user is feeling nostalgic, the value of an NFT using past footage can be highly valued. Similarly, if the user is excited, the value of an NFT using live performance footage can be highly valued. Furthermore, if the user is relaxed, the value of an NFT using interview footage can be highly valued. This allows for more appropriate valuation by evaluating the value of NFTs based on the user's emotions.

[0107] The data collection unit can collect data that reflects the popularity of a talent in different regions by considering the regional variations in the data being collected. For example, it can collect video footage of programs in which a talent has appeared overseas. It can also collect video footage of live performances that a talent has given in different regions. Furthermore, it can collect interview articles that a talent has given in different regions. In this way, by considering regional variations, it is possible to collect data that reflects the popularity of a talent in different regions.

[0108] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on those emotions. For example, if the user is feeling nostalgic, it can analyze past data in detail. If the user is excited, it can also analyze live performance data in detail. Furthermore, if the user is relaxed, it can analyze interview video data in detail. By adjusting the accuracy of the analysis based on the user's emotions, more appropriate analysis results can be obtained.

[0109] The reproduction unit can estimate the user's emotions and adjust the details of the reproduced talent's facial expressions and voice based on the estimated emotions. For example, if the user is feeling nostalgic, the talent's smile can be emphasized. If the user is excited, the talent's voice tone can be set higher. Furthermore, if the user is relaxed, the talent's facial expression can be set to be gentler. By adjusting the details of the talent's facial expressions and voice based on the user's emotions, a more realistic reproduction becomes possible.

[0110] The advertising department can estimate user emotions and adjust ad delivery timing based on those emotions. For example, if a user is feeling nostalgic, ads using past footage can be delivered. If a user is excited, ads using live performance footage can be delivered. Furthermore, if a user is relaxed, ads using interview footage can be delivered. By adjusting ad delivery timing based on user emotions, more effective ad delivery becomes possible.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The data collection unit collects past data on the talent. For example, it can collect television appearances, magazine photos, and audio data from radio appearances during the talent's peak period. The data collection unit can focus on collecting data from the time when the talent was at their most popular. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can use AI to analyze the collected data and learn the facial features, facial movements, voice characteristics, and speaking style of the talent. The analysis unit can analyze video data to learn the facial features and facial movements of the talent, and analyze audio data to learn the voice characteristics and speaking style of the talent. Step 3: The reproduction unit recreates the appearance and voice of the talent at their peak based on the data analyzed by the analysis unit. For example, an AI model can be built to accurately recreate the appearance and voice of the talent at their peak. Step 4: The advertising department uses the talent data recreated by the recreation department to develop advertising strategies. For example, they can create new commercials using the recreated talent's footage. Step 5: The NFT creation department converts the talent data used by the advertising department into NFTs. For example, the recreated talent data can be sold as an NFT.

[0113] 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.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0115] 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.

[0116] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] 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.

[0132] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] 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.

[0148] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] 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.

[0165] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of Symbols]

[0185] 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. The collection department collects past data on talents, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the data analyzed by the aforementioned analysis unit, a reproduction unit recreates the appearance and voice of the talent at a specific time period. The advertising department uses the talent data reproduced by the aforementioned reproduction unit to develop advertising strategies, The system includes an NFT conversion unit that converts the talent data used by the advertising department into NFTs. A system characterized by the following features.

2. The aforementioned collection unit is This involves collecting video footage of a celebrity's television appearances, magazine photos, and audio data from their radio appearances from specific periods in their career. The system according to feature 1.

3. The aforementioned analysis unit, AI analyzes collected data to learn the facial features, facial expressions, voice characteristics, and speaking style of the talent. The system according to feature 1.

4. The reproduction unit is, We will build an AI model to accurately reproduce the appearance and voice of a talent at a specific time period. The system according to feature 1.

5. The aforementioned advertising department, We will create a commercial using recreated footage of the celebrity. The system according to feature 1.

6. The aforementioned NFT conversion unit is The recreated talent data will be provided as an NFT. The system according to feature 1.

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

8. The aforementioned collection unit is When collecting past data on talents, the reliability of the data is evaluated, and reliable data is prioritized for collection. The system according to feature 1.

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