System

Abstract

An object of a system according to an embodiment is to efficiently perform creation, promotion, and monetization of a character using a generated AI.SOLUTION: A system according to an embodiment includes a generation unit, a story generation unit, a promotion unit, and a monetization unit. The generation unit generates the appearance or personality of the character using the generation AI. The story generation unit generates a background story of the character using the generation AI. The promotion unit promotes the character generated by the generation unit and the story generation unit. The monetization unit monetizes the character promoted by the promotion unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process of creating, promoting, and monetizing characters was complex and difficult to carry out efficiently.

[0005] The system of the embodiment aims to efficiently carry out character creation, promotion, and monetization using generation AI. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a story generation unit, a promotion unit, and a monetization unit. The generation unit generates a character's appearance or personality using a generation AI. The story generation unit generates a character's background story using the generation AI. The promotion unit promotes the character generated by the generation unit and the story generation unit. The monetization unit monetizes the character promoted by the promotion unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently create characters, promote them, and monetize them using generation AI. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An entertainment system according to an embodiment of the present invention uses a generative AI to generate character appearances, personalities, and background stories, and promotes and monetizes them. The entertainment system provides new value to the entertainment industry by using a generative AI to generate character appearances, personalities, and background stories, and by promoting and monetizing them. For example, the entertainment system uses a generative AI to create unique and diverse characters. The generative AI generates character appearances, personalities, and background stories. For example, the generative AI generates characters such as a "hero in a fantasy world" or a "detective in a futuristic city." The entertainment system then promotes the generated characters. For example, the entertainment system manages social media accounts for the characters to interact with fans. The entertainment system also generates revenue by planning advertising and events featuring the characters. Furthermore, the entertainment system includes monetization methods such as advertising, events, and merchandise sales. For example, the entertainment system develops advertising campaigns using the characters to earn advertising revenue from companies. The entertainment system also sells merchandise featuring the characters to generate revenue from fans. This allows the entertainment system to utilize a generative AI to create unique VTuber characters, providing a next-generation entertainment hub that connects talented creators with viewers. This allows entertainment systems to use generative AI to create new characters and content that could not be achieved using conventional methods. For example, generative AI can automatically generate character stories and provide them to viewers, allowing them to enjoy constantly new content. This will provide viewers with new entertainment experiences and evolve the entire industry.

[0029] An entertainment system according to an embodiment includes a generation unit, a story generation unit, a promotion unit, and a monetization unit. The generation unit generates a character's appearance or personality using a generation AI. The generation unit generates a character's appearance or personality using, for example, deep learning. The generation unit can also generate a character's appearance or personality using the generation AI. For example, the generation unit can generate a character's appearance using deep learning. The generation unit can also generate a character's personality using deep learning. The story generation unit generates a character's background story using the generation AI. The story generation unit generates a character's background story using, for example, natural language processing. The story generation unit can also generate a character's background story using the generation AI. For example, the story generation unit can generate a character's background story using natural language processing. The story generation unit can also generate a character's background story using natural language processing. The promotion unit promotes the character generated by the generation unit and the story generation unit. The promotion unit, for example, manages the character's social media accounts to interact with fans. The promotion unit can also plan advertisements and events using the character to generate revenue. For example, the promotion department can manage social media accounts for characters and interact with fans. The promotion department can also plan advertisements and events using the characters to generate revenue. The monetization department monetizes the characters promoted by the promotion department. For example, the monetization department can develop advertising campaigns using the characters to receive advertising revenue from companies. The monetization department can also sell merchandise for the characters to generate revenue from fans. For example, the monetization department can develop advertising campaigns using the characters to receive advertising revenue from companies. The monetization department can also sell merchandise for the characters to generate revenue from fans.As a result, the entertainment system according to the embodiment can provide new value to the entertainment industry by using generative AI to generate character appearances, personalities, and background stories, and then promoting and monetizing them.

[0030] The generation unit may generate the character's appearance or personality using deep learning. Deep learning includes, but is not limited to, a convolutional neural network (CNN) or a recurrent neural network (RNN). The generation unit may generate the character's appearance using, for example, a convolutional neural network (CNN). The generation unit may also generate the character's personality using a recurrent neural network (RNN). The generation unit may also generate the character's appearance or personality using a generation AI. For example, the generation unit may generate the character's appearance using deep learning. The generation unit may also generate the character's personality using deep learning. This allows for the generation of more realistic and unique characters by using deep learning.

[0031] The story generation unit can generate a character's background story using natural language processing. Natural language processing includes, but is not limited to, for example, morphological analysis, grammatical analysis, and semantic analysis. For example, the story generation unit can generate a character's background story using morphological analysis. The story generation unit can also generate a character's background story using grammatical analysis. The story generation unit can also generate a character's background story using semantic analysis. For example, the story generation unit can generate a character's background story using morphological analysis. The story generation unit can also generate a character's background story using grammatical analysis. In this way, by using natural language processing, it is possible to generate a character's background story in a more natural and appealing way.

[0032] The promotion department may operate a social media account for the character to interact with fans. Examples of social media accounts include, but are not limited to, Twitter (registered trademark), Instagram (registered trademark), and Facebook (registered trademark). For example, the promotion department may operate a Twitter account to interact with fans. The promotion department may also operate an Instagram account to interact with fans. The promotion department may also operate a Facebook account to interact with fans. For example, the promotion department may operate a Twitter account to interact with fans. The promotion department may also operate an Instagram account to interact with fans. By interacting with fans through the social media accounts, the character's popularity and fan engagement can be increased.

[0033] The monetization unit can develop advertising campaigns using the characters and earn advertising revenue from companies. Examples of advertising campaigns include, but are not limited to, online advertising, television commercials, and event advertising. For example, the monetization unit can develop online advertising and earn advertising revenue from companies. The monetization unit can also develop television commercials and earn advertising revenue from companies. The monetization unit can also develop event advertising and earn advertising revenue from companies. For example, the monetization unit can develop online advertising and earn advertising revenue from companies. The monetization unit can also develop television commercials and earn advertising revenue from companies. In this way, advertising campaigns using the characters can earn advertising revenue from companies.

[0034] The monetization department can sell character merchandise to increase revenue from fans. Examples of merchandise include, but are not limited to, figurines, T-shirts, and posters. For example, the monetization department can sell figurines to increase revenue from fans. Alternatively, the monetization department can sell T-shirts to increase revenue from fans. Alternatively, the monetization department can sell posters to increase revenue from fans. For example, the monetization department can sell figurines to increase revenue from fans. Alternatively, the monetization department can sell T-shirts to increase revenue from fans. In this way, revenue from fans can be increased by selling character merchandise.

[0035] The monetization department can hold events using characters and earn ticket revenue or revenue from the sale of related merchandise. Examples of events include, but are not limited to, live events, fan meetings, exhibitions, etc. For example, the monetization department can hold live events and earn ticket revenue. The monetization department can also hold fan meetings and earn revenue from the sale of related merchandise. The monetization department can also hold exhibitions and earn ticket revenue and revenue from the sale of related merchandise. For example, the monetization department can hold live events and earn ticket revenue. The monetization department can also hold fan meetings and earn revenue from the sale of related merchandise. In this way, by holding events using characters, ticket revenue and revenue from the sale of related merchandise can be earned.

[0036] When generating a character, the generation unit can analyze the user's past preferences or viewing history to generate an optimal character. Examples of the analysis of the user's past preferences include, but are not limited to, viewing history, purchase history, and survey results. For example, the generation unit can analyze the characteristics of characters that the user has viewed with interest in the past and generate a character with similar characteristics. The generation unit can also generate a character based on a specific genre or theme from the user's viewing history. The generation unit can also generate a character that matches the user's preferences based on data of characters that the user has previously rated. For example, the generation unit can analyze the characteristics of characters that the user has viewed with interest in the past and generate a character with similar characteristics. The generation unit can also generate a character based on a specific genre or theme from the user's viewing history. In this way, by analyzing the user's past preferences and viewing history, a character that more closely matches the user's preferences can be generated.

[0037] When generating a character, the generation unit can customize the appearance or personality based on a specific theme or trend. Specific themes include, but are not limited to, fantasy, science fiction, and horror. For example, the generation unit can generate a character that reflects current fashion and makeup trends. The generation unit can also generate a character based on the theme of a popular movie or anime. The generation unit can also generate a character with an appearance or personality that matches a season or event. For example, the generation unit can generate a character that reflects current fashion and makeup trends. The generation unit can also generate a character based on the theme of a popular movie or anime. In this way, by customizing the character based on a specific theme or trend, it is possible to provide a more attractive character.

[0038] When generating a character, the generation unit may adjust the appearance or personality by taking into account the user's geographical or cultural background. Examples of geographical backgrounds include, but are not limited to, a country, region, or city. For example, the generation unit may generate a character that reflects the culture and traditions of the region in which the user lives. The generation unit may also generate a character that incorporates the unique fashion and language of the user's country or region. The generation unit may also generate a character with the scenery or buildings of a specific region as its background, based on the user's geographical background. For example, the generation unit may generate a character that reflects the culture and traditions of the region in which the user lives. The generation unit may also generate a character that incorporates the unique fashion and language of the user's country or region. This makes it possible to provide a more personalized character by taking into account the user's geographical and cultural background.

[0039] When generating a character, the generation unit may analyze the user's social media activity to generate a related character. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the generation unit may analyze the content of accounts the user follows on social media to generate a related character. The generation unit may also generate a character that is likely to be of interest to the user based on the content of the user's social media posts. The generation unit may also generate a related character based on the activity of the user's friends on social media. For example, the generation unit may analyze the content of accounts the user follows on social media to generate a related character. The generation unit may also generate a character that is likely to be of interest to the user based on the content of the user's social media posts. In this way, by analyzing the user's social media activity, it is possible to generate a character that is more suited to the user's interests.

[0040] When generating a character, the generation unit can select the optimal generation method by taking into account the user's device information. Device information includes, but is not limited to, the type of device being used, the OS, and the browser. For example, if the user is using a smartphone, the generation unit can generate a character optimized for the screen size. Furthermore, if the user is using a tablet, the generation unit can generate a character optimized for a larger screen. Furthermore, if the user is using a PC, the generation unit can generate a high-resolution character. For example, if the user is using a smartphone, the generation unit can generate a character optimized for the screen size. Furthermore, if the user is using a tablet, the generation unit can generate a character optimized for a larger screen. This allows for providing a more appropriate character by taking into account the user's device information.

[0041] The generation unit can adjust the generation algorithm by reflecting user feedback when generating a character. User feedback includes, but is not limited to, survey results, reviews, and comments. For example, the generation unit can adjust the character's appearance and personality based on the user's feedback. The generation unit can also improve the generation algorithm by reflecting the user's evaluation and generate a character that better suits the user's preferences. The generation unit can also collect user feedback and periodically update the generation algorithm. For example, the generation unit can adjust the character's appearance and personality based on the user's feedback. The generation unit can also improve the generation algorithm by reflecting the user's evaluation and generate a character that better suits the user's preferences. In this way, by reflecting the user's feedback, it is possible to provide a character that better suits the user's preferences.

[0042] When generating a story, the story generation unit may adjust the level of detail of the story based on the character's background or setting. Examples of the level of detail of the story include, but are not limited to, the depth of description and the number of episodes. For example, if the character is a resident of a fantasy world, the story generation unit may generate a detailed story that matches the worldview of the fantasy world. Furthermore, if the character is a detective in a futuristic city, the story generation unit may generate a detailed cyberpunk-style story. Furthermore, if the character is a modern-day student, the story generation unit may generate a detailed story based on daily life. For example, if the character is a resident of a fantasy world, the story generation unit may generate a detailed story that matches the worldview of the fantasy world. Furthermore, if the character is a detective in a futuristic city, the story generation unit may generate a detailed cyberpunk-style story. In this way, by adjusting the level of detail of the story based on the character's background or setting, it is possible to provide a more compelling story.

[0043] When generating a story, the story generation unit can improve the accuracy of the story by referring to the user's past story viewing history. Examples of story accuracy include, but are not limited to, plot consistency and the rationality of character actions. For example, the story generation unit analyzes the genre of stories the user has previously viewed and generates a story of a similar genre. The story generation unit can also generate a story that reflects the user's preferred story development based on the user's viewing history. The story generation unit can also generate a story that incorporates elements of stories that the user has highly rated. For example, the story generation unit can analyze the genre of stories the user has previously viewed and generate a story of a similar genre. The story generation unit can also generate a story that reflects the user's preferred story development based on the user's viewing history. In this way, by referring to the user's past story viewing history, it is possible to provide a story that better suits the user's preferences.

[0044] The story generation unit may apply different generation algorithms according to a specific genre or theme when generating a story. Examples of genres include, but are not limited to, action, romance, and mystery. For example, in the case of a fantasy genre, the story generation unit may apply a generation algorithm with a magic or adventure theme. In addition, in the case of a cyberpunk genre, the story generation unit may apply a generation algorithm with a futuristic technology or city theme. In addition, in the case of a romance genre, the story generation unit may apply a generation algorithm with a romance or emotional theme. For example, in the case of a fantasy genre, the story generation unit may apply a generation algorithm with a magic or adventure theme. In addition, in the case of a cyberpunk genre, the story generation unit may apply a generation algorithm with a futuristic technology or city theme. In this way, by applying a generation algorithm according to a specific genre or theme, more diverse stories can be provided.

[0045] When generating a story, the story generation unit can adjust the development of the story based on the growth or evolution of the character. Character growth includes, but is not limited to, for example, skill improvement and personality change. The story generation unit, for example, generates a story depicting the process of a character's growth. The story generation unit can also generate a story including a scene in which a character acquires a new ability. The story generation unit can also generate a story depicting the process in which a character overcomes difficulties. For example, the story generation unit can generate a story depicting the process in which a character grows. The story generation unit can also generate a story including a scene in which a character acquires a new ability. In this way, by adjusting the development of the story based on the growth or evolution of the character, it is possible to provide a more appealing story.

[0046] When generating a story, the story generation unit can adjust the use of technical terms in the story according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, if the user is a beginner, the story generation unit can generate a story that is easy to understand by using less technical terms. Furthermore, if the user is an intermediate user, the story generation unit can generate a story that includes an appropriate amount of technical terms. Furthermore, if the user is an advanced user, the story generation unit can generate a detailed story that uses a lot of technical terms. For example, if the user is a beginner, the story generation unit can generate a story that is easy to understand by using less technical terms. Furthermore, if the user is an intermediate user, the story generation unit can generate a story that includes an appropriate amount of technical terms. In this way, by adjusting the use of technical terms according to the user's level of expertise, a story that is easier to understand can be provided.

[0047] The story generation unit can customize the content of the story by reflecting user feedback when generating the story. The story content includes, for example, a plot, character settings, episodes, etc., but is not limited to these examples. The story generation unit can, for example, adjust the development of the story based on feedback provided by the user. The story generation unit can also improve the content of the story by reflecting user ratings. The story generation unit can also collect user feedback and periodically update the story generation algorithm. For example, the story generation unit can adjust the development of the story based on feedback provided by the user. The story generation unit can also improve the content of the story by reflecting user ratings. In this way, by reflecting user feedback, it is possible to provide a story that better suits the user's preferences.

[0048] During promotion, the promotion unit may adjust the level of detail of the promotion based on the popularity of the character or the reaction of fans. Examples of the level of detail of the promotion include, but are not limited to, the depth of information and the level of detail of the explanation. For example, the promotion unit may conduct detailed promotion for a popular character. Furthermore, the promotion unit may increase the frequency of promotion if the fan reaction is good. Furthermore, the promotion unit may conduct concise promotion for a new character. For example, the promotion unit may conduct detailed promotion for a popular character. Furthermore, the promotion unit may increase the frequency of promotion if the fan reaction is good. In this way, by adjusting the level of detail of the promotion based on the popularity of the character and the reaction of fans, it is possible to provide more effective promotion.

[0049] During promotion, the promotion unit can improve the accuracy of the promotion by referring to the user's past promotion viewing history. Examples of promotion accuracy include, but are not limited to, targeting accuracy and message consistency. For example, the promotion unit can analyze the content of promotions the user has viewed in the past and provide promotions containing similar content. Furthermore, the promotion unit can also provide promotions that reflect the user's preferred promotion format based on the user's viewing history. Furthermore, the promotion unit can also provide promotions that incorporate elements of promotions that the user has given high ratings. For example, the promotion unit can analyze the content of promotions the user has viewed in the past and provide promotions containing similar content. Furthermore, the promotion unit can also provide promotions that reflect the user's preferred promotion format based on the user's viewing history. In this way, by referring to the user's past promotion viewing history, it is possible to provide promotions that are more suited to the user's preferences.

[0050] The promotion department may apply different promotional methods depending on a specific event or campaign during promotion. Examples of promotional methods include, but are not limited to, online advertising, offline events, and influencer marketing. For example, in the case of a Christmas campaign, the promotion department may conduct a promotion that reflects the seasonal feel. Furthermore, in the case of a new product launch event, the promotion department may conduct a promotion that emphasizes the features of the product. Furthermore, in the case of a fan meeting, the promotion department may conduct a promotion that emphasizes interaction with fans. For example, in the case of a Christmas campaign, the promotion department may conduct a promotion that reflects the seasonal feel. Furthermore, in the case of a new product launch event, the promotion department may conduct a promotion that emphasizes the features of the product. In this way, by applying promotional methods depending on a specific event or campaign, more effective promotions can be provided.

[0051] During promotion, the promotion unit can adjust the content of the promotion based on the character's growth or evolution. Examples of the content of the promotion include, but are not limited to, messages, visuals, and catchphrases. For example, if a character acquires a new skill, the promotion unit can run a promotion that highlights the skill. Furthermore, if a character wears a new costume, the promotion unit can run a promotion that introduces the costume. Furthermore, if a character plays an important role in the story, the promotion unit can run a promotion that highlights the role. For example, if a character acquires a new skill, the promotion unit can run a promotion that highlights the skill. Furthermore, if a character wears a new costume, the promotion unit can run a promotion that introduces the costume. By adjusting the content of the promotion based on the character's growth or evolution, it is possible to provide a more attractive promotion.

[0052] During promotion, the promotion unit may adjust the use of technical terms in the promotion according to the user's level of expertise. Technical terms include, but are not limited to, technical terms and industry jargon. For example, if the user is a beginner, the promotion unit may use less technical terms to provide an easy-to-understand promotion. Furthermore, if the user is an intermediate user, the promotion unit may use appropriate technical terms to provide a promotion that includes more detailed technical terms. For example, if the user is a beginner, the promotion unit may use less technical terms to provide an easy-to-understand promotion. Furthermore, if the user is an intermediate user, the promotion unit may use appropriate technical terms to provide a promotion that includes appropriate technical terms. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide a promotion that is easier to understand.

[0053] The promotion unit can customize the content of a promotion by reflecting user feedback during the promotion. Examples of the content of the promotion include, but are not limited to, messages, visuals, and catchy slogans. For example, the promotion unit can adjust the content of the promotion based on the feedback provided by the user. The promotion unit can also improve the content of the promotion by reflecting user ratings. The promotion unit can also collect user feedback and periodically update the content of the promotion. For example, the promotion unit can adjust the content of the promotion based on the feedback provided by the user. The promotion unit can also improve the content of the promotion by reflecting user ratings. By reflecting user feedback, it is possible to provide promotions that better suit the user's preferences.

[0054] The monetization unit may adjust the level of detail of monetization based on the popularity of the character or fan response during monetization. Examples of the level of detail of monetization include, but are not limited to, pricing, sales channels, and promotion methods. For example, the monetization unit may propose a detailed monetization method for a popular character. Furthermore, the monetization unit may increase the frequency of monetization if fan response is favorable. Furthermore, the monetization unit may propose a simple monetization method for a new character. For example, the monetization unit may propose a detailed monetization method for a popular character. Furthermore, the monetization unit may increase the frequency of monetization if fan response is favorable. Thus, by adjusting the level of detail of monetization based on the popularity of the character and fan response, more effective monetization can be achieved.

[0055] At the time of monetization, the monetization unit can select the optimal monetization method by analyzing the user's past consumption behavior. Consumption behavior includes, but is not limited to, purchase history, browsing history, survey results, etc. For example, the monetization unit can suggest related new products based on data on goods the user has purchased in the past. The monetization unit can also suggest specific events or campaigns based on the user's consumption behavior. The monetization unit can also suggest related new events based on data on events the user has participated in in the past. For example, the monetization unit can suggest related new products based on data on goods the user has purchased in the past. The monetization unit can also suggest specific events or campaigns based on the user's consumption behavior. In this way, a more effective monetization method can be provided by analyzing the user's past consumption behavior.

[0056] The monetization unit can apply different monetization methods depending on a specific event or campaign during monetization. Examples of monetization methods include, but are not limited to, advertising revenue, merchandise sales, and event revenue. For example, in the case of a Christmas campaign, the monetization unit can propose a monetization method that reflects the seasonal feel. Furthermore, in the case of a new product launch event, the monetization unit can also propose a monetization method that emphasizes the features of the product. Furthermore, in the case of a fan meeting, the monetization unit can also propose a monetization method that emphasizes interaction with fans. For example, in the case of a Christmas campaign, the monetization unit can propose a monetization method that reflects the seasonal feel. Furthermore, in the case of a new product launch event, the monetization unit can also propose a monetization method that emphasizes the features of the product. Thus, by applying a monetization method depending on a specific event or campaign, more effective monetization can be achieved.

[0057] The monetization unit can adjust the monetization content based on the character's growth or evolution during monetization. Examples of monetization content include, but are not limited to, pricing, sales channels, and promotion methods. For example, if a character acquires a new skill, the monetization unit can propose a monetization method that emphasizes the skill. Furthermore, if a character wears a new costume, the monetization unit can also propose a monetization method that showcases the costume. Furthermore, if a character plays an important role in the story, the monetization unit can also propose a monetization method that highlights the role. For example, if a character acquires a new skill, the monetization unit can propose a monetization method that highlights the skill. Furthermore, if a character wears a new costume, the monetization unit can also propose a monetization method that showcases the costume. Thus, by adjusting the monetization content based on the character's growth or evolution, more effective monetization can be achieved.

[0058] During monetization, the monetization unit may adjust the use of technical terminology for monetization according to the user's level of expertise. Technical terminology includes, but is not limited to, technical terms, industry jargon, etc. For example, if the user is a beginner, the monetization unit may propose an easy-to-understand monetization method using less technical terminology. Furthermore, if the user is an intermediate user, the monetization unit may propose a monetization method that includes appropriate technical terminology. Furthermore, if the user is an advanced user, the monetization unit may propose a detailed monetization method that uses a lot of technical terminology. For example, if the user is a beginner, the monetization unit may propose an easy-to-understand monetization method using less technical terminology. Furthermore, if the user is an intermediate user, the monetization unit may propose a monetization method that includes appropriate technical terminology. In this way, by adjusting the use of technical terminology according to the user's level of expertise, a more understandable monetization method can be provided.

[0059] The monetization unit can customize the monetization content by reflecting user feedback during monetization. Examples of monetization content include, but are not limited to, pricing, sales channels, and promotion methods. For example, the monetization unit can adjust the monetization method content based on feedback provided by the user. The monetization unit can also improve the monetization method content by reflecting user ratings. The monetization unit can also collect user feedback and periodically update the monetization method content. For example, the monetization unit can adjust the monetization method content based on feedback provided by the user. The monetization unit can also improve the monetization method content by reflecting user ratings. By reflecting user feedback, it is possible to provide a monetization method that better suits the user's preferences.

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

[0061] The generation unit can acquire real-time behavioral data of the user and dynamically adjust the behavior and facial expressions of the generated character. For example, when the user is playing a game, the character's behavior can be changed according to the user's playing style. Also, when the user is listening to music, the character's movements can be adjusted to match the rhythm of the music. Furthermore, when the user is playing sports, the character's movements can be reflected in real time based on the exercise data. In this way, by reflecting the user's real-time behavioral data, a more interactive and dynamic character can be provided.

[0062] The story generation unit can analyze the user's past story viewing history and customize the development of the story based on the viewing history. For example, it can analyze the genre of stories the user has previously viewed and generate a story of a similar genre. It can also generate a story that reflects the characteristics of characters that the user liked to watch. It can also generate a story that incorporates elements of stories that the user gave a high rating to. In this way, by reflecting the user's past viewing history, it is possible to provide a story that better suits the user's preferences.

[0063] The generation unit can adjust the character's appearance or personality by taking into account the user's geographical or cultural background. For example, a character that reflects the culture and traditions of the region in which the user lives can be generated. A character incorporating the unique fashion and language of the user's country or region can also be generated. Furthermore, a character with a background of scenery or buildings of a specific region can be generated based on the user's geographical background. This makes it possible to provide a more personalized character by taking into account the user's geographical and cultural background.

[0064] The promotion unit can improve the accuracy of promotions by referring to the user's past promotion viewing history. For example, the promotion unit analyzes the content of promotions the user has viewed in the past and provides promotions containing similar content. It can also provide promotions that reflect the user's preferred promotion format based on the user's viewing history. It can also provide promotions that incorporate elements of promotions that the user has given high ratings to. By referring to the user's past promotion viewing history, it is possible to provide promotions that better suit the user's preferences.

[0065] The monetization unit can analyze a user's past consumption behavior and select the optimal monetization method. For example, it can suggest related new products based on data on goods the user has purchased in the past. It can also suggest specific events or campaigns based on the user's consumption behavior. It can also suggest related new events based on data on events the user has participated in in the past. In this way, by analyzing a user's past consumption behavior, it is possible to provide a more effective monetization method.

[0066] The generator can adjust the generation algorithm by reflecting user feedback. For example, the generator can adjust the character's appearance and personality based on the user's feedback. The generator can also improve the generation algorithm by reflecting the user's evaluation, generating a character that better suits the user's preferences. Furthermore, the generator can collect user feedback and periodically update the generation algorithm. In this way, by reflecting the user's feedback, it is possible to provide a character that better suits the user's preferences.

[0067] The story generation unit can adjust the use of technical terms in the story according to the user's level of expertise. For example, if the user is a beginner, it can generate an easy-to-understand story using less technical terms. If the user is an intermediate learner, it can generate a story that includes a moderate amount of technical terms. Furthermore, if the user is an advanced learner, it can generate a detailed story that uses a lot of technical terms. In this way, it is possible to provide a story that is easier to understand by adjusting the use of technical terms according to the user's level of expertise.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The generator uses a generation AI to generate the character's appearance or personality. The generator can generate the character's appearance or personality using, for example, deep learning. Step 2: The story generation unit generates a background story for the character using a generation AI. The story generation unit can generate a background story for the character using, for example, natural language processing. Step 3: The Promotion Department promotes the characters created by the Creation Department and Story Creation Department. For example, the Promotion Department manages social media accounts for the characters to interact with fans. They can also plan advertisements and events using the characters to generate revenue. Step 4: The monetization department monetizes the characters promoted by the promotion department. For example, the monetization department may run advertising campaigns using the characters to earn advertising revenue from companies. They may also sell character merchandise to generate revenue from fans.

[0070] (Example 2) An entertainment system according to an embodiment of the present invention uses a generative AI to generate character appearances, personalities, and background stories, and promotes and monetizes them. The entertainment system provides new value to the entertainment industry by using a generative AI to generate character appearances, personalities, and background stories, and by promoting and monetizing them. For example, the entertainment system uses a generative AI to create unique and diverse characters. The generative AI generates character appearances, personalities, and background stories. For example, the generative AI generates characters such as a "hero in a fantasy world" or a "detective in a futuristic city." The entertainment system then promotes the generated characters. For example, the entertainment system manages social media accounts for the characters to interact with fans. The entertainment system also generates revenue by planning advertising and events featuring the characters. Furthermore, the entertainment system includes monetization methods such as advertising, events, and merchandise sales. For example, the entertainment system develops advertising campaigns using the characters to earn advertising revenue from companies. The entertainment system also sells merchandise featuring the characters to generate revenue from fans. This allows the entertainment system to utilize a generative AI to create unique VTuber characters, providing a next-generation entertainment hub that connects talented creators with viewers. This allows entertainment systems to use generative AI to create new characters and content that could not be achieved using conventional methods. For example, generative AI can automatically generate character stories and provide them to viewers, allowing them to enjoy constantly new content. This will provide viewers with new entertainment experiences and evolve the entire industry.

[0071] An entertainment system according to an embodiment includes a generation unit, a story generation unit, a promotion unit, and a monetization unit. The generation unit generates a character's appearance or personality using a generation AI. The generation unit generates a character's appearance or personality using, for example, deep learning. The generation unit can also generate a character's appearance or personality using the generation AI. For example, the generation unit can generate a character's appearance using deep learning. The generation unit can also generate a character's personality using deep learning. The story generation unit generates a character's background story using the generation AI. The story generation unit generates a character's background story using, for example, natural language processing. The story generation unit can also generate a character's background story using the generation AI. For example, the story generation unit can generate a character's background story using natural language processing. The story generation unit can also generate a character's background story using natural language processing. The promotion unit promotes the character generated by the generation unit and the story generation unit. The promotion unit, for example, manages the character's social media accounts to interact with fans. The promotion unit can also plan advertisements and events using the character to generate revenue. For example, the promotion department can manage social media accounts for characters and interact with fans. The promotion department can also plan advertisements and events using the characters to generate revenue. The monetization department monetizes the characters promoted by the promotion department. For example, the monetization department can develop advertising campaigns using the characters to receive advertising revenue from companies. The monetization department can also sell merchandise for the characters to generate revenue from fans. For example, the monetization department can develop advertising campaigns using the characters to receive advertising revenue from companies. The monetization department can also sell merchandise for the characters to generate revenue from fans.As a result, the entertainment system according to the embodiment can provide new value to the entertainment industry by using generative AI to generate character appearances, personalities, and background stories, and then promoting and monetizing them.

[0072] The generation unit may generate the character's appearance or personality using deep learning. Deep learning includes, but is not limited to, a convolutional neural network (CNN) or a recurrent neural network (RNN). The generation unit may generate the character's appearance using, for example, a convolutional neural network (CNN). The generation unit may also generate the character's personality using a recurrent neural network (RNN). The generation unit may also generate the character's appearance or personality using a generation AI. For example, the generation unit may generate the character's appearance using deep learning. The generation unit may also generate the character's personality using deep learning. This allows for the generation of more realistic and unique characters by using deep learning.

[0073] The story generation unit can generate a character's background story using natural language processing. Natural language processing includes, but is not limited to, for example, morphological analysis, grammatical analysis, and semantic analysis. For example, the story generation unit can generate a character's background story using morphological analysis. The story generation unit can also generate a character's background story using grammatical analysis. The story generation unit can also generate a character's background story using semantic analysis. For example, the story generation unit can generate a character's background story using morphological analysis. The story generation unit can also generate a character's background story using grammatical analysis. In this way, by using natural language processing, it is possible to generate a character's background story in a more natural and appealing way.

[0074] The promotion department may operate a social media account for the character to interact with fans. Examples of social media accounts include, but are not limited to, Twitter, Instagram, and Facebook. For example, the promotion department may operate a Twitter account to interact with fans. The promotion department may also operate an Instagram account to interact with fans. The promotion department may also operate a Facebook account to interact with fans. For example, the promotion department may operate a Twitter account to interact with fans. The promotion department may also operate an Instagram account to interact with fans. In this way, by interacting with fans through the social media accounts, it is possible to increase the character's popularity and fan engagement.

[0075] The monetization unit can develop advertising campaigns using the characters and earn advertising revenue from companies. Examples of advertising campaigns include, but are not limited to, online advertising, television commercials, and event advertising. For example, the monetization unit can develop online advertising and earn advertising revenue from companies. The monetization unit can also develop television commercials and earn advertising revenue from companies. The monetization unit can also develop event advertising and earn advertising revenue from companies. For example, the monetization unit can develop online advertising and earn advertising revenue from companies. The monetization unit can also develop television commercials and earn advertising revenue from companies. In this way, advertising campaigns using the characters can earn advertising revenue from companies.

[0076] The monetization department can sell character merchandise to increase revenue from fans. Examples of merchandise include, but are not limited to, figurines, T-shirts, and posters. For example, the monetization department can sell figurines to increase revenue from fans. Alternatively, the monetization department can sell T-shirts to increase revenue from fans. Alternatively, the monetization department can sell posters to increase revenue from fans. For example, the monetization department can sell figurines to increase revenue from fans. Alternatively, the monetization department can sell T-shirts to increase revenue from fans. In this way, revenue from fans can be increased by selling character merchandise.

[0077] The monetization department can hold events using characters and earn ticket revenue or revenue from the sale of related merchandise. Examples of events include, but are not limited to, live events, fan meetings, exhibitions, etc. For example, the monetization department can hold live events and earn ticket revenue. The monetization department can also hold fan meetings and earn revenue from the sale of related merchandise. The monetization department can also hold exhibitions and earn ticket revenue and revenue from the sale of related merchandise. For example, the monetization department can hold live events and earn ticket revenue. The monetization department can also hold fan meetings and earn revenue from the sale of related merchandise. In this way, by holding events using characters, ticket revenue and revenue from the sale of related merchandise can be earned.

[0078] The generation unit can estimate the user's emotions and adjust the character's appearance or personality based on the estimated user's emotions. Techniques such as, but not limited to, facial expression recognition, voice analysis, and text analysis can be used to estimate the user's emotions. For example, if the user is relaxed, the generation unit can generate a character with a calm expression and soft colors. Furthermore, if the user is excited, the generation unit can generate a character with a lively and energetic appearance and personality. Furthermore, if the user is sad, the generation unit can generate a character with a comforting, gentle expression and warm colors. For example, if the user is relaxed, the generation unit can generate a character with a calm expression and soft colors. Furthermore, if the user is excited, the generation unit can generate a character with a lively and energetic appearance and personality. This allows for a more personalized character to be provided by adjusting the character's appearance and personality based on the user's emotions.

[0079] When generating a character, the generation unit can analyze the user's past preferences or viewing history to generate an optimal character. Examples of the analysis of the user's past preferences include, but are not limited to, viewing history, purchase history, and survey results. For example, the generation unit can analyze the characteristics of characters that the user has viewed with interest in the past and generate a character with similar characteristics. The generation unit can also generate a character based on a specific genre or theme from the user's viewing history. The generation unit can also generate a character that matches the user's preferences based on data of characters that the user has previously rated. For example, the generation unit can analyze the characteristics of characters that the user has viewed with interest in the past and generate a character with similar characteristics. The generation unit can also generate a character based on a specific genre or theme from the user's viewing history. In this way, by analyzing the user's past preferences and viewing history, a character that more closely matches the user's preferences can be generated.

[0080] When generating a character, the generation unit can customize the appearance or personality based on a specific theme or trend. Specific themes include, but are not limited to, fantasy, science fiction, and horror. For example, the generation unit can generate a character that reflects current fashion and makeup trends. The generation unit can also generate a character based on the theme of a popular movie or anime. The generation unit can also generate a character with an appearance or personality that matches a season or event. For example, the generation unit can generate a character that reflects current fashion and makeup trends. The generation unit can also generate a character based on the theme of a popular movie or anime. In this way, by customizing the character based on a specific theme or trend, it is possible to provide a more attractive character.

[0081] When generating a character, the generation unit may adjust the appearance or personality by taking into account the user's geographical or cultural background. Examples of geographical backgrounds include, but are not limited to, a country, region, or city. For example, the generation unit may generate a character that reflects the culture and traditions of the region in which the user lives. The generation unit may also generate a character that incorporates the unique fashion and language of the user's country or region. The generation unit may also generate a character with the scenery or buildings of a specific region as its background, based on the user's geographical background. For example, the generation unit may generate a character that reflects the culture and traditions of the region in which the user lives. The generation unit may also generate a character that incorporates the unique fashion and language of the user's country or region. This makes it possible to provide a more personalized character by taking into account the user's geographical and cultural background.

[0082] The generation unit can estimate the user's emotions and determine the priority of characters to be generated based on the estimated user's emotions. The user's emotion score, popularity, etc., can be used to determine the priority of characters, but are not limited to these examples. For example, if the user is feeling stressed, the generation unit can preferentially generate a character with a relaxing effect. Furthermore, if the user is excited, the generation unit can preferentially generate an energetic character. Furthermore, if the user is sad, the generation unit can preferentially generate a comforting character. For example, if the user is feeling stressed, the generation unit can preferentially generate a character with a relaxing effect. Furthermore, if the user is excited, the generation unit can preferentially generate an energetic character. Thus, by determining the priority of characters based on the user's emotions, more appropriate characters can be provided.

[0083] When generating a character, the generation unit may analyze the user's social media activity to generate a related character. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the generation unit may analyze the content of accounts the user follows on social media to generate a related character. The generation unit may also generate a character that is likely to be of interest to the user based on the content of the user's social media posts. The generation unit may also generate a related character based on the activity of the user's friends on social media. For example, the generation unit may analyze the content of accounts the user follows on social media to generate a related character. The generation unit may also generate a character that is likely to be of interest to the user based on the content of the user's social media posts. In this way, by analyzing the user's social media activity, it is possible to generate a character that is more suited to the user's interests.

[0084] When generating a character, the generation unit can select the optimal generation method by taking into account the user's device information. Device information includes, but is not limited to, the type of device being used, the OS, and the browser. For example, if the user is using a smartphone, the generation unit can generate a character optimized for the screen size. Furthermore, if the user is using a tablet, the generation unit can generate a character optimized for a larger screen. Furthermore, if the user is using a PC, the generation unit can generate a high-resolution character. For example, if the user is using a smartphone, the generation unit can generate a character optimized for the screen size. Furthermore, if the user is using a tablet, the generation unit can generate a character optimized for a larger screen. This allows for providing a more appropriate character by taking into account the user's device information.

[0085] The generation unit can adjust the generation algorithm by reflecting user feedback when generating a character. User feedback includes, but is not limited to, survey results, reviews, and comments. For example, the generation unit can adjust the character's appearance and personality based on the user's feedback. The generation unit can also improve the generation algorithm by reflecting the user's evaluation and generate a character that better suits the user's preferences. The generation unit can also collect user feedback and periodically update the generation algorithm. For example, the generation unit can adjust the character's appearance and personality based on the user's feedback. The generation unit can also improve the generation algorithm by reflecting the user's evaluation and generate a character that better suits the user's preferences. In this way, by reflecting the user's feedback, it is possible to provide a character that better suits the user's preferences.

[0086] The story generation unit can estimate the user's emotions and adjust the way the story is expressed based on the estimated user's emotions. Examples of the way the story is expressed include, but are not limited to, the tone of the writing, the point of view, and the level of detail of the description. For example, if the user is relaxed, the story generation unit can generate a story that progresses in a calm tone. Also, if the user is excited, the story generation unit can generate a story that includes many action scenes. Also, if the user is sad, the story generation unit can generate a story that progresses in a calm tone. Also, if the user is excited, the story generation unit can generate a story that includes many action scenes. In this way, by adjusting the way the story is expressed based on the user's emotions, it is possible to provide a more personalized story.

[0087] When generating a story, the story generation unit may adjust the level of detail of the story based on the character's background or setting. Examples of the level of detail of the story include, but are not limited to, the depth of description and the number of episodes. For example, if the character is a resident of a fantasy world, the story generation unit may generate a detailed story that matches the worldview of the fantasy world. Furthermore, if the character is a detective in a futuristic city, the story generation unit may generate a detailed cyberpunk-style story. Furthermore, if the character is a modern-day student, the story generation unit may generate a detailed story based on daily life. For example, if the character is a resident of a fantasy world, the story generation unit may generate a detailed story that matches the worldview of the fantasy world. Furthermore, if the character is a detective in a futuristic city, the story generation unit may generate a detailed cyberpunk-style story. In this way, by adjusting the level of detail of the story based on the character's background or setting, it is possible to provide a more compelling story.

[0088] When generating a story, the story generation unit can improve the accuracy of the story by referring to the user's past story viewing history. Examples of story accuracy include, but are not limited to, plot consistency and the rationality of character actions. For example, the story generation unit analyzes the genre of stories the user has previously viewed and generates a story of a similar genre. The story generation unit can also generate a story that reflects the user's preferred story development based on the user's viewing history. The story generation unit can also generate a story that incorporates elements of stories that the user has highly rated. For example, the story generation unit can analyze the genre of stories the user has previously viewed and generate a story of a similar genre. The story generation unit can also generate a story that reflects the user's preferred story development based on the user's viewing history. In this way, by referring to the user's past story viewing history, it is possible to provide a story that better suits the user's preferences.

[0089] The story generation unit may apply different generation algorithms according to a specific genre or theme when generating a story. Examples of genres include, but are not limited to, action, romance, and mystery. For example, in the case of a fantasy genre, the story generation unit may apply a generation algorithm with a magic or adventure theme. In addition, in the case of a cyberpunk genre, the story generation unit may apply a generation algorithm with a futuristic technology or city theme. In addition, in the case of a romance genre, the story generation unit may apply a generation algorithm with a romance or emotional theme. For example, in the case of a fantasy genre, the story generation unit may apply a generation algorithm with a magic or adventure theme. In addition, in the case of a cyberpunk genre, the story generation unit may apply a generation algorithm with a futuristic technology or city theme. In this way, by applying a generation algorithm according to a specific genre or theme, more diverse stories can be provided.

[0090] The story generation unit can estimate the user's emotions and adjust the length of the story based on the estimated user's emotions. Examples of the story length include, but are not limited to, the number of chapters, the number of pages, and the length of episodes. For example, the story generation unit can generate a short, to-the-point story when the user is in a hurry. Furthermore, the story generation unit can generate a longer story with detailed explanations when the user is relaxed. Furthermore, the story generation unit can generate a fast-paced story when the user is excited. For example, the story generation unit can generate a short, to-the-point story when the user is in a hurry. Furthermore, the story generation unit can generate a longer story with detailed explanations when the user is relaxed. In this way, by adjusting the length of the story based on the user's emotions, it is possible to provide a more appropriate story.

[0091] When generating a story, the story generation unit can adjust the development of the story based on the growth or evolution of the character. Character growth includes, but is not limited to, for example, skill improvement and personality change. The story generation unit, for example, generates a story depicting the process of a character's growth. The story generation unit can also generate a story including a scene in which a character acquires a new ability. The story generation unit can also generate a story depicting the process in which a character overcomes difficulties. For example, the story generation unit can generate a story depicting the process in which a character grows. The story generation unit can also generate a story including a scene in which a character acquires a new ability. In this way, by adjusting the development of the story based on the growth or evolution of the character, it is possible to provide a more appealing story.

[0092] When generating a story, the story generation unit can adjust the use of technical terms in the story according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, if the user is a beginner, the story generation unit can generate a story that is easy to understand by using less technical terms. Furthermore, if the user is an intermediate user, the story generation unit can generate a story that includes an appropriate amount of technical terms. Furthermore, if the user is an advanced user, the story generation unit can generate a detailed story that uses a lot of technical terms. For example, if the user is a beginner, the story generation unit can generate a story that is easy to understand by using less technical terms. Furthermore, if the user is an intermediate user, the story generation unit can generate a story that includes an appropriate amount of technical terms. In this way, by adjusting the use of technical terms according to the user's level of expertise, a story that is easier to understand can be provided.

[0093] The story generation unit can customize the content of the story by reflecting user feedback when generating the story. The story content includes, for example, a plot, character settings, episodes, etc., but is not limited to these examples. The story generation unit can, for example, adjust the development of the story based on feedback provided by the user. The story generation unit can also improve the content of the story by reflecting user ratings. The story generation unit can also collect user feedback and periodically update the story generation algorithm. For example, the story generation unit can adjust the development of the story based on feedback provided by the user. The story generation unit can also improve the content of the story by reflecting user ratings. In this way, by reflecting user feedback, it is possible to provide a story that better suits the user's preferences.

[0094] The promotion unit can estimate the user's emotions and adjust the promotion presentation method based on the estimated user emotions. Examples of the promotion presentation method include, but are not limited to, the tone of the advertisement, visual design, and message. For example, if the user is relaxed, the promotion unit can perform the promotion in a calm tone. Also, if the user is excited, the promotion unit can perform the promotion in an energetic tone. Also, if the user is sad, the promotion unit can perform the promotion in a comforting tone. For example, if the user is relaxed, the promotion unit can perform the promotion in a calm tone. Also, if the user is excited, the promotion unit can perform the promotion in an energetic tone. In this way, by adjusting the promotion presentation method based on the user's emotions, it is possible to provide more effective promotions.

[0095] During promotion, the promotion unit may adjust the level of detail of the promotion based on the popularity of the character or the reaction of fans. Examples of the level of detail of the promotion include, but are not limited to, the depth of information and the level of detail of the explanation. For example, the promotion unit may conduct detailed promotion for a popular character. Furthermore, the promotion unit may increase the frequency of promotion if the fan reaction is good. Furthermore, the promotion unit may conduct concise promotion for a new character. For example, the promotion unit may conduct detailed promotion for a popular character. Furthermore, the promotion unit may increase the frequency of promotion if the fan reaction is good. In this way, by adjusting the level of detail of the promotion based on the popularity of the character and the reaction of fans, it is possible to provide more effective promotion.

[0096] During promotion, the promotion unit can improve the accuracy of the promotion by referring to the user's past promotion viewing history. Examples of promotion accuracy include, but are not limited to, targeting accuracy and message consistency. For example, the promotion unit can analyze the content of promotions the user has viewed in the past and provide promotions containing similar content. Furthermore, the promotion unit can also provide promotions that reflect the user's preferred promotion format based on the user's viewing history. Furthermore, the promotion unit can also provide promotions that incorporate elements of promotions that the user has given high ratings. For example, the promotion unit can analyze the content of promotions the user has viewed in the past and provide promotions containing similar content. Furthermore, the promotion unit can also provide promotions that reflect the user's preferred promotion format based on the user's viewing history. In this way, by referring to the user's past promotion viewing history, it is possible to provide promotions that are more suited to the user's preferences.

[0097] The promotion department may apply different promotional methods depending on a specific event or campaign during promotion. Examples of promotional methods include, but are not limited to, online advertising, offline events, and influencer marketing. For example, in the case of a Christmas campaign, the promotion department may conduct a promotion that reflects the seasonal feel. Furthermore, in the case of a new product launch event, the promotion department may conduct a promotion that emphasizes the features of the product. Furthermore, in the case of a fan meeting, the promotion department may conduct a promotion that emphasizes interaction with fans. For example, in the case of a Christmas campaign, the promotion department may conduct a promotion that reflects the seasonal feel. Furthermore, in the case of a new product launch event, the promotion department may conduct a promotion that emphasizes the features of the product. In this way, by applying promotional methods depending on a specific event or campaign, more effective promotions can be provided.

[0098] The promotion unit can estimate the user's emotions and adjust the length of the promotion based on the estimated user emotions. Examples of the length of the promotion include, but are not limited to, the playback time of an advertisement and the duration of a campaign. For example, the promotion unit can perform a short and to-the-point promotion when the user is in a hurry. Alternatively, the promotion unit can perform a longer promotion with detailed explanations when the user is relaxed. Alternatively, the promotion unit can perform a fast-paced promotion when the user is excited. For example, the promotion unit can perform a short and to-the-point promotion when the user is in a hurry. Alternatively, the promotion unit can perform a longer promotion with detailed explanations when the user is relaxed. In this way, by adjusting the length of the promotion based on the user's emotions, it is possible to provide more appropriate promotions.

[0099] During promotion, the promotion unit can adjust the content of the promotion based on the character's growth or evolution. Examples of the content of the promotion include, but are not limited to, messages, visuals, and catchphrases. For example, if a character acquires a new skill, the promotion unit can run a promotion that highlights the skill. Furthermore, if a character wears a new costume, the promotion unit can run a promotion that introduces the costume. Furthermore, if a character plays an important role in the story, the promotion unit can run a promotion that highlights the role. For example, if a character acquires a new skill, the promotion unit can run a promotion that highlights the skill. Furthermore, if a character wears a new costume, the promotion unit can run a promotion that introduces the costume. By adjusting the content of the promotion based on the character's growth or evolution, it is possible to provide a more attractive promotion.

[0100] During promotion, the promotion unit may adjust the use of technical terms in the promotion according to the user's level of expertise. Technical terms include, but are not limited to, technical terms and industry jargon. For example, if the user is a beginner, the promotion unit may use less technical terms to provide an easy-to-understand promotion. Furthermore, if the user is an intermediate user, the promotion unit may use appropriate technical terms to provide a promotion that includes more detailed technical terms. For example, if the user is a beginner, the promotion unit may use less technical terms to provide an easy-to-understand promotion. Furthermore, if the user is an intermediate user, the promotion unit may use appropriate technical terms to provide a promotion that includes appropriate technical terms. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide a promotion that is easier to understand.

[0101] The promotion unit can customize the content of a promotion by reflecting user feedback during the promotion. Examples of the content of the promotion include, but are not limited to, messages, visuals, and catchy slogans. For example, the promotion unit can adjust the content of the promotion based on the feedback provided by the user. The promotion unit can also improve the content of the promotion by reflecting user ratings. The promotion unit can also collect user feedback and periodically update the content of the promotion. For example, the promotion unit can adjust the content of the promotion based on the feedback provided by the user. The promotion unit can also improve the content of the promotion by reflecting user ratings. By reflecting user feedback, it is possible to provide promotions that better suit the user's preferences.

[0102] The monetization unit can estimate a user's emotions and adjust a monetization method based on the estimated user's emotions. Examples of monetization methods include, but are not limited to, advertising revenue, merchandise sales, and event revenue. For example, if a user is relaxed, the monetization unit can suggest a monetization method that uses a calm tone. Furthermore, if a user is excited, the monetization unit can suggest a monetization method that uses an energetic tone. Furthermore, if a user is sad, the monetization unit can suggest a monetization method that uses a comforting tone. For example, if a user is relaxed, the monetization unit can suggest a monetization method that uses a calm tone. Furthermore, if a user is excited, the monetization unit can suggest a monetization method that uses an energetic tone. Thus, by adjusting the monetization method based on the user's emotions, more effective monetization can be achieved.

[0103] The monetization unit may adjust the level of detail of monetization based on the popularity of the character or fan response during monetization. Examples of the level of detail of monetization include, but are not limited to, pricing, sales channels, and promotion methods. For example, the monetization unit may propose a detailed monetization method for a popular character. Furthermore, the monetization unit may increase the frequency of monetization if fan response is favorable. Furthermore, the monetization unit may propose a simple monetization method for a new character. For example, the monetization unit may propose a detailed monetization method for a popular character. Furthermore, the monetization unit may increase the frequency of monetization if fan response is favorable. Thus, by adjusting the level of detail of monetization based on the popularity of the character and fan response, more effective monetization can be achieved.

[0104] At the time of monetization, the monetization unit can select the optimal monetization method by analyzing the user's past consumption behavior. Consumption behavior includes, but is not limited to, purchase history, browsing history, survey results, etc. For example, the monetization unit can suggest related new products based on data on goods the user has purchased in the past. The monetization unit can also suggest specific events or campaigns based on the user's consumption behavior. The monetization unit can also suggest related new events based on data on events the user has participated in in the past. For example, the monetization unit can suggest related new products based on data on goods the user has purchased in the past. The monetization unit can also suggest specific events or campaigns based on the user's consumption behavior. In this way, a more effective monetization method can be provided by analyzing the user's past consumption behavior.

[0105] The monetization unit can apply different monetization methods depending on a specific event or campaign during monetization. Examples of monetization methods include, but are not limited to, advertising revenue, merchandise sales, and event revenue. For example, in the case of a Christmas campaign, the monetization unit can propose a monetization method that reflects the seasonal feel. Furthermore, in the case of a new product launch event, the monetization unit can also propose a monetization method that emphasizes the features of the product. Furthermore, in the case of a fan meeting, the monetization unit can also propose a monetization method that emphasizes interaction with fans. For example, in the case of a Christmas campaign, the monetization unit can propose a monetization method that reflects the seasonal feel. Furthermore, in the case of a new product launch event, the monetization unit can also propose a monetization method that emphasizes the features of the product. Thus, by applying a monetization method depending on a specific event or campaign, more effective monetization can be achieved.

[0106] The monetization unit can estimate the user's emotions and determine monetization priorities based on the estimated user's emotions. Monetization priorities include, but are not limited to, revenue prospects and the user's level of interest. For example, if the user is relaxed, the monetization unit can prioritize a monetization technique that proceeds in a calm tone. Furthermore, if the user is excited, the monetization unit can prioritize a monetization technique that proceeds in an energetic tone. Furthermore, if the user is sad, the monetization unit can prioritize a monetization technique that proceeds in a comforting tone. For example, if the user is relaxed, the monetization unit can prioritize a monetization technique that proceeds in a calm tone. Furthermore, if the user is excited, the monetization unit can prioritize a monetization technique that proceeds in an energetic tone. Thus, by determining monetization priorities based on the user's emotions, more effective monetization can be achieved.

[0107] The monetization unit can adjust the monetization content based on the character's growth or evolution during monetization. Examples of monetization content include, but are not limited to, pricing, sales channels, and promotion methods. For example, if a character acquires a new skill, the monetization unit can propose a monetization method that emphasizes the skill. Furthermore, if a character wears a new costume, the monetization unit can also propose a monetization method that showcases the costume. Furthermore, if a character plays an important role in the story, the monetization unit can also propose a monetization method that highlights the role. For example, if a character acquires a new skill, the monetization unit can propose a monetization method that highlights the skill. Furthermore, if a character wears a new costume, the monetization unit can also propose a monetization method that showcases the costume. Thus, by adjusting the monetization content based on the character's growth or evolution, more effective monetization can be achieved.

[0108] During monetization, the monetization unit may adjust the use of technical terminology for monetization according to the user's level of expertise. Technical terminology includes, but is not limited to, technical terms, industry jargon, etc. For example, if the user is a beginner, the monetization unit may propose an easy-to-understand monetization method using less technical terminology. Furthermore, if the user is an intermediate user, the monetization unit may propose a monetization method that includes appropriate technical terminology. Furthermore, if the user is an advanced user, the monetization unit may propose a detailed monetization method that uses a lot of technical terminology. For example, if the user is a beginner, the monetization unit may propose an easy-to-understand monetization method using less technical terminology. Furthermore, if the user is an intermediate user, the monetization unit may propose a monetization method that includes appropriate technical terminology. In this way, by adjusting the use of technical terminology according to the user's level of expertise, a more understandable monetization method can be provided.

[0109] The monetization unit can customize the monetization content by reflecting user feedback during monetization. Examples of monetization content include, but are not limited to, pricing, sales channels, and promotion methods. For example, the monetization unit can adjust the monetization method content based on feedback provided by the user. The monetization unit can also improve the monetization method content by reflecting user ratings. The monetization unit can also collect user feedback and periodically update the monetization method content. For example, the monetization unit can adjust the monetization method content based on feedback provided by the user. The monetization unit can also improve the monetization method content by reflecting user ratings. By reflecting user feedback, it is possible to provide a monetization method that better suits the user's preferences. === Hard Collateral 1-1 === Each of the multiple elements, including the generation unit, story generation unit, promotion unit, and monetization unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the generation unit can generate a character's appearance and personality using the processor 46 of the smart device 14. The story generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. For example, the story generation unit can generate a character's background story using the processor 28 of the data processing device 12. The promotion unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the promotion unit can operate a social media account for the character using the processor 46 of the smart device 14 to facilitate interaction with fans. The monetization unit is realized, for example, by the specific processing unit 290 of the data processing device 12. For example, the monetization unit can use the processor 28 of the data processing device 12 to develop an advertising campaign using the character and earn advertising revenue from businesses. === Hard Collateral 1-2 === Each of the multiple elements, including the generation unit, story generation unit, promotion unit, and monetization unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the generation unit can generate the character's appearance and personality using the processor 46 of the smart glasses 214. The story generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. For example, the story generation unit can generate the character's background story using the processor 28 of the data processing device 12. The promotion unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the promotion unit can operate the character's social media account using the processor 46 of the smart glasses 214 to facilitate interaction with fans. The monetization unit is realized, for example, by the specific processing unit 290 of the data processing device 12. For example, the monetization unit can use the processor 28 of the data processing device 12 to develop an advertising campaign using the character and earn advertising revenue from businesses. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned generation unit, story generation unit, promotion unit, and monetization unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the generation unit can generate the character's appearance and personality using the processor 46 of the headset type terminal 314. The story generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. For example, the story generation unit can generate the character's background story using the processor 28 of the data processing device 12. The promotion unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the promotion unit can operate the character's social media account using the processor 46 of the headset type terminal 314 to facilitate interaction with fans. The monetization unit is realized, for example, by the specific processing unit 290 of the data processing device 12. For example, the monetization unit can use the processor 28 of the data processing device 12 to develop an advertising campaign using the character and earn advertising revenue from businesses. === Hard Collateral 1-4 === Each of the multiple elements including the generation unit, story generation unit, promotion unit, and monetization unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the generation unit can generate the character's appearance and personality using the processor 46 of the robot 414. The story generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. For example, the story generation unit can generate the character's background story using the processor 28 of the data processing device 12. The promotion unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the promotion unit can operate the character's social media account using the processor 46 of the robot 414 to facilitate interaction with fans. The monetization unit is realized, for example, by the specific processing unit 290 of the data processing device 12. For example, the monetization unit can use the processor 28 of the data processing device 12 to develop an advertising campaign using the character and earn advertising revenue from businesses.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The generation unit can acquire real-time behavioral data of the user and dynamically adjust the behavior and facial expressions of the generated character. For example, when the user is playing a game, the character's behavior can be changed according to the user's playing style. Also, when the user is listening to music, the character's movements can be adjusted to match the rhythm of the music. Furthermore, when the user is playing sports, the character's movements can be reflected in real time based on the exercise data. In this way, by reflecting the user's real-time behavioral data, a more interactive and dynamic character can be provided.

[0112] The story generation unit can analyze the user's past story viewing history and customize the development of the story based on the viewing history. For example, it can analyze the genre of stories the user has previously viewed and generate a story of a similar genre. It can also generate a story that reflects the characteristics of characters that the user liked to watch. It can also generate a story that incorporates elements of stories that the user gave a high rating to. In this way, by reflecting the user's past viewing history, it is possible to provide a story that better suits the user's preferences.

[0113] The promotion unit can estimate the user's emotions and adjust the timing of promotions based on the estimated user emotions. For example, if the user is relaxed, the promotion can be performed in a calm tone. If the user is excited, the promotion can be performed in an energetic tone. Furthermore, if the user is sad, the promotion can be performed in a comforting tone. In this way, by adjusting the timing of promotions based on the user's emotions, more effective promotions can be provided.

[0114] The monetization unit can estimate the user's emotions and adjust the monetization method based on the estimated user's emotions. For example, if the user is relaxed, the monetization unit can suggest a monetization method that proceeds in a calm tone. If the user is excited, the monetization unit can suggest a monetization method that proceeds in an energetic tone. Furthermore, if the user is sad, the monetization unit can suggest a monetization method that proceeds in a comforting tone. In this way, by adjusting the monetization method based on the user's emotions, more effective monetization can be achieved.

[0115] The generation unit can adjust the character's appearance or personality by taking into account the user's geographical or cultural background. For example, a character that reflects the culture and traditions of the region in which the user lives can be generated. A character incorporating the unique fashion and language of the user's country or region can also be generated. Furthermore, a character with a background of scenery or buildings of a specific region can be generated based on the user's geographical background. This makes it possible to provide a more personalized character by taking into account the user's geographical and cultural background.

[0116] The story generation unit can estimate the user's emotions and adjust the way the story is presented based on the estimated user's emotions. For example, if the user is relaxed, a story that progresses in a calm tone can be generated. If the user is excited, a story that includes many action scenes can be generated. Furthermore, if the user is sad, a story that includes many moving scenes can be generated. In this way, by adjusting the way the story is presented based on the user's emotions, a more personalized story can be provided.

[0117] The promotion unit can improve the accuracy of promotions by referring to the user's past promotion viewing history. For example, the promotion unit analyzes the content of promotions the user has viewed in the past and provides promotions containing similar content. It can also provide promotions that reflect the user's preferred promotion format based on the user's viewing history. It can also provide promotions that incorporate elements of promotions that the user has given high ratings to. By referring to the user's past promotion viewing history, it is possible to provide promotions that better suit the user's preferences.

[0118] The monetization unit can analyze a user's past consumption behavior and select the optimal monetization method. For example, it can suggest related new products based on data on goods the user has purchased in the past. It can also suggest specific events or campaigns based on the user's consumption behavior. It can also suggest related new events based on data on events the user has participated in in the past. In this way, by analyzing a user's past consumption behavior, it is possible to provide a more effective monetization method.

[0119] The generator can adjust the generation algorithm by reflecting user feedback. For example, the generator can adjust the character's appearance and personality based on the user's feedback. The generator can also improve the generation algorithm by reflecting the user's evaluation, generating a character that better suits the user's preferences. Furthermore, the generator can collect user feedback and periodically update the generation algorithm. In this way, by reflecting the user's feedback, it is possible to provide a character that better suits the user's preferences.

[0120] The story generation unit can adjust the use of technical terms in the story according to the user's level of expertise. For example, if the user is a beginner, it can generate an easy-to-understand story using less technical terms. If the user is an intermediate learner, it can generate a story that includes a moderate amount of technical terms. Furthermore, if the user is an advanced learner, it can generate a detailed story that uses a lot of technical terms. In this way, it is possible to provide a story that is easier to understand by adjusting the use of technical terms according to the user's level of expertise.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The generator uses a generation AI to generate the character's appearance or personality. The generator can generate the character's appearance or personality using, for example, deep learning. Step 2: The story generation unit generates a background story for the character using a generation AI. The story generation unit can generate a background story for the character using, for example, natural language processing. Step 3: The Promotion Department promotes the characters created by the Creation Department and Story Creation Department. For example, the Promotion Department manages social media accounts for the characters to interact with fans. They can also plan advertisements and events using the characters to generate revenue. Step 4: The monetization department monetizes the characters promoted by the promotion department. For example, the monetization department may run advertising campaigns using the characters to earn advertising revenue from companies. They may also sell character merchandise to generate revenue from fans.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0146] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0167] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0168] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0170] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0179] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0180] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0184] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0185] 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.

[0186] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0187] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0188] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0189] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0192] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A generation unit that generates the appearance or personality of a character using a generation AI; a story generation unit that generates a background story of a character using a generation AI; a promotion unit that promotes the characters generated by the generation unit and the story generation unit; a monetization unit that monetizes the characters promoted by the promotion unit. A system characterized by:

2. The generation unit Generate a character's appearance or personality using deep learning 2. The system of claim 1.

3. The story generation unit Generate character background stories using natural language processing 2. The system of claim 1.

4. The promotion department Manage the character's social media account and interact with fans 2. The system of claim 1.

5. The monetization unit Develop advertising campaigns using characters and earn advertising revenue from companies 2. The system of claim 1.

6. The monetization unit Sell ​​character merchandise and earn revenue from fans 2. The system of claim 1.

7. The monetization unit Hold events featuring characters and earn ticket sales or merchandise sales revenue 2. The system of claim 1.

8. The generation unit Estimating a user's emotion and adjusting a character's appearance or personality based on the estimated user's emotion 2. The system of claim 1.

Images