System

Abstract

An object of a system according to an embodiment is to automatically generate moving image content according to a user's preference.SOLUTION: A system includes an input unit, an analysis unit, a generation unit, and a provision unit. The input unit inputs information of a user's preference. The analysis unit analyzes the information input by the input unit. The generation unit sets a scenario or a character on the basis of the information analyzed by the analysis unit. The provision unit provides the moving image generated by the generation 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] Conventional technologies have had the problem of making it difficult to automatically generate video content tailored to a user's preferences.

[0005] The system according to the embodiment aims to automatically generate video content that matches the preferences of a user. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a generation unit, and a provision unit. The input unit inputs user preference information. The analysis unit analyzes the information input by the input unit. The generation unit sets a scenario or character based on the information analyzed by the analysis unit. The provision unit provides the video generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate video content that matches the preferences of the user. [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) A system according to an embodiment of the present invention allows a user to input preference information, and a generation AI analyzes the information to generate and provide drama videos tailored to the user's preferences. In this system, a user inputs information such as their preferred genre, characters, and storyline, and the generation AI analyzes the information to generate drama videos based on the user's preferences. The generated drama videos are customized based on the user's preferences, making them appealing to viewers. For example, if a user inputs preferences such as "mystery," "strong female protagonist," and "complex plot," the generation AI generates a mystery drama in which a strong female protagonist solves a complex case based on this information. This allows users to easily create drama videos tailored to their preferences. This allows the system to automatically generate and provide drama videos based on the user's preferences. For example, a user who likes a particular genre or character can generate drama videos tailored to their preferences. Furthermore, drama videos customized to the viewer's preferences can enhance the viewing experience.

[0029] A drama video generation system according to an embodiment includes an input unit, an analysis unit, a generation unit, and a provision unit. The input unit inputs user preference information. The user preference information includes, but is not limited to, genre, characters, and storyline. The input unit allows the user to input preferences such as "mystery," "strong female protagonist," and "complex plot." The analysis unit uses a generation AI to analyze the information input by the input unit. The analysis is performed using, but is not limited to, natural language processing or a machine learning algorithm. For example, the generation AI generates an appropriate scenario, character settings, and plot development based on the user preferences. The generation unit uses the generation AI to set the scenario and characters based on the information analyzed by the analysis unit. The generation unit includes, for example, a scenario generation unit and a character setting unit, and the scenario generation unit automatically generates a plot and uses a scenario template. The character setting unit sets the character's personality, background, relationships, and the like. The provision unit provides the video generated by the generation unit. The providing unit includes, for example, an editing unit that edits and modifies the generated video, such as by cutting, adding effects, and adjusting audio. This allows the drama video generation system according to the embodiment to automatically generate and provide drama videos based on the user's preferences. For example, it is possible to cast specific actors and actresses that the user likes, and the development of the scenario and plot is also adjusted according to the user's preferences. This allows for the provision of drama videos that are appealing to viewers.

[0030] The analysis unit includes a learning unit that learns user preferences. The learning unit learns user preferences using a generative AI. The learning is performed, for example, using a machine learning algorithm or a dataset, but is not limited to these examples. For example, the learning unit learns user preferences based on the user's past viewing history and feedback. The learning unit can also update the learning data in response to changes in the user's preferences. This allows the analysis unit to learn user preferences and improve the accuracy of the analysis. For example, the accuracy of the analysis can be improved based on the genres and characters of dramas that the user has watched in the past.

[0031] The generation unit includes a scenario generation unit that generates a scenario and a character setting unit that sets characters. The scenario generation unit generates a scenario using a generation AI. Scenario generation may involve, for example, automatic plot generation or the use of a scenario template, but is not limited to these examples. For example, the scenario generation unit generates an appropriate scenario based on a user's preferences. The character setting unit sets characters using a generation AI. Character setting may involve, for example, setting the character's personality, background, relationships, etc., but is not limited to these examples. For example, the character setting unit sets appropriate characters based on the user's preferences. This allows the generation unit to divide the responsibilities of scenario generation and character setting, thereby improving the accuracy of generation. For example, the scenario generation unit can generate a complex plot based on the user's preferences, and the character setting unit can set attractive characters based on the user's preferences.

[0032] The providing unit includes an editing unit that edits and modifies the generated video. The editing unit edits and modifies the generated video using the generation AI. Editing and modification includes, but is not limited to, cutting, adding effects, and adjusting audio. For example, the editing unit can cut unnecessary scenes from the generated video and add visually appealing effects. The editing unit can also adjust the audio of the generated video to make it easier for viewers to hear. As a result, the providing unit edits and modifies the generated video, thereby improving the quality of the video to be provided. For example, the editing unit can customize the generated video based on user preferences and provide a video that is appealing to viewers.

[0033] The input unit allows a user to input information about a preferred genre, character, and storyline. Examples of genres include, but are not limited to, action, drama, and comedy. Examples of characters include, but are not limited to, personalities, backgrounds, and relationships. Examples of storylines include, but are not limited to, plot developments, climaxes, and endings. For example, a user can use the input unit to input preferred information such as "mystery," "strong female protagonist," and "complex plot." This allows the user to input detailed preferred information, thereby improving the degree of customization of the generated video. For example, by inputting a specific genre or character that the user prefers, the generated video can be more tailored to the user's preferences.

[0034] The generation unit can generate a scenario, character settings, and plot development based on the user's preferences. Scenario generation can be, for example, automatic plot generation or the use of a scenario template, but is not limited to these examples. Character settings can be, for example, setting character personalities, backgrounds, relationships, etc., but is not limited to these examples. Plot development can be, for example, storyboard creation or the use of a scenario template, but is not limited to these examples. For example, the generation unit can generate an appropriate scenario, character settings, and plot development based on the user's preferences. This improves the accuracy of videos generated based on the user's preferences. For example, if the user inputs preferences such as "mystery," "strong female protagonist," and "complex plot," the generation unit can generate a mystery drama in which a strong female protagonist solves a complex case based on this.

[0035] The input unit can analyze the user's past input history and suggest the optimal input method. The input unit uses a generation AI to analyze the user's past input history. The input history includes, for example, past input data, input frequency, and input content categories, but is not limited to these examples. For example, the input unit can automatically display genres and characters that the user has frequently input in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest genres and characters that will be used in a specific time period based on the user's past input history. In this way, by analyzing the past input history, the optimal input method can be suggested to the user. For example, automatically displaying genres and characters that the user has frequently input in the past as candidates improves input efficiency.

[0036] The input unit can present input candidates based on the user's current interests and trends when the user types. The input unit uses generative AI to analyze the user's current interests and trends. Examples of interests and trends include, but are not limited to, social media trends, news articles, and search history. For example, the input unit can suggest related genres and characters based on topics recently searched by the user. The input unit can also present input candidates based on trends the user follows on social media. Furthermore, the input unit can suggest related storylines based on the content of videos the user recently watched. This improves input efficiency by presenting input candidates based on the user's current interests and trends. For example, suggesting related genres and characters based on topics recently searched by the user can reduce the effort required for input.

[0037] The input unit can select the optimal input means depending on the user's input method when inputting. The input unit uses a generative AI to analyze the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, when a user inputs a favorite genre or character by voice, the input unit can support the input using voice recognition technology. Furthermore, when a user inputs a detailed storyline in text, the input unit can support the input using text analysis technology. Furthermore, when a user inputs an image of a character using an image, the input unit can support the input using image recognition technology. This improves the accuracy of the input by selecting the optimal input means depending on the user's input method. For example, when a user inputs a favorite genre or character by voice, the accuracy of the input improves by supporting the input using voice recognition technology.

[0038] The input unit can customize the input method by reflecting the user's past feedback during input. The input unit uses a generative AI to analyze the user's past feedback. The feedback includes, but is not limited to, the user's ratings, comments, and usage history. For example, the input unit can prioritize providing input methods that the user has previously preferred. The input unit can also adjust to avoid input methods that the user has previously dissatisfied with. Furthermore, the input unit can optimize the layout of the input interface based on the user's past feedback. This allows the input method to be optimized by reflecting the user's past feedback. For example, preferentially providing input methods that the user has previously preferred improves input efficiency.

[0039] The input unit can present highly relevant input candidates by taking into account the user's geographical location information when inputting. The input unit uses a generative AI to analyze the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP address, and location information services. For example, if the user is in a specific area, the input unit can suggest genres and characters related to that area. Also, if the user is traveling, the input unit can suggest storylines related to the travel destination. Furthermore, if the user is participating in a specific event, the input unit can present input candidates related to that event. In this way, highly relevant input candidates can be presented by taking into account the user's geographical location information. For example, if the user is in a specific area, suggesting genres and characters related to that area improves input efficiency.

[0040] The input unit can analyze the user's social media activity during input and present related input candidates. The input unit uses a generative AI to analyze the user's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the input unit can analyze the content of posts from accounts the user follows on social media and suggest related genres and characters. The input unit can also present input candidates based on content shared by the user on social media. Furthermore, the input unit can suggest related storylines based on the activities of the user's friends on social media. In this way, related input candidates can be presented by analyzing the user's social media activity. For example, analyzing the content of posts from accounts the user follows on social media and suggesting related genres and characters improves input efficiency.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past viewing history. The analysis unit uses a generative AI to analyze the user's past viewing history. The viewing history includes, but is not limited to, a list of viewed content, viewing time, and viewing frequency. For example, the analysis unit can improve the accuracy of the analysis based on the genres and characters of dramas the user has previously viewed. The analysis unit can also predict preferred plot developments from the user's past viewing history and reflect this in the analysis. Furthermore, the analysis unit can optimize the analysis results based on the user's ratings of videos they have previously viewed. This improves the accuracy of the analysis by referring to the user's past viewing history. For example, the analysis can improve the accuracy of the analysis based on the genres and characters of dramas the user has previously viewed.

[0042] During analysis, the analysis unit can apply different analysis methods depending on the category of the user's input content. The analysis unit uses a generation AI to analyze the category of the user's input content. Examples of categories of input content include, but are not limited to, genres, themes, and topics. For example, if the user likes mysteries, the analysis unit can apply an analysis method specialized for mysteries. Furthermore, if the user likes romance, the analysis unit can apply an analysis method specialized for romance. Furthermore, if the user likes action, the analysis unit can apply an analysis method specialized for action. In this way, by applying an analysis method depending on the category of the user's input content, the accuracy of the analysis is improved. For example, if the user likes mysteries, the accuracy of the analysis is improved by applying an analysis method specialized for mysteries.

[0043] The analysis unit can optimize the analysis algorithm by reflecting user feedback during analysis. The analysis unit uses a generative AI to analyze user feedback. Examples of feedback include, but are not limited to, user ratings, comments, and usage history. For example, the analysis unit can adjust the analysis algorithm based on feedback previously provided by the user. Furthermore, if the user is satisfied with a particular analysis result, the analysis unit can preferentially apply that method. Furthermore, if the user is dissatisfied with a particular analysis result, the analysis unit can adjust to avoid that method. In this way, the analysis algorithm can be optimized by reflecting user feedback. For example, adjusting the analysis algorithm based on feedback previously provided by the user improves the accuracy of the analysis.

[0044] The analysis unit can take into account the user's geographic location information during analysis. The analysis unit uses a generating AI to analyze the user's geographic location information. Examples of geographic location information include, but are not limited to, GPS data, IP addresses, and location information services. For example, if the user is in a specific area, the analysis unit can prioritize analyzing genres and characters related to that area. Also, if the user is traveling, the analysis unit can analyze storylines related to the user's travel destination. Furthermore, if the user is participating in a specific event, the analysis unit can perform analysis related to that event. This improves the accuracy of the analysis by taking the user's geographic location information into account. For example, if the user is in a specific area, the analysis unit prioritizes analyzing genres and characters related to that area, thereby improving the accuracy of the analysis.

[0045] The analysis unit can improve the accuracy of the analysis by referring to the user's social media activity during analysis. The analysis unit uses a generative AI to analyze the user's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the analysis unit can reflect the content posted by accounts the user follows on social media in the analysis. The analysis unit can also reflect content shared by the user on social media in the analysis. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the activity of the user's friends on social media. In this way, the accuracy of the analysis is improved by referring to the user's social media activity. For example, the accuracy of the analysis is improved by reflecting the content posted by accounts the user follows on social media in the analysis.

[0046] The analysis unit can take the user's market data into consideration when performing the analysis. The analysis unit uses the generation AI to analyze the user's market data. Market data includes, but is not limited to, sales data, customer data, and competitor data. For example, the analysis unit can improve the accuracy of the analysis based on market data in which the user is interested. The analysis unit can also perform analysis based on products and services that the user has purchased in the past. Furthermore, the analysis unit can analyze the user's market data and provide optimal analysis results. In this way, the accuracy of the analysis is improved by taking the user's market data into consideration. For example, the accuracy of the analysis can be improved based on market data in which the user is interested.

[0047] During generation, the generation unit can improve the accuracy of generation by referring to the user's past viewing history. The generation unit uses a generation AI to analyze the user's past viewing history. The viewing history includes, but is not limited to, a list of viewed content, viewing time, and viewing frequency. For example, the generation unit can improve the accuracy of generation based on the genres and characters of dramas the user has previously watched. The generation unit can also predict preferred plot developments from the user's past viewing history and reflect this in the generation. Furthermore, the generation unit can optimize the generation results based on the user's ratings of videos they have previously watched. This improves the accuracy of generation by referring to the user's past viewing history. For example, the accuracy of generation can be improved based on the genres and characters of dramas the user has previously watched.

[0048] During generation, the generation unit can apply different generation methods depending on the category of the user's input content. The generation unit uses a generation AI to analyze the category of the user's input content. Examples of categories of input content include, but are not limited to, genres, themes, and topics. For example, if the user likes mysteries, the generation unit can apply a generation method specialized for mysteries. Also, if the user likes romance, the generation unit can apply a generation method specialized for romance. Furthermore, if the user likes action, the generation unit can apply a generation method specialized for action. In this way, by applying a generation method depending on the category of the user's input content, the accuracy of generation is improved. For example, if the user likes mysteries, the accuracy of generation is improved by applying a generation method specialized for mysteries.

[0049] The generation unit can optimize the generation algorithm by reflecting user feedback during generation. The generation unit uses the generation AI to analyze user feedback. Feedback includes, but is not limited to, user ratings, comments, and usage history. For example, the generation unit can adjust the generation algorithm based on feedback previously provided by the user. Furthermore, if the user is satisfied with a particular generation result, the generation unit can preferentially apply that method. Furthermore, if the user is dissatisfied with a particular generation result, the generation unit can adjust to avoid that method. In this way, the generation algorithm can be optimized by reflecting user feedback. For example, adjusting the generation algorithm based on feedback previously provided by the user improves generation accuracy.

[0050] The generation unit can generate the game taking into account the user's geographical location information. The generation unit uses a generation AI to analyze the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP address, and location information services. For example, if the user is in a specific area, the generation unit can prioritize generating genres and characters related to that area. Also, if the user is traveling, the generation unit can generate a storyline related to the travel destination. Furthermore, if the user is participating in a specific event, the generation unit can generate a storyline related to that event. This improves the accuracy of generation by taking the user's geographical location information into account. For example, if the user is in a specific area, the generation unit can prioritize generating genres and characters related to that area, thereby improving the accuracy of generation.

[0051] The generation unit can improve the accuracy of generation by referring to the user's social media activity during generation. The generation unit uses a generation AI to analyze the user's social media activity. 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 can reflect the content posted by accounts the user follows on social media in the generation. The generation unit can also reflect content shared by the user on social media in the generation. Furthermore, the generation unit can improve the accuracy of generation by referring to the activity of the user's friends on social media. In this way, the accuracy of generation is improved by referring to the user's social media activity. For example, the accuracy of generation is improved by reflecting the content posted by accounts the user follows on social media in the generation.

[0052] The generation unit can generate the data by taking into account the user's market data. The generation unit uses a generation AI to analyze the user's market data. Market data includes, but is not limited to, sales data, customer data, and competitor data. For example, the generation unit can improve the accuracy of the generation based on market data in which the user is interested. The generation unit can also generate data based on products and services that the user has purchased in the past. Furthermore, the generation unit can analyze the user's market data and provide optimal generation results. In this way, the accuracy of the generation is improved by taking into account the user's market data. For example, the accuracy of the generation can be improved based on market data in which the user is interested.

[0053] The provision unit can select the optimal delivery method by referring to the user's past viewing history when providing content. The provision unit uses a generation AI to analyze the user's past viewing history. The viewing history includes, but is not limited to, a list of viewed content, viewing time, and viewing frequency. For example, the provision unit can select the optimal delivery method based on the genres and characters of dramas the user has previously viewed. The provision unit can also predict preferred plot developments from the user's past viewing history and reflect this in the provision. Furthermore, the provision unit can optimize the delivery method based on the user's ratings of videos they have previously viewed. This allows the optimal delivery method to be selected by referring to the user's past viewing history. For example, selecting the optimal delivery method based on the genres and characters of dramas the user has previously viewed improves the viewing experience.

[0054] The providing unit can customize the content provided based on the user's current interests and trends at the time of provision. The providing unit uses a generation AI to analyze the user's current interests and trends. Examples of interests and trends include, but are not limited to, social media trends, news articles, and search history. For example, the providing unit can provide related genres and characters based on topics recently searched by the user. The providing unit can also customize the content provided based on trends the user follows on social media. Furthermore, the providing unit can provide related storylines based on the content of videos recently viewed by the user. This improves the viewing experience by customizing the content provided based on the user's current interests and trends. For example, the viewing experience improves by providing related genres and characters based on topics recently searched by the user.

[0055] The providing unit can improve the delivery method by reflecting user feedback when delivering content. The providing unit uses a generation AI to analyze user feedback. The feedback includes, but is not limited to, user ratings, comments, and usage history. For example, the providing unit can adjust the delivery method based on feedback previously provided by the user. Furthermore, if the user is satisfied with a particular delivery method, the providing unit can preferentially apply that method. Furthermore, if the user is dissatisfied with a particular delivery method, the providing unit can adjust to avoid that method. In this way, the delivery method can be optimized by reflecting user feedback. For example, adjusting the delivery method based on feedback previously provided by the user improves the viewing experience.

[0056] The providing unit can select the optimal delivery method by taking into account the user's geographical location information when providing content. The providing unit uses a generation AI to analyze the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP address, and location information services. For example, if the user is in a specific area, the providing unit can prioritize providing genres and characters related to that area. Also, if the user is traveling, the providing unit can provide storylines related to the user's travel destination. Furthermore, if the user is participating in a specific event, the providing unit can provide content related to that event. This allows the optimal delivery method to be selected by taking into account the user's geographical location information. For example, if the user is in a specific area, the viewing experience can be improved by prioritized provision of genres and characters related to that area.

[0057] The providing unit can customize the provided content by analyzing the user's social media activity at the time of providing the content. The providing unit uses a generation AI to analyze the user's social media activity. 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 providing unit can reflect the content posted by accounts the user follows on social media in the provided content. The providing unit can also reflect content shared by the user on social media in the provided content. Furthermore, the providing unit can customize the provided content by taking into account the activity of the user's friends on social media. In this way, the provided content can be customized by analyzing the user's social media activity. For example, reflecting the content posted by accounts the user follows on social media in the provided content improves the viewing experience.

[0058] The providing unit can customize the delivery method by reflecting the user's past feedback when delivering content. The providing unit uses a generation AI to analyze the user's past feedback. The feedback includes, but is not limited to, the user's ratings, comments, and usage history. For example, the providing unit can prioritize delivery methods that the user has previously preferred. The providing unit can also adjust the delivery method to avoid delivery methods that the user has previously dissatisfied with. Furthermore, the providing unit can optimize the layout of the delivery interface based on the user's past feedback. This allows the delivery method to be optimized by reflecting the user's past feedback. For example, the viewing experience can be improved by prioritizing delivery methods that the user has previously preferred.

[0059] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit analyzes past learning data using a generative AI. The learning data includes, but is not limited to, past viewing history, user feedback, and content metadata. For example, the learning unit can select an optimal learning algorithm based on the past learning data. The learning unit can also extract effective learning methods from the past learning data and reflect them in the algorithm. Furthermore, the learning unit can analyze the past learning data to improve the accuracy of the learning algorithm. In this way, the learning algorithm can be optimized by referring to the past learning data. For example, selecting an optimal learning algorithm based on the past learning data improves the accuracy of learning.

[0060] During learning, the learning unit can analyze fluctuations in the user's viewing history and adjust the update frequency of the learning data. The learning unit uses the generation AI to analyze fluctuations in the user's viewing history. Fluctuations in viewing history include, but are not limited to, changes in viewing frequency and changes in viewing content. For example, if the user's viewing history changes frequently, the learning unit can increase the update frequency of the learning data. Also, if the user's viewing history is stable, the learning unit can decrease the update frequency of the learning data. Furthermore, the learning unit can analyze the change pattern of the user's viewing history and set an optimal update frequency. In this way, the update frequency of the learning data can be optimized by analyzing fluctuations in the user's viewing history. For example, if the user's viewing history changes frequently, increasing the update frequency of the learning data improves the accuracy of learning.

[0061] During learning, the learning unit can weight the learning data based on the time when the viewing history was submitted. The learning unit uses a generation AI to analyze the time when the viewing history was submitted. The time when the viewing history was submitted includes, but is not limited to, the timing and frequency of the viewing history submission. For example, the learning unit can weight the learning data based on recent viewing history. The learning unit can also weight the learning data based on past viewing history. Furthermore, the learning unit can adjust the weighting of the learning data depending on the time when the viewing history was submitted. Thus, weighting the learning data based on the time when the viewing history was submitted improves the accuracy of learning. For example, weighting the learning data based on recent viewing history improves the accuracy of learning.

[0062] The learning unit can adjust the learning algorithm by reflecting user feedback during learning. The learning unit uses a generative AI to analyze user feedback. Feedback includes, but is not limited to, user ratings, comments, and usage history. For example, the learning unit can adjust the learning algorithm based on feedback previously provided by the user. Furthermore, if the user is satisfied with a particular learning result, the learning unit can preferentially apply that method. Furthermore, if the user is dissatisfied with a particular learning result, the learning unit can adjust to avoid that method. In this way, the learning algorithm can be optimized by reflecting user feedback. For example, adjusting the learning algorithm based on feedback previously provided by the user improves learning accuracy.

[0063] When generating a scenario, the scenario generation unit can improve the accuracy of the scenario by referring to the user's past viewing history. The scenario generation unit uses a generation AI to analyze the user's past viewing history. The viewing history includes, for example, a list of viewed content, viewing time, viewing frequency, etc., but is not limited to these examples. For example, the scenario generation unit can improve the accuracy of the scenario based on the genres and characters of dramas the user has watched in the past. The scenario generation unit can also predict the user's preferred plot development from the user's past viewing history and reflect this in the scenario. Furthermore, the scenario generation unit can optimize the scenario based on the user's evaluation of videos they have watched in the past. In this way, the accuracy of the scenario can be improved by referring to the user's past viewing history. For example, the accuracy of the scenario can be improved based on the genres and characters of dramas the user has watched in the past.

[0064] When generating a scenario, the scenario generation unit can apply different scenario generation methods depending on the category of the user's input content. The scenario generation unit uses a generation AI to analyze the category of the user's input content. The category of the input content includes, but is not limited to, genre, theme, topic, etc. For example, if the user likes mysteries, the scenario generation unit can apply a scenario generation method specialized for mysteries. Furthermore, if the user likes romance, the scenario generation unit can apply a scenario generation method specialized for romance. Furthermore, if the user likes action, the scenario generation unit can apply a scenario generation method specialized for action. In this way, by applying a scenario generation method depending on the category of the user's input content, the accuracy of the scenario is improved. For example, if the user likes mysteries, the accuracy of the scenario is improved by applying a scenario generation method specialized for mysteries.

[0065] The scenario generation unit can optimize the scenario generation algorithm by reflecting user feedback when generating a scenario. The scenario generation unit uses a generation AI to analyze user feedback. The feedback includes, but is not limited to, user ratings, comments, and usage history. For example, the scenario generation unit can adjust the scenario generation algorithm based on feedback previously provided by the user. Furthermore, if the user is satisfied with a particular scenario, the scenario generation unit can preferentially apply that method. Furthermore, if the user is dissatisfied with a particular scenario, the scenario generation unit can adjust to avoid that method. In this way, the scenario generation algorithm can be optimized by reflecting user feedback. For example, adjusting the scenario generation algorithm based on feedback previously provided by the user improves the accuracy of the scenario.

[0066] The scenario generation unit can generate a scenario taking into account the user's geographical location information when generating a scenario. The scenario generation unit uses a generation AI to analyze the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, location information services, etc. For example, if the user is in a specific area, the scenario generation unit can prioritize incorporating genres and characters related to that area into the scenario. Also, if the user is traveling, the scenario generation unit can incorporate a storyline related to the user's travel destination into the scenario. Furthermore, if the user is participating in a specific event, the scenario generation unit can generate a scenario related to that event. In this way, by taking the user's geographical location information into consideration, the accuracy of the scenario is improved. For example, if the user is in a specific area, the accuracy of the scenario is improved by prioritized incorporation of genres and characters related to that area into the scenario.

[0067] The scenario generation unit can improve the accuracy of the scenario by referring to the user's social media activity when generating a scenario. The scenario generation unit uses a generation AI to analyze the user's social media activity. 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 scenario generation unit can reflect the content posted by accounts the user follows on social media in the scenario. The scenario generation unit can also reflect content shared by the user on social media in the scenario. Furthermore, the scenario generation unit can improve the accuracy of the scenario by referring to the activity of the user's friends on social media. In this way, the accuracy of the scenario is improved by referring to the user's social media activity. For example, the accuracy of the scenario is improved by reflecting the content posted by accounts the user follows on social media in the scenario.

[0068] The scenario generation unit can generate a scenario by taking into account the user's market data. The scenario generation unit uses a generation AI to analyze the user's market data. Market data includes, but is not limited to, sales data, customer data, and competitor data. For example, the scenario generation unit can improve the accuracy of the scenario based on market data in which the user is interested. The scenario generation unit can also generate a scenario based on products or services that the user has purchased in the past. Furthermore, the scenario generation unit can analyze the user's market data and provide an optimal scenario. In this way, the accuracy of the scenario is improved by taking the user's market data into consideration. For example, the accuracy of the scenario can be improved based on market data in which the user is interested.

[0069] When setting a character, the character setting unit can improve the accuracy of the character setting by referring to the user's past viewing history. The character setting unit uses a generation AI to analyze the user's past viewing history. The viewing history includes, but is not limited to, a list of viewed content, viewing time, and viewing frequency. For example, the character setting unit can improve the accuracy of the character setting based on characters from dramas the user has watched in the past. The character setting unit can also predict a preferred character setting from the user's past viewing history and reflect this in the setting. Furthermore, the character setting unit can optimize the character setting based on the user's evaluation of videos they have watched in the past. In this way, the accuracy of the character setting is improved by referring to the user's past viewing history. For example, the accuracy of the character setting can be improved based on characters from dramas the user has watched in the past.

[0070] When setting a character, the character setting unit can apply different character setting methods depending on the category of the user's input content. The character setting unit uses a generation AI to analyze the category of the user's input content. Examples of categories of input content include, but are not limited to, genre, theme, and topic. For example, if the user likes mysteries, the character setting unit can apply a character setting method specialized for mysteries. Furthermore, if the user likes romance, the character setting unit can apply a character setting method specialized for romance. Furthermore, if the user likes action, the character setting unit can apply a character setting method specialized for action. In this way, applying a character setting method depending on the category of the user's input content improves the accuracy of character setting. For example, if the user likes mysteries, applying a character setting method specialized for mysteries improves the accuracy of character setting.

[0071] The character setting unit can optimize the character setting algorithm by reflecting user feedback when setting a character. The character setting unit uses a generation AI to analyze the user's feedback. The feedback includes, but is not limited to, user ratings, comments, and usage history. For example, the character setting unit can adjust the character setting algorithm based on feedback previously provided by the user. Furthermore, if the user is satisfied with a particular character setting, the character setting unit can preferentially apply that method. Furthermore, if the user is dissatisfied with a particular character setting, the character setting unit can adjust to avoid that method. In this way, the character setting algorithm can be optimized by reflecting user feedback. For example, adjusting the character setting algorithm based on feedback previously provided by the user improves the accuracy of character setting.

[0072] The character setting unit can set the character taking into account the user's geographical location information when setting the character. The character setting unit uses a generation AI to analyze the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, location information services, etc. For example, if the user is in a specific area, the character setting unit can set a character related to that area. Also, if the user is traveling, the character setting unit can set a character related to the travel destination. Furthermore, if the user is participating in a specific event, the character setting unit can set a character related to that event. This improves the accuracy of character setting by taking the user's geographical location information into account. For example, if the user is in a specific area, setting a character related to that area improves the accuracy of character setting.

[0073] The character setting unit can improve the accuracy of the character setting by referring to the user's social media activity when setting the character. The character setting unit uses a generation AI to analyze the user's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the character setting unit can reflect the content posted by accounts the user follows on social media in the character setting. The character setting unit can also reflect content shared by the user on social media in the character setting. Furthermore, the character setting unit can improve the accuracy of the character setting by referring to the activity of the user's friends on social media. In this way, the accuracy of the character setting is improved by referring to the user's social media activity. For example, the accuracy of the character setting is improved by reflecting the content posted by accounts the user follows on social media in the character setting.

[0074] The character setting unit can set the character by taking into account the user's market data. The character setting unit uses a generation AI to analyze the user's market data. Market data includes, but is not limited to, sales data, customer data, and competitor data. For example, the character setting unit can improve the accuracy of the character setting based on market data that the user is interested in. The character setting unit can also set the character based on products and services that the user has purchased in the past. Furthermore, the character setting unit can analyze the user's market data and provide an optimal character setting. In this way, the accuracy of the character setting is improved by taking the user's market data into consideration. For example, the accuracy of the character setting can be improved based on market data that the user is interested in.

[0075] During editing, the editing department can improve the accuracy of editing by referring to the user's past viewing history. The editing department uses a generative AI to analyze the user's past viewing history. The viewing history includes, but is not limited to, a list of viewed content, viewing time, and viewing frequency. For example, the editing department can improve the accuracy of editing based on the genres and characters of dramas the user has previously watched. The editing department can also predict preferred plot developments from the user's past viewing history and reflect this in the editing. Furthermore, the editing department can optimize the editing results based on the user's ratings of videos they have previously watched. This improves the accuracy of editing by referring to the user's past viewing history. For example, the editing department can improve the accuracy of editing based on the genres and characters of dramas the user has previously watched.

[0076] During editing, the editing department can apply different editing techniques depending on the category of the user's input content. The editing department uses a generation AI to analyze the category of the user's input content. The category of the input content includes, but is not limited to, genre, theme, topic, etc. For example, if the user likes mysteries, the editing department can apply an editing technique specialized for mysteries. Furthermore, if the user likes romance, the editing department can apply an editing technique specialized for romance. Furthermore, if the user likes action, the editing department can apply an editing technique specialized for action. In this way, by applying an editing technique depending on the category of the user's input content, the accuracy of editing is improved. For example, if the user likes mysteries, the accuracy of editing is improved by applying an editing technique specialized for mysteries.

[0077] The editorial department can optimize the editing algorithm by reflecting user feedback during editing. The editorial department uses a generative AI to analyze user feedback. The feedback includes, but is not limited to, user ratings, comments, and usage history. For example, the editorial department can adjust the editing algorithm based on feedback previously provided by the user. Furthermore, if the user is satisfied with a particular editing result, the editorial department can preferentially apply that technique. Furthermore, if the user is dissatisfied with a particular editing result, the editorial department can adjust to avoid that technique. In this way, the editing algorithm can be optimized by reflecting user feedback. For example, adjusting the editing algorithm based on feedback previously provided by the user improves editing accuracy.

[0078] The editorial department can take the user's geographic location information into consideration when editing. The editorial department uses a generative AI to analyze the user's geographic location information. Examples of geographic location information include, but are not limited to, GPS data, IP addresses, and location information services. For example, if the user is in a specific area, the editorial department can prioritize editing genres and characters related to that area. Also, if the user is traveling, the editorial department can edit storylines related to the user's travel destination. Furthermore, if the user is participating in a specific event, the editorial department can edit related to that event. This improves the accuracy of editing by taking the user's geographic location information into consideration. For example, if the user is in a specific area, the editorial department can prioritize editing genres and characters related to that area, improving the accuracy of editing.

[0079] The editorial department can improve the accuracy of editing by referring to the user's social media activity when editing. The editorial department uses generative AI to analyze the user's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the editorial department can reflect the content posted by accounts the user follows on social media in the edits. The editorial department can also reflect content shared by the user on social media in the edits. Furthermore, the editorial department can improve the accuracy of editing by referring to the activity of the user's friends on social media. In this way, the accuracy of editing is improved by referring to the user's social media activity. For example, the accuracy of editing is improved by reflecting the content posted by accounts the user follows on social media in the edits.

[0080] The editorial department can take the user's market data into consideration when editing. The editorial department uses a generative AI to analyze the user's market data. Market data includes, but is not limited to, sales data, customer data, and competitor data. For example, the editorial department can improve the accuracy of editing based on market data in which the user is interested. The editorial department can also edit based on products and services the user has purchased in the past. Furthermore, the editorial department can analyze the user's market data and provide optimal editing results. In this way, the accuracy of editing is improved by taking the user's market data into consideration. For example, the accuracy of editing can be improved based on market data in which the user is interested.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The input unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display as candidates genres and characters that the user has frequently input in the past. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest genres and characters that will be used in a specific time period based on the user's past input history. In this way, by analyzing the past input history, it is possible to suggest the optimal input method to the user. For example, by automatically displaying as candidates genres and characters that the user has frequently input in the past, input efficiency is improved.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past viewing history. For example, the analysis unit can improve the accuracy of the analysis based on the genres and characters of dramas the user has watched in the past. The analysis unit can also predict the user's preferred plot developments from the user's past viewing history and reflect this in the analysis. Furthermore, the analysis unit can optimize the analysis results based on the user's ratings of videos they have watched in the past. In this way, the accuracy of the analysis can be improved by referring to the user's past viewing history. For example, the accuracy of the analysis can be improved based on the genres and characters of dramas the user has watched in the past.

[0084] When generating a scenario, the scenario generation unit can improve the accuracy of the scenario by referring to the user's past viewing history. For example, the scenario generation unit can improve the accuracy of the scenario based on the genres and characters of dramas that the user has watched in the past. The scenario generation unit can also predict the user's preferred plot development from the user's past viewing history and reflect this in the scenario. Furthermore, the scenario generation unit can optimize the scenario based on the user's evaluations of videos that the user has watched in the past. In this way, the accuracy of the scenario can be improved by referring to the user's past viewing history. For example, the accuracy of the scenario can be improved based on the genres and characters of dramas that the user has watched in the past.

[0085] When setting a character, the character setting unit can improve the accuracy of the character setting by referring to the user's past viewing history. For example, the character setting unit can improve the accuracy of the character setting based on characters in dramas that the user has watched in the past. The character setting unit can also predict a preferred character setting from the user's past viewing history and reflect this in the setting. Furthermore, the character setting unit can optimize the character setting based on ratings of videos that the user has watched in the past. In this way, the accuracy of the character setting is improved by referring to the user's past viewing history. For example, the accuracy of the character setting can be improved based on characters in dramas that the user has watched in the past.

[0086] When editing, the editing department can improve the accuracy of editing by referring to the user's past viewing history. For example, the editing department can improve the accuracy of editing based on the genres and characters of dramas that the user has watched in the past. The editing department can also predict the user's preferred plot development from the user's past viewing history and reflect this in the editing. Furthermore, the editing department can optimize the editing results based on the user's ratings of videos that they have watched in the past. In this way, the accuracy of editing can be improved by referring to the user's past viewing history. For example, the accuracy of editing can be improved based on the genres and characters of dramas that the user has watched in the past.

[0087] The processing flow of the first embodiment will be briefly explained below.

[0088] Step 1: The input unit inputs user preference information. User preference information includes genre, characters, storyline, etc. For example, a user can input preferences such as "mystery," "strong female protagonist," and "complex plot." Step 2: The analysis unit uses the generation AI to analyze the information entered by the input unit. The analysis is performed using natural language processing and machine learning algorithms. For example, the generation AI generates an appropriate scenario, character settings, and plot development based on the user's preferences. Step 3: The generation unit uses generation AI to create scenarios and character settings based on the information analyzed by the analysis unit. The generation unit includes a scenario generation unit and a character setting unit, and the scenario generation unit automatically generates plots and uses scenario templates. The character setting unit sets the character's personality, background, relationships, etc. Step 4: The providing unit provides the video generated by the generating unit. The providing unit includes an editing unit that edits and modifies the generated video, such as by cutting, adding effects, and adjusting the audio.

[0089] (Example 2) A system according to an embodiment of the present invention allows a user to input preference information, and a generation AI analyzes the information to generate and provide drama videos tailored to the user's preferences. In this system, a user inputs information such as their preferred genre, characters, and storyline, and the generation AI analyzes the information to generate drama videos based on the user's preferences. The generated drama videos are customized based on the user's preferences, making them appealing to viewers. For example, if a user inputs preferences such as "mystery," "strong female protagonist," and "complex plot," the generation AI generates a mystery drama in which a strong female protagonist solves a complex case based on this information. This allows users to easily create drama videos tailored to their preferences. This allows the system to automatically generate and provide drama videos based on the user's preferences. For example, a user who likes a particular genre or character can generate drama videos tailored to their preferences. Furthermore, drama videos customized to the viewer's preferences can enhance the viewing experience.

[0090] A drama video generation system according to an embodiment includes an input unit, an analysis unit, a generation unit, and a provision unit. The input unit inputs user preference information. The user preference information includes, but is not limited to, genre, characters, and storyline. The input unit allows the user to input preferences such as "mystery," "strong female protagonist," and "complex plot." The analysis unit uses a generation AI to analyze the information input by the input unit. The analysis is performed using, but is not limited to, natural language processing or a machine learning algorithm. For example, the generation AI generates an appropriate scenario, character settings, and plot development based on the user preferences. The generation unit uses the generation AI to set the scenario and characters based on the information analyzed by the analysis unit. The generation unit includes, for example, a scenario generation unit and a character setting unit, and the scenario generation unit automatically generates a plot and uses a scenario template. The character setting unit sets the character's personality, background, relationships, and the like. The provision unit provides the video generated by the generation unit. The providing unit includes, for example, an editing unit that edits and modifies the generated video, such as by cutting, adding effects, and adjusting audio. This allows the drama video generation system according to the embodiment to automatically generate and provide drama videos based on the user's preferences. For example, it is possible to cast specific actors and actresses that the user likes, and the development of the scenario and plot is also adjusted according to the user's preferences. This allows for the provision of drama videos that are appealing to viewers.

[0091] The analysis unit includes a learning unit that learns user preferences. The learning unit learns user preferences using a generative AI. The learning is performed, for example, using a machine learning algorithm or a dataset, but is not limited to these examples. For example, the learning unit learns user preferences based on the user's past viewing history and feedback. The learning unit can also update the learning data in response to changes in the user's preferences. This allows the analysis unit to learn user preferences and improve the accuracy of the analysis. For example, the accuracy of the analysis can be improved based on the genres and characters of dramas that the user has watched in the past.

[0092] The generation unit includes a scenario generation unit that generates a scenario and a character setting unit that sets characters. The scenario generation unit generates a scenario using a generation AI. Scenario generation may involve, for example, automatic plot generation or the use of a scenario template, but is not limited to these examples. For example, the scenario generation unit generates an appropriate scenario based on a user's preferences. The character setting unit sets characters using a generation AI. Character setting may involve, for example, setting the character's personality, background, relationships, etc., but is not limited to these examples. For example, the character setting unit sets appropriate characters based on the user's preferences. This allows the generation unit to divide the responsibilities of scenario generation and character setting, thereby improving the accuracy of generation. For example, the scenario generation unit can generate a complex plot based on the user's preferences, and the character setting unit can set attractive characters based on the user's preferences.

[0093] The providing unit includes an editing unit that edits and modifies the generated video. The editing unit edits and modifies the generated video using the generation AI. Editing and modification includes, but is not limited to, cutting, adding effects, and adjusting audio. For example, the editing unit can cut unnecessary scenes from the generated video and add visually appealing effects. The editing unit can also adjust the audio of the generated video to make it easier for viewers to hear. As a result, the providing unit edits and modifies the generated video, thereby improving the quality of the video to be provided. For example, the editing unit can customize the generated video based on user preferences and provide a video that is appealing to viewers.

[0094] The input unit allows a user to input information about a preferred genre, character, and storyline. Examples of genres include, but are not limited to, action, drama, and comedy. Examples of characters include, but are not limited to, personalities, backgrounds, and relationships. Examples of storylines include, but are not limited to, plot developments, climaxes, and endings. For example, a user can use the input unit to input preferred information such as "mystery," "strong female protagonist," and "complex plot." This allows the user to input detailed preferred information, thereby improving the degree of customization of the generated video. For example, by inputting a specific genre or character that the user prefers, the generated video can be more tailored to the user's preferences.

[0095] The generation unit can generate a scenario, character settings, and plot development based on the user's preferences. Scenario generation can be, for example, automatic plot generation or the use of a scenario template, but is not limited to these examples. Character settings can be, for example, setting character personalities, backgrounds, relationships, etc., but is not limited to these examples. Plot development can be, for example, storyboard creation or the use of a scenario template, but is not limited to these examples. For example, the generation unit can generate an appropriate scenario, character settings, and plot development based on the user's preferences. This improves the accuracy of videos generated based on the user's preferences. For example, if the user inputs preferences such as "mystery," "strong female protagonist," and "complex plot," the generation unit can generate a mystery drama in which a strong female protagonist solves a complex case based on this.

[0096] The input unit can estimate a user's emotions and adjust the display method of the input interface based on the estimated user emotions. The input unit estimates the user's emotions using generative AI. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, if the user is feeling stressed, the input unit can provide a simple interface and minimize input steps. Also, if the user is relaxed, the input unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the input unit can prioritize voice input to allow the user to quickly input preferred information. This improves the user's input experience by adjusting the input interface according to the user's emotions. For example, if the user is feeling stressed, a simple interface can be provided to reduce the burden of input.

[0097] The input unit can analyze the user's past input history and suggest the optimal input method. The input unit uses a generation AI to analyze the user's past input history. The input history includes, for example, past input data, input frequency, and input content categories, but is not limited to these examples. For example, the input unit can automatically display genres and characters that the user has frequently input in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest genres and characters that will be used in a specific time period based on the user's past input history. In this way, by analyzing the past input history, the optimal input method can be suggested to the user. For example, automatically displaying genres and characters that the user has frequently input in the past as candidates improves input efficiency.

[0098] The input unit can present input candidates based on the user's current interests and trends when the user types. The input unit uses generative AI to analyze the user's current interests and trends. Examples of interests and trends include, but are not limited to, social media trends, news articles, and search history. For example, the input unit can suggest related genres and characters based on topics recently searched by the user. The input unit can also present input candidates based on trends the user follows on social media. Furthermore, the input unit can suggest related storylines based on the content of videos the user recently watched. This improves input efficiency by presenting input candidates based on the user's current interests and trends. For example, suggesting related genres and characters based on topics recently searched by the user can reduce the effort required for input.

[0099] The input unit can select the optimal input means depending on the user's input method when inputting. The input unit uses a generative AI to analyze the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, when a user inputs a favorite genre or character by voice, the input unit can support the input using voice recognition technology. Furthermore, when a user inputs a detailed storyline in text, the input unit can support the input using text analysis technology. Furthermore, when a user inputs an image of a character using an image, the input unit can support the input using image recognition technology. This improves the accuracy of the input by selecting the optimal input means depending on the user's input method. For example, when a user inputs a favorite genre or character by voice, the accuracy of the input improves by supporting the input using voice recognition technology.

[0100] The input unit can estimate a user's emotions and adjust the design of the input interface based on the estimated user emotions. The input unit estimates the user's emotions using generative AI. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, if the user is nervous, the input unit can provide an interface with a calm color scheme to reduce visual stress. Also, if the user is having fun, the input unit can provide an interface with a bright color scheme to make input work more enjoyable. Furthermore, if the user is tired, the input unit can provide a simple, highly visible interface to make input work easier. This improves the user's input experience by adjusting the design of the input interface according to the user's emotions. For example, if the user is nervous, the input unit can provide an interface with a calm color scheme to reduce visual stress.

[0101] The input unit can customize the input method by reflecting the user's past feedback during input. The input unit uses a generative AI to analyze the user's past feedback. The feedback includes, but is not limited to, the user's ratings, comments, and usage history. For example, the input unit can prioritize providing input methods that the user has previously preferred. The input unit can also adjust to avoid input methods that the user has previously dissatisfied with. Furthermore, the input unit can optimize the layout of the input interface based on the user's past feedback. This allows the input method to be optimized by reflecting the user's past feedback. For example, preferentially providing input methods that the user has previously preferred improves input efficiency.

[0102] The input unit can present highly relevant input candidates by taking into account the user's geographical location information when inputting. The input unit uses a generative AI to analyze the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP address, and location information services. For example, if the user is in a specific area, the input unit can suggest genres and characters related to that area. Also, if the user is traveling, the input unit can suggest storylines related to the travel destination. Furthermore, if the user is participating in a specific event, the input unit can present input candidates related to that event. In this way, highly relevant input candidates can be presented by taking into account the user's geographical location information. For example, if the user is in a specific area, suggesting genres and characters related to that area improves input efficiency.

[0103] The input unit can analyze the user's social media activity during input and present related input candidates. The input unit uses a generative AI to analyze the user's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the input unit can analyze the content of posts from accounts the user follows on social media and suggest related genres and characters. The input unit can also present input candidates based on content shared by the user on social media. Furthermore, the input unit can suggest related storylines based on the activities of the user's friends on social media. In this way, related input candidates can be presented by analyzing the user's social media activity. For example, analyzing the content of posts from accounts the user follows on social media and suggesting related genres and characters improves input efficiency.

[0104] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. The analysis unit uses generative AI to estimate the user's emotions. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide more options. Also, if the user is in a hurry, the analysis unit can perform a quick analysis and narrow down the optimal options. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results. This improves the accuracy of the analysis by adjusting the analysis algorithm according to the user's emotions. For example, if the user is relaxed, a detailed analysis can provide more options.

[0105] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past viewing history. The analysis unit uses a generative AI to analyze the user's past viewing history. The viewing history includes, but is not limited to, a list of viewed content, viewing time, and viewing frequency. For example, the analysis unit can improve the accuracy of the analysis based on the genres and characters of dramas the user has previously viewed. The analysis unit can also predict preferred plot developments from the user's past viewing history and reflect this in the analysis. Furthermore, the analysis unit can optimize the analysis results based on the user's ratings of videos they have previously viewed. This improves the accuracy of the analysis by referring to the user's past viewing history. For example, the analysis can improve the accuracy of the analysis based on the genres and characters of dramas the user has previously viewed.

[0106] During analysis, the analysis unit can apply different analysis methods depending on the category of the user's input content. The analysis unit uses a generation AI to analyze the category of the user's input content. Examples of categories of input content include, but are not limited to, genres, themes, and topics. For example, if the user likes mysteries, the analysis unit can apply an analysis method specialized for mysteries. Furthermore, if the user likes romance, the analysis unit can apply an analysis method specialized for romance. Furthermore, if the user likes action, the analysis unit can apply an analysis method specialized for action. In this way, by applying an analysis method depending on the category of the user's input content, the accuracy of the analysis is improved. For example, if the user likes mysteries, the accuracy of the analysis is improved by applying an analysis method specialized for mysteries.

[0107] The analysis unit can optimize the analysis algorithm by reflecting user feedback during analysis. The analysis unit uses a generative AI to analyze user feedback. Examples of feedback include, but are not limited to, user ratings, comments, and usage history. For example, the analysis unit can adjust the analysis algorithm based on feedback previously provided by the user. Furthermore, if the user is satisfied with a particular analysis result, the analysis unit can preferentially apply that method. Furthermore, if the user is dissatisfied with a particular analysis result, the analysis unit can adjust to avoid that method. In this way, the analysis algorithm can be optimized by reflecting user feedback. For example, adjusting the analysis algorithm based on feedback previously provided by the user improves the accuracy of the analysis.

[0108] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit estimates the user's emotions using a generative AI. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. In this way, adjusting the display method of the analysis results according to the user's emotions deepens the user's understanding. For example, if the user is nervous, providing a simple, highly visible display method deepens the user's understanding of the analysis results.

[0109] The analysis unit can take into account the user's geographic location information during analysis. The analysis unit uses a generating AI to analyze the user's geographic location information. Examples of geographic location information include, but are not limited to, GPS data, IP addresses, and location information services. For example, if the user is in a specific area, the analysis unit can prioritize analyzing genres and characters related to that area. Also, if the user is traveling, the analysis unit can analyze storylines related to the user's travel destination. Furthermore, if the user is participating in a specific event, the analysis unit can perform analysis related to that event. This improves the accuracy of the analysis by taking the user's geographic location information into account. For example, if the user is in a specific area, the analysis unit prioritizes analyzing genres and characters related to that area, thereby improving the accuracy of the analysis.

[0110] The analysis unit can improve the accuracy of the analysis by referring to the user's social media activity during analysis. The analysis unit uses a generative AI to analyze the user's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the analysis unit can reflect the content posted by accounts the user follows on social media in the analysis. The analysis unit can also reflect content shared by the user on social media in the analysis. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the activity of the user's friends on social media. In this way, the accuracy of the analysis is improved by referring to the user's social media activity. For example, the accuracy of the analysis is improved by reflecting the content posted by accounts the user follows on social media in the analysis.

[0111] The analysis unit can take the user's market data into consideration when performing the analysis. The analysis unit uses the generation AI to analyze the user's market data. Market data includes, but is not limited to, sales data, customer data, and competitor data. For example, the analysis unit can improve the accuracy of the analysis based on market data in which the user is interested. The analysis unit can also perform analysis based on products and services that the user has purchased in the past. Furthermore, the analysis unit can analyze the user's market data and provide optimal analysis results. In this way, the accuracy of the analysis is improved by taking the user's market data into consideration. For example, the accuracy of the analysis can be improved based on market data in which the user is interested.

[0112] The generation unit can estimate the user's emotions and adjust the content of the generated video based on the estimated user emotions. The generation unit estimates the user's emotions using a generation AI. Emotion estimation is performed using, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, if the user is relaxed, the generation unit can generate a video that progresses at a leisurely pace. Also, if the user is in a hurry, the generation unit can generate a video that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a video that adds visually stimulating effects. This improves the viewing experience by adjusting the content of the video according to the user's emotions. For example, if the user is relaxed, the viewing experience is improved by generating a video that progresses at a leisurely pace.

[0113] During generation, the generation unit can improve the accuracy of generation by referring to the user's past viewing history. The generation unit uses a generation AI to analyze the user's past viewing history. The viewing history includes, but is not limited to, a list of viewed content, viewing time, and viewing frequency. For example, the generation unit can improve the accuracy of generation based on the genres and characters of dramas the user has previously watched. The generation unit can also predict preferred plot developments from the user's past viewing history and reflect this in the generation. Furthermore, the generation unit can optimize the generation results based on the user's ratings of videos they have previously watched. This improves the accuracy of generation by referring to the user's past viewing history. For example, the accuracy of generation can be improved based on the genres and characters of dramas the user has previously watched.

[0114] During generation, the generation unit can apply different generation methods depending on the category of the user's input content. The generation unit uses a generation AI to analyze the category of the user's input content. Examples of categories of input content include, but are not limited to, genres, themes, and topics. For example, if the user likes mysteries, the generation unit can apply a generation method specialized for mysteries. Also, if the user likes romance, the generation unit can apply a generation method specialized for romance. Furthermore, if the user likes action, the generation unit can apply a generation method specialized for action. In this way, by applying a generation method depending on the category of the user's input content, the accuracy of generation is improved. For example, if the user likes mysteries, the accuracy of generation is improved by applying a generation method specialized for mysteries.

[0115] The generation unit can optimize the generation algorithm by reflecting user feedback during generation. The generation unit uses the generation AI to analyze user feedback. Feedback includes, but is not limited to, user ratings, comments, and usage history. For example, the generation unit can adjust the generation algorithm based on feedback previously provided by the user. Furthermore, if the user is satisfied with a particular generation result, the generation unit can preferentially apply that method. Furthermore, if the user is dissatisfied with a particular generation result, the generation unit can adjust to avoid that method. In this way, the generation algorithm can be optimized by reflecting user feedback. For example, adjusting the generation algorithm based on feedback previously provided by the user improves generation accuracy.

[0116] The generation unit can estimate the user's emotions and adjust the length of the generated video based on the estimated user emotions. The generation unit uses a generation AI to estimate the user's emotions. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point video. Also, if the user is relaxed, the generation unit can generate a longer video with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a video with visually stimulating effects. This improves the viewing experience by adjusting the length of the video according to the user's emotions. For example, if the user is in a hurry, the viewing experience can be improved by generating a short, to-the-point video.

[0117] The generation unit can generate the game taking into account the user's geographical location information. The generation unit uses a generation AI to analyze the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP address, and location information services. For example, if the user is in a specific area, the generation unit can prioritize generating genres and characters related to that area. Also, if the user is traveling, the generation unit can generate a storyline related to the travel destination. Furthermore, if the user is participating in a specific event, the generation unit can generate a storyline related to that event. This improves the accuracy of generation by taking the user's geographical location information into account. For example, if the user is in a specific area, the generation unit can prioritize generating genres and characters related to that area, thereby improving the accuracy of generation.

[0118] The generation unit can improve the accuracy of generation by referring to the user's social media activity during generation. The generation unit uses a generation AI to analyze the user's social media activity. 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 can reflect the content posted by accounts the user follows on social media in the generation. The generation unit can also reflect content shared by the user on social media in the generation. Furthermore, the generation unit can improve the accuracy of generation by referring to the activity of the user's friends on social media. In this way, the accuracy of generation is improved by referring to the user's social media activity. For example, the accuracy of generation is improved by reflecting the content posted by accounts the user follows on social media in the generation.

[0119] The generation unit can generate the data by taking into account the user's market data. The generation unit uses a generation AI to analyze the user's market data. Market data includes, but is not limited to, sales data, customer data, and competitor data. For example, the generation unit can improve the accuracy of the generation based on market data in which the user is interested. The generation unit can also generate data based on products and services that the user has purchased in the past. Furthermore, the generation unit can analyze the user's market data and provide optimal generation results. In this way, the accuracy of the generation is improved by taking into account the user's market data. For example, the accuracy of the generation can be improved based on market data in which the user is interested.

[0120] The providing unit can estimate the user's emotions and adjust the display method of the video to be provided based on the estimated user's emotions. The providing unit estimates the user's emotions using a generation AI. Emotion estimation is performed using, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. This improves the viewing experience by adjusting the video display method according to the user's emotions. For example, if the user is nervous, the viewing experience is improved by providing a simple, highly visible display method.

[0121] The provision unit can select the optimal delivery method by referring to the user's past viewing history when providing content. The provision unit uses a generation AI to analyze the user's past viewing history. The viewing history includes, but is not limited to, a list of viewed content, viewing time, and viewing frequency. For example, the provision unit can select the optimal delivery method based on the genres and characters of dramas the user has previously viewed. The provision unit can also predict preferred plot developments from the user's past viewing history and reflect this in the provision. Furthermore, the provision unit can optimize the delivery method based on the user's ratings of videos they have previously viewed. This allows the optimal delivery method to be selected by referring to the user's past viewing history. For example, selecting the optimal delivery method based on the genres and characters of dramas the user has previously viewed improves the viewing experience.

[0122] The providing unit can customize the content provided based on the user's current interests and trends at the time of provision. The providing unit uses a generation AI to analyze the user's current interests and trends. Examples of interests and trends include, but are not limited to, social media trends, news articles, and search history. For example, the providing unit can provide related genres and characters based on topics recently searched by the user. The providing unit can also customize the content provided based on trends the user follows on social media. Furthermore, the providing unit can provide related storylines based on the content of videos recently viewed by the user. This improves the viewing experience by customizing the content provided based on the user's current interests and trends. For example, the viewing experience improves by providing related genres and characters based on topics recently searched by the user.

[0123] The providing unit can improve the delivery method by reflecting user feedback when delivering content. The providing unit uses a generation AI to analyze user feedback. The feedback includes, but is not limited to, user ratings, comments, and usage history. For example, the providing unit can adjust the delivery method based on feedback previously provided by the user. Furthermore, if the user is satisfied with a particular delivery method, the providing unit can preferentially apply that method. Furthermore, if the user is dissatisfied with a particular delivery method, the providing unit can adjust to avoid that method. In this way, the delivery method can be optimized by reflecting user feedback. For example, adjusting the delivery method based on feedback previously provided by the user improves the viewing experience.

[0124] The providing unit can estimate the user's emotions and determine the priority of videos to be provided based on the estimated user's emotions. The providing unit estimates the user's emotions using a generation AI. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, if the user is nervous, the providing unit can prioritize providing videos with relaxing content. Furthermore, if the user is relaxed, the providing unit can prioritize providing videos with interesting content. Furthermore, if the user is in a hurry, the providing unit can prioritize providing videos that can be viewed in a short time. This improves the viewing experience by prioritizing videos according to the user's emotions. For example, if the user is nervous, the viewing experience is improved by prioritized provision of videos with relaxing content.

[0125] The providing unit can select the optimal delivery method by taking into account the user's geographical location information when providing content. The providing unit uses a generation AI to analyze the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP address, and location information services. For example, if the user is in a specific area, the providing unit can prioritize providing genres and characters related to that area. Also, if the user is traveling, the providing unit can provide storylines related to the user's travel destination. Furthermore, if the user is participating in a specific event, the providing unit can provide content related to that event. This allows the optimal delivery method to be selected by taking into account the user's geographical location information. For example, if the user is in a specific area, the viewing experience can be improved by prioritized provision of genres and characters related to that area.

[0126] The providing unit can customize the provided content by analyzing the user's social media activity at the time of providing the content. The providing unit uses a generation AI to analyze the user's social media activity. 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 providing unit can reflect the content posted by accounts the user follows on social media in the provided content. The providing unit can also reflect content shared by the user on social media in the provided content. Furthermore, the providing unit can customize the provided content by taking into account the activity of the user's friends on social media. In this way, the provided content can be customized by analyzing the user's social media activity. For example, reflecting the content posted by accounts the user follows on social media in the provided content improves the viewing experience.

[0127] The providing unit can customize the delivery method by reflecting the user's past feedback when delivering content. The providing unit uses a generation AI to analyze the user's past feedback. The feedback includes, but is not limited to, the user's ratings, comments, and usage history. For example, the providing unit can prioritize delivery methods that the user has previously preferred. The providing unit can also adjust the delivery method to avoid delivery methods that the user has previously dissatisfied with. Furthermore, the providing unit can optimize the layout of the delivery interface based on the user's past feedback. This allows the delivery method to be optimized by reflecting the user's past feedback. For example, the viewing experience can be improved by prioritizing delivery methods that the user has previously preferred.

[0128] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. The learning unit estimates the user's emotions using a generative AI. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, the learning unit can select detailed training data when the user is relaxed. Furthermore, the learning unit can select data that can be learned quickly when the user is in a hurry. Furthermore, the learning unit can select visually stimulating training data when the user is excited. In this way, by selecting training data according to the user's emotions, the accuracy of learning is improved. For example, when the user is relaxed, the accuracy of learning is improved by selecting detailed training data.

[0129] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit analyzes past learning data using a generative AI. The learning data includes, but is not limited to, past viewing history, user feedback, and content metadata. For example, the learning unit can select an optimal learning algorithm based on the past learning data. The learning unit can also extract effective learning methods from the past learning data and reflect them in the algorithm. Furthermore, the learning unit can analyze the past learning data to improve the accuracy of the learning algorithm. In this way, the learning algorithm can be optimized by referring to the past learning data. For example, selecting an optimal learning algorithm based on the past learning data improves the accuracy of learning.

[0130] During learning, the learning unit can analyze fluctuations in the user's viewing history and adjust the update frequency of the learning data. The learning unit uses the generation AI to analyze fluctuations in the user's viewing history. Fluctuations in viewing history include, but are not limited to, changes in viewing frequency and changes in viewing content. For example, if the user's viewing history changes frequently, the learning unit can increase the update frequency of the learning data. Also, if the user's viewing history is stable, the learning unit can decrease the update frequency of the learning data. Furthermore, the learning unit can analyze the change pattern of the user's viewing history and set an optimal update frequency. In this way, the update frequency of the learning data can be optimized by analyzing fluctuations in the user's viewing history. For example, if the user's viewing history changes frequently, increasing the update frequency of the learning data improves the accuracy of learning.

[0131] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. The learning unit estimates the user's emotions using a generative AI. Emotion estimation is performed using, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, the learning unit can increase the frequency of learning when the user is relaxed. The learning unit can also decrease the frequency of learning when the user is in a hurry. Furthermore, the learning unit can adjust the frequency of learning when the user is excited. In this way, adjusting the frequency of learning according to the user's emotions improves learning efficiency. For example, when the user is relaxed, increasing the frequency of learning improves learning accuracy.

[0132] During learning, the learning unit can weight the learning data based on the time when the viewing history was submitted. The learning unit uses a generation AI to analyze the time when the viewing history was submitted. The time when the viewing history was submitted includes, but is not limited to, the timing and frequency of the viewing history submission. For example, the learning unit can weight the learning data based on recent viewing history. The learning unit can also weight the learning data based on past viewing history. Furthermore, the learning unit can adjust the weighting of the learning data depending on the time when the viewing history was submitted. Thus, weighting the learning data based on the time when the viewing history was submitted improves the accuracy of learning. For example, weighting the learning data based on recent viewing history improves the accuracy of learning.

[0133] The learning unit can adjust the learning algorithm by reflecting user feedback during learning. The learning unit uses a generative AI to analyze user feedback. Feedback includes, but is not limited to, user ratings, comments, and usage history. For example, the learning unit can adjust the learning algorithm based on feedback previously provided by the user. Furthermore, if the user is satisfied with a particular learning result, the learning unit can preferentially apply that method. Furthermore, if the user is dissatisfied with a particular learning result, the learning unit can adjust to avoid that method. In this way, the learning algorithm can be optimized by reflecting user feedback. For example, adjusting the learning algorithm based on feedback previously provided by the user improves learning accuracy.

[0134] The scenario generation unit can estimate the user's emotions and adjust the content of the scenario based on the estimated user's emotions. The scenario generation unit estimates the user's emotions using a generation AI. Emotion estimation is performed using, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, if the user is relaxed, the scenario generation unit can generate a scenario that progresses at a leisurely pace. Furthermore, if the user is in a hurry, the scenario generation unit can generate a scenario that emphasizes tense developments. Furthermore, if the user is excited, the scenario generation unit can generate a scenario that adds visually stimulating effects. In this way, the viewing experience is improved by adjusting the content of the scenario according to the user's emotions. For example, if the user is relaxed, the viewing experience is improved by generating a scenario that progresses at a leisurely pace.

[0135] When generating a scenario, the scenario generation unit can improve the accuracy of the scenario by referring to the user's past viewing history. The scenario generation unit uses a generation AI to analyze the user's past viewing history. The viewing history includes, for example, a list of viewed content, viewing time, viewing frequency, etc., but is not limited to these examples. For example, the scenario generation unit can improve the accuracy of the scenario based on the genres and characters of dramas the user has watched in the past. The scenario generation unit can also predict the user's preferred plot development from the user's past viewing history and reflect this in the scenario. Furthermore, the scenario generation unit can optimize the scenario based on the user's evaluation of videos they have watched in the past. In this way, the accuracy of the scenario can be improved by referring to the user's past viewing history. For example, the accuracy of the scenario can be improved based on the genres and characters of dramas the user has watched in the past.

[0136] When generating a scenario, the scenario generation unit can apply different scenario generation methods depending on the category of the user's input content. The scenario generation unit uses a generation AI to analyze the category of the user's input content. The category of the input content includes, but is not limited to, genre, theme, topic, etc. For example, if the user likes mysteries, the scenario generation unit can apply a scenario generation method specialized for mysteries. Furthermore, if the user likes romance, the scenario generation unit can apply a scenario generation method specialized for romance. Furthermore, if the user likes action, the scenario generation unit can apply a scenario generation method specialized for action. In this way, by applying a scenario generation method depending on the category of the user's input content, the accuracy of the scenario is improved. For example, if the user likes mysteries, the accuracy of the scenario is improved by applying a scenario generation method specialized for mysteries.

[0137] The scenario generation unit can optimize the scenario generation algorithm by reflecting user feedback when generating a scenario. The scenario generation unit uses a generation AI to analyze user feedback. The feedback includes, but is not limited to, user ratings, comments, and usage history. For example, the scenario generation unit can adjust the scenario generation algorithm based on feedback previously provided by the user. Furthermore, if the user is satisfied with a particular scenario, the scenario generation unit can preferentially apply that method. Furthermore, if the user is dissatisfied with a particular scenario, the scenario generation unit can adjust to avoid that method. In this way, the scenario generation algorithm can be optimized by reflecting user feedback. For example, adjusting the scenario generation algorithm based on feedback previously provided by the user improves the accuracy of the scenario.

[0138] The scenario generation unit can estimate the user's emotions and adjust the length of the scenario based on the estimated user emotions. The scenario generation unit estimates the user's emotions using a generation AI. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, if the user is in a hurry, the scenario generation unit can generate a short, to-the-point scenario. Also, if the user is relaxed, the scenario generation unit can generate a longer scenario with detailed explanations. Furthermore, if the user is excited, the scenario generation unit can generate a scenario with visually stimulating effects. This improves the viewing experience by adjusting the length of the scenario according to the user's emotions. For example, if the user is in a hurry, generating a short, to-the-point scenario improves the viewing experience.

[0139] The scenario generation unit can generate a scenario taking into account the user's geographical location information when generating a scenario. The scenario generation unit uses a generation AI to analyze the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, location information services, etc. For example, if the user is in a specific area, the scenario generation unit can prioritize incorporating genres and characters related to that area into the scenario. Also, if the user is traveling, the scenario generation unit can incorporate a storyline related to the user's travel destination into the scenario. Furthermore, if the user is participating in a specific event, the scenario generation unit can generate a scenario related to that event. In this way, by taking the user's geographical location information into consideration, the accuracy of the scenario is improved. For example, if the user is in a specific area, the accuracy of the scenario is improved by prioritized incorporation of genres and characters related to that area into the scenario.

[0140] The scenario generation unit can improve the accuracy of the scenario by referring to the user's social media activity when generating a scenario. The scenario generation unit uses a generation AI to analyze the user's social media activity. 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 scenario generation unit can reflect the content posted by accounts the user follows on social media in the scenario. The scenario generation unit can also reflect content shared by the user on social media in the scenario. Furthermore, the scenario generation unit can improve the accuracy of the scenario by referring to the activity of the user's friends on social media. In this way, the accuracy of the scenario is improved by referring to the user's social media activity. For example, the accuracy of the scenario is improved by reflecting the content posted by accounts the user follows on social media in the scenario.

[0141] The scenario generation unit can generate a scenario by taking into account the user's market data. The scenario generation unit uses a generation AI to analyze the user's market data. Market data includes, but is not limited to, sales data, customer data, and competitor data. For example, the scenario generation unit can improve the accuracy of the scenario based on market data in which the user is interested. The scenario generation unit can also generate a scenario based on products or services that the user has purchased in the past. Furthermore, the scenario generation unit can analyze the user's market data and provide an optimal scenario. In this way, the accuracy of the scenario is improved by taking the user's market data into consideration. For example, the accuracy of the scenario can be improved based on market data in which the user is interested.

[0142] The character setting unit can estimate the user's emotions and adjust the character settings based on the estimated user emotions. The character setting unit estimates the user's emotions using a generation AI. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, if the user is relaxed, the character setting unit can set a character with a calm personality. Also, if the user is in a hurry, the character setting unit can set a character with an active personality. Furthermore, if the user is excited, the character setting unit can set a character with a visually stimulating design. This improves the viewing experience by adjusting the character settings according to the user's emotions. For example, if the user is relaxed, setting a character with a calm personality improves the viewing experience.

[0143] When setting a character, the character setting unit can improve the accuracy of the character setting by referring to the user's past viewing history. The character setting unit uses a generation AI to analyze the user's past viewing history. The viewing history includes, but is not limited to, a list of viewed content, viewing time, and viewing frequency. For example, the character setting unit can improve the accuracy of the character setting based on characters from dramas the user has watched in the past. The character setting unit can also predict a preferred character setting from the user's past viewing history and reflect this in the setting. Furthermore, the character setting unit can optimize the character setting based on the user's evaluation of videos they have watched in the past. In this way, the accuracy of the character setting is improved by referring to the user's past viewing history. For example, the accuracy of the character setting can be improved based on characters from dramas the user has watched in the past.

[0144] When setting a character, the character setting unit can apply different character setting methods depending on the category of the user's input content. The character setting unit uses a generation AI to analyze the category of the user's input content. Examples of categories of input content include, but are not limited to, genre, theme, and topic. For example, if the user likes mysteries, the character setting unit can apply a character setting method specialized for mysteries. Furthermore, if the user likes romance, the character setting unit can apply a character setting method specialized for romance. Furthermore, if the user likes action, the character setting unit can apply a character setting method specialized for action. In this way, applying a character setting method depending on the category of the user's input content improves the accuracy of character setting. For example, if the user likes mysteries, applying a character setting method specialized for mysteries improves the accuracy of character setting.

[0145] The character setting unit can optimize the character setting algorithm by reflecting user feedback when setting a character. The character setting unit uses a generation AI to analyze the user's feedback. The feedback includes, but is not limited to, user ratings, comments, and usage history. For example, the character setting unit can adjust the character setting algorithm based on feedback previously provided by the user. Furthermore, if the user is satisfied with a particular character setting, the character setting unit can preferentially apply that method. Furthermore, if the user is dissatisfied with a particular character setting, the character setting unit can adjust to avoid that method. In this way, the character setting algorithm can be optimized by reflecting user feedback. For example, adjusting the character setting algorithm based on feedback previously provided by the user improves the accuracy of character setting.

[0146] The character setting unit can estimate the user's emotions and adjust the level of detail of the character setting based on the estimated user's emotions. The character setting unit estimates the user's emotions using a generation AI. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, the character setting unit can provide a detailed character setting when the user is relaxed. Also, the character setting unit can provide a concise character setting when the user is in a hurry. Furthermore, the character setting unit can provide a visually stimulating character setting when the user is excited. This improves the viewing experience by adjusting the level of detail of the character setting according to the user's emotions. For example, when the user is relaxed, providing a detailed character setting improves the viewing experience.

[0147] The character setting unit can set the character taking into account the user's geographical location information when setting the character. The character setting unit uses a generation AI to analyze the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, location information services, etc. For example, if the user is in a specific area, the character setting unit can set a character related to that area. Also, if the user is traveling, the character setting unit can set a character related to the travel destination. Furthermore, if the user is participating in a specific event, the character setting unit can set a character related to that event. This improves the accuracy of character setting by taking the user's geographical location information into account. For example, if the user is in a specific area, setting a character related to that area improves the accuracy of character setting.

[0148] The character setting unit can improve the accuracy of the character setting by referring to the user's social media activity when setting the character. The character setting unit uses a generation AI to analyze the user's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the character setting unit can reflect the content posted by accounts the user follows on social media in the character setting. The character setting unit can also reflect content shared by the user on social media in the character setting. Furthermore, the character setting unit can improve the accuracy of the character setting by referring to the activity of the user's friends on social media. In this way, the accuracy of the character setting is improved by referring to the user's social media activity. For example, the accuracy of the character setting is improved by reflecting the content posted by accounts the user follows on social media in the character setting.

[0149] The character setting unit can set the character by taking into account the user's market data. The character setting unit uses a generation AI to analyze the user's market data. Market data includes, but is not limited to, sales data, customer data, and competitor data. For example, the character setting unit can improve the accuracy of the character setting based on market data that the user is interested in. The character setting unit can also set the character based on products and services that the user has purchased in the past. Furthermore, the character setting unit can analyze the user's market data and provide an optimal character setting. In this way, the accuracy of the character setting is improved by taking the user's market data into consideration. For example, the accuracy of the character setting can be improved based on market data that the user is interested in.

[0150] The editing department can estimate the user's emotions and adjust the editing content based on the estimated user's emotions. The editing department estimates the user's emotions using a generative AI. Emotion estimation is performed using, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, if the user is relaxed, the editing department can edit the content to progress at a leisurely pace. Also, if the user is in a hurry, the editing department can edit the content to emphasize tense developments. Furthermore, if the user is excited, the editing department can edit the content to add visually stimulating effects. In this way, the viewing experience is improved by adjusting the editing content according to the user's emotions. For example, if the user is relaxed, the viewing experience is improved by editing the content to progress at a leisurely pace.

[0151] During editing, the editing department can improve the accuracy of editing by referring to the user's past viewing history. The editing department uses a generative AI to analyze the user's past viewing history. The viewing history includes, but is not limited to, a list of viewed content, viewing time, and viewing frequency. For example, the editing department can improve the accuracy of editing based on the genres and characters of dramas the user has previously watched. The editing department can also predict preferred plot developments from the user's past viewing history and reflect this in the editing. Furthermore, the editing department can optimize the editing results based on the user's ratings of videos they have previously watched. This improves the accuracy of editing by referring to the user's past viewing history. For example, the editing department can improve the accuracy of editing based on the genres and characters of dramas the user has previously watched.

[0152] During editing, the editing department can apply different editing techniques depending on the category of the user's input content. The editing department uses a generation AI to analyze the category of the user's input content. The category of the input content includes, but is not limited to, genre, theme, topic, etc. For example, if the user likes mysteries, the editing department can apply an editing technique specialized for mysteries. Furthermore, if the user likes romance, the editing department can apply an editing technique specialized for romance. Furthermore, if the user likes action, the editing department can apply an editing technique specialized for action. In this way, by applying an editing technique depending on the category of the user's input content, the accuracy of editing is improved. For example, if the user likes mysteries, the accuracy of editing is improved by applying an editing technique specialized for mysteries.

[0153] The editorial department can optimize the editing algorithm by reflecting user feedback during editing. The editorial department uses a generative AI to analyze user feedback. The feedback includes, but is not limited to, user ratings, comments, and usage history. For example, the editorial department can adjust the editing algorithm based on feedback previously provided by the user. Furthermore, if the user is satisfied with a particular editing result, the editorial department can preferentially apply that technique. Furthermore, if the user is dissatisfied with a particular editing result, the editorial department can adjust to avoid that technique. In this way, the editing algorithm can be optimized by reflecting user feedback. For example, adjusting the editing algorithm based on feedback previously provided by the user improves editing accuracy.

[0154] The editing department can estimate the user's emotions and determine editing priorities based on the estimated user emotions. The editing department estimates the user's emotions using a generation AI. Emotion estimation is performed using, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. For example, if the user is nervous, the editing department can prioritize editing content that will help the user relax. Also, if the user is relaxed, the editing department can prioritize editing content that is interesting. Furthermore, if the user is in a hurry, the editing department can prioritize editing content that can be viewed in a short amount of time. In this way, the viewing experience is improved by determining editing priorities according to the user's emotions. For example, if the user is nervous, the viewing experience is improved by prioritizing editing content that will help the user relax.

[0155] The editorial department can take the user's geographic location information into consideration when editing. The editorial department uses a generative AI to analyze the user's geographic location information. Examples of geographic location information include, but are not limited to, GPS data, IP addresses, and location information services. For example, if the user is in a specific area, the editorial department can prioritize editing genres and characters related to that area. Also, if the user is traveling, the editorial department can edit storylines related to the user's travel destination. Furthermore, if the user is participating in a specific event, the editorial department can edit related to that event. This improves the accuracy of editing by taking the user's geographic location information into consideration. For example, if the user is in a specific area, the editorial department can prioritize editing genres and characters related to that area, improving the accuracy of editing.

[0156] The editorial department can improve the accuracy of editing by referring to the user's social media activity when editing. The editorial department uses generative AI to analyze the user's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the editorial department can reflect the content posted by accounts the user follows on social media in the edits. The editorial department can also reflect content shared by the user on social media in the edits. Furthermore, the editorial department can improve the accuracy of editing by referring to the activity of the user's friends on social media. In this way, the accuracy of editing is improved by referring to the user's social media activity. For example, the accuracy of editing is improved by reflecting the content posted by accounts the user follows on social media in the edits.

[0157] The editorial department can take the user's market data into consideration when editing. The editorial department uses a generative AI to analyze the user's market data. Market data includes, but is not limited to, sales data, customer data, and competitor data. For example, the editorial department can improve the accuracy of editing based on market data in which the user is interested. The editorial department can also edit based on products and services the user has purchased in the past. Furthermore, the editorial department can analyze the user's market data and provide optimal editing results. In this way, the accuracy of editing is improved by taking the user's market data into consideration. For example, the accuracy of editing can be improved based on market data in which the user is interested. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned input unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit can input user preference information using the reception device 38 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a drama video based on the analyzed information. The provision unit can provide the generated video to the user using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned input unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit can input user preference information using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a drama video based on the analyzed information. The provision unit can provide the generated video to the user using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the input unit can input user preference information using the microphone 238 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a drama video based on the analyzed information. The provision unit can provide the generated video to the user using the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit can input user preference information using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a drama video based on the analyzed information. The provision unit can provide the generated video to the user using the speaker 240 of the robot 414.

[0158] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0159] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user's emotions. For example, if the user is relaxed, a detailed analysis can be performed to provide more options. If the user is in a hurry, a quick analysis can be performed to narrow down the optimal options. Furthermore, if the user is excited, a visually stimulating analysis result can be provided. This improves the accuracy of the analysis by adjusting the analysis algorithm according to the user's emotions. For example, if the user is relaxed, a detailed analysis can be performed to provide more options.

[0160] The providing unit can estimate the user's emotions and adjust the display method of the video to be provided based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. Also, if the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, the viewing experience is improved by adjusting the video display method according to the user's emotions. For example, if the user is nervous, a simple and highly visible display method can be provided, thereby improving the viewing experience.

[0161] The input unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user's emotions. For example, if the user is nervous, an interface with calm colors can be provided to reduce visual stress. If the user is having fun, an interface with bright colors can be provided to make input work more enjoyable. Furthermore, if the user is tired, an interface with simple and high visibility can be provided to make input work easier. In this way, adjusting the design of the input interface according to the user's emotions improves the user's input experience. For example, if the user is nervous, an interface with calm colors can be provided to reduce visual stress.

[0162] The generation unit can estimate the user's emotions and adjust the content of the video to be generated based on the estimated user's emotions. For example, if the user is relaxed, a video that progresses at a leisurely pace can be generated. If the user is in a hurry, a video that emphasizes the shortest route can be generated. Furthermore, if the user is excited, a video that adds visually stimulating effects can be generated. In this way, the viewing experience is improved by adjusting the content of the video according to the user's emotions. For example, if the user is relaxed, the viewing experience is improved by generating a video that progresses at a leisurely pace.

[0163] The character setting unit can estimate the user's emotions and adjust the character setting based on the estimated user's emotions. For example, if the user is relaxed, a character with a calm personality can be set. If the user is in a hurry, a character with an active personality can be set. Furthermore, if the user is excited, a character with a visually stimulating design can be set. In this way, the viewing experience is improved by adjusting the character setting according to the user's emotions. For example, if the user is relaxed, a character with a calm personality can be set.

[0164] The input unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display as candidates genres and characters that the user has frequently input in the past. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest genres and characters that will be used in a specific time period based on the user's past input history. In this way, by analyzing the past input history, it is possible to suggest the optimal input method to the user. For example, by automatically displaying as candidates genres and characters that the user has frequently input in the past, input efficiency is improved.

[0165] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past viewing history. For example, the analysis unit can improve the accuracy of the analysis based on the genres and characters of dramas the user has watched in the past. The analysis unit can also predict the user's preferred plot developments from the user's past viewing history and reflect this in the analysis. Furthermore, the analysis unit can optimize the analysis results based on the user's ratings of videos they have watched in the past. In this way, the accuracy of the analysis can be improved by referring to the user's past viewing history. For example, the accuracy of the analysis can be improved based on the genres and characters of dramas the user has watched in the past.

[0166] When generating a scenario, the scenario generation unit can improve the accuracy of the scenario by referring to the user's past viewing history. For example, the scenario generation unit can improve the accuracy of the scenario based on the genres and characters of dramas that the user has watched in the past. The scenario generation unit can also predict the user's preferred plot development from the user's past viewing history and reflect this in the scenario. Furthermore, the scenario generation unit can optimize the scenario based on the user's evaluations of videos that the user has watched in the past. In this way, the accuracy of the scenario can be improved by referring to the user's past viewing history. For example, the accuracy of the scenario can be improved based on the genres and characters of dramas that the user has watched in the past.

[0167] When setting a character, the character setting unit can improve the accuracy of the character setting by referring to the user's past viewing history. For example, the character setting unit can improve the accuracy of the character setting based on characters in dramas that the user has watched in the past. The character setting unit can also predict a preferred character setting from the user's past viewing history and reflect this in the setting. Furthermore, the character setting unit can optimize the character setting based on ratings of videos that the user has watched in the past. In this way, the accuracy of the character setting is improved by referring to the user's past viewing history. For example, the accuracy of the character setting can be improved based on characters in dramas that the user has watched in the past.

[0168] When editing, the editing department can improve the accuracy of editing by referring to the user's past viewing history. For example, the editing department can improve the accuracy of editing based on the genres and characters of dramas that the user has watched in the past. The editing department can also predict the user's preferred plot development from the user's past viewing history and reflect this in the editing. Furthermore, the editing department can optimize the editing results based on the user's ratings of videos that they have watched in the past. In this way, the accuracy of editing can be improved by referring to the user's past viewing history. For example, the accuracy of editing can be improved based on the genres and characters of dramas that the user has watched in the past.

[0169] The processing flow of the second embodiment will be briefly explained below.

[0170] Step 1: The input unit inputs user preference information. User preference information includes genre, characters, storyline, etc. For example, a user can input preferences such as "mystery," "strong female protagonist," and "complex plot." Step 2: The analysis unit uses the generation AI to analyze the information entered by the input unit. The analysis is performed using natural language processing and machine learning algorithms. For example, the generation AI generates an appropriate scenario, character settings, and plot development based on the user's preferences. Step 3: The generation unit uses generation AI to create scenarios and character settings based on the information analyzed by the analysis unit. The generation unit includes a scenario generation unit and a character setting unit, and the scenario generation unit automatically generates plots and uses scenario templates. The character setting unit sets the character's personality, background, relationships, etc. Step 4: The providing unit provides the video generated by the generating unit. The providing unit includes an editing unit that edits and modifies the generated video, such as by cutting, adding effects, and adjusting the audio.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0175] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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 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.

[0189] 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.

[0190] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0191] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0192] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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).

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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 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.

[0205] 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.

[0206] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0207] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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 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.

[0222] 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.

[0223] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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).

[0228] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0229] 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."

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] [Explanation of symbols]

[0243] 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. an input section for inputting user preference information; an analysis unit that analyzes the information input by the input unit; a generation unit that sets a scenario or a character based on the information analyzed by the analysis unit; a providing unit that provides the video generated by the generating unit. A system characterized by:

2. The analysis unit Includes a learning module that learns user preferences 2. The system of claim 1.

3. The generation unit a scenario generation unit that generates a scenario; Includes a character setting section that sets up characters 2. The system of claim 1.

4. The providing unit Includes an editorial department that edits and modifies the generated videos.

2. The system of claim 1.

5. The input unit Users input information about their preferred genres, characters, and storylines 2. The system of claim 1.

6. The generation unit Generate scenarios, character settings, and plot developments based on user preferences 2. The system of claim 1.

7. The input unit The system estimates the user's emotions and adjusts the display method of the input interface based on the estimated user emotions.

2. The system of claim 1.

8. The input unit Analyzes the user's past input history and suggests input methods 2. The system of claim 1.

Images