system

Abstract

The system according to this embodiment aims to customize famous lines and scenes from movies, anime, and manga for use in everyday communication and on social media. [Solution] The system according to the embodiment comprises a selection unit, a customization unit, and a provision unit. The selection unit allows the user to select famous lines or scenes from movies, anime, and manga from a database. The customization unit uses a generating AI to customize the lines or scenes selected by the selection unit. The provision unit allows the user to use the content generated by the customization unit in their daily communication and on social media.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to effectively utilize the famous lines and scenes of movies, animations, and comics in daily communication and social media.

[0005] The system according to the embodiment aims to customize the famous lines and scenes of movies, animations, and comics for use in daily communication and social media.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a selection unit, a customization unit, and a provision unit. The selection unit allows the user to select famous lines or scenes from movies, anime, and manga from a database. The customization unit uses a generating AI to customize the lines or scenes selected by the selection unit. The provision unit allows the user to use the content generated by the customization unit in their daily communication and on social media. [Effects of the Invention]

[0007] The system according to this embodiment can customize famous lines and scenes from movies, anime, and manga for use in everyday communication and on social media. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Dialogue Master Agent System according to an embodiment of the present invention is a system that databases famous lines and scenes from movies, anime, and manga, and uses a generating AI to generate customized content that users can use in their daily communication and on social media. The Dialogue Master Agent System allows users to select famous lines and scenes from movies, anime, and manga from the database, and the generating AI customizes the selected lines and scenes based on the user's input. For example, users can change lines to suit specific situations or replace lines from specific characters with those of other characters. The generating AI utilizes natural language processing and image recognition technology to generate dynamic content based on the user's input. The generated content can be used by users in their daily communication and on social media. This allows users to enjoy unique and engaging communication. For example, users can post lines tailored to specific situations on social media or use customized lines in chats with friends. This system addresses the needs of entertainment fans who want to incorporate parts of movies and anime into their daily lives, and can meet the needs of social media users who seek unique communication. Furthermore, it proposes new ways of using entertainment content and realizes an improved user experience through the use of AI technology. This allows the dialogue master agent system to make user communication more engaging and enjoyable.

[0029] The dialogue master agent system according to this embodiment comprises a selection unit, a customization unit, and a provision unit. The selection unit allows the user to select famous lines or scenes from movies, anime, and manga from a database. For example, the selection unit can allow the user to select famous lines or scenes from movies, anime, and manga of a specific genre or era. The selection unit can also use AI to analyze the user's preferences and past selection history and suggest the optimal choice. The customization unit uses generation AI to customize the lines or scenes selected by the selection unit. For example, the customization unit can change the lines to suit a specific situation based on user input. The customization unit can also replace the lines of a specific character with those of another character. The generation AI uses natural language processing technology to generate dynamic content based on user input. The provision unit provides the content generated by the customization unit for the user to use in daily communication and on social media. For example, the provision unit can send the generated content to the user's smartphone or personal computer. The provision unit can also directly post the generated content to social media platforms. As a result, the dialogue master agent system according to the embodiment can make user communication more engaging and enjoyable.

[0030] The selection function allows users to choose famous lines and scenes from movies, anime, and manga from a database. For example, users can select famous lines and scenes from movies, anime, and manga of a specific genre or era. Specifically, the selection function accesses a vast database and provides an interface for users to search for famous lines and scenes that may interest them. Users can easily find lines related to specific characters or situations using keyword search and filtering functions. Furthermore, the selection function can use AI to analyze user preferences and past selection history to suggest optimal choices. For example, it can learn patterns from previously selected lines and scenes and prioritize displaying similar lines and scenes. The selection function can also analyze user input in real time to suggest lines that match the user's current mood and situation. This allows the selection function to quickly and accurately provide the lines and scenes users are looking for, improving the user experience.

[0031] The customization section uses a generative AI to customize the lines and scenes selected by the selection section. For example, the customization section can change lines to suit specific situations based on user input. Specifically, the generative AI dynamically generates lines using natural language processing technology based on the situation and character information entered by the user. For example, if the user inputs "I want to use this in a situation where I'm encouraging a friend," the generative AI will generate encouraging lines appropriate for that situation. The customization section can also replace the lines of a specific character with those of another character. For example, it can change famous lines from movies to be spoken by anime characters. The generative AI learns the characteristics and speaking styles of characters, enabling it to generate lines in a natural way. Furthermore, the customization section can fine-tune the generated lines based on user feedback to provide content that better matches the user's intentions. In this way, the customization section can achieve flexible content generation that meets user needs and increase user satisfaction.

[0032] The service provider delivers content generated by the customization service provider for users to use in their daily communications and on social media. Specifically, the service provider can send the generated content to the user's smartphone or personal computer. For example, it can send generated dialogue or scenes as text messages or images to the user's device for easy access. The service provider can also directly post the generated content to social media platforms. For example, if a user wants to share generated dialogue on social media, the service provider can automatically post the dialogue and share it with the user's followers. Furthermore, the service provider has the functionality to finely adjust the sharing scope and visibility settings of content to protect user privacy. This allows the service provider to make generated content safe and convenient for users, making daily communications more engaging and enjoyable.

[0033] The generation unit generates dynamic content using a generation AI. The generation unit can, for example, provide content that is generated in real time. The generation unit can also generate content in response to user input. For example, the generation unit can use the generation AI to generate new content based on text and images entered by the user. The generation unit can use the generation AI to provide content that meets the user's needs. This allows the generation unit to provide dynamic content that meets the user's needs. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user input into the generation AI, and the generation AI can generate dynamic content.

[0034] The modification unit changes the dialogue to suit a specific situation based on user input. For example, the modification unit can change the dialogue based on a situation entered by the user. The modification unit can use a generative AI to change the dialogue based on user input. For example, the modification unit changes the content of the dialogue to suit a situation entered by the user. The modification unit can use a generative AI to change the dialogue based on user input. This allows the modification unit to provide more appropriate content by changing the dialogue based on user input. Some or all of the above processing in the modification unit may be performed using a generative AI or not. For example, the modification unit can input user input into a generative AI, and the generative AI can change the dialogue.

[0035] The replacement unit replaces the lines of a specific character with those of another character. For example, the replacement unit can replace the lines of a character selected by the user with those of another character. The replacement unit can use a generative AI to replace the lines of a specific character with those of another character. For example, the replacement unit replaces the lines of a character selected by the user with those of another character. The replacement unit can use a generative AI to replace the lines of a specific character with those of another character. This allows the replacement unit to provide content tailored to the user's preferences by replacing character lines. Some or all of the above-described processing in the replacement unit may be performed using a generative AI or not. For example, the replacement unit can input the lines of a character selected by the user into a generative AI, which can then replace them with the lines of another character.

[0036] The service provider enables users to use the generated content on social media. For example, the service provider can directly post the generated content to the user's social media account. The service provider can also send the generated content to the user's smartphone or personal computer. The service provider can directly post the generated content to social media platforms. This allows the service provider to enable users to enjoy personalized communication by using the generated content on social media. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the generated content into an AI, which can then post it to a social media platform.

[0037] The selection unit can analyze the user's past selection history and suggest the most suitable options. For example, it can analyze the trends in the lines and scenes the user has previously selected and suggest similar options. If the user likes a particular character, the selection unit can also prioritize presenting lines and scenes related to that character. If the user likes a particular genre, the selection unit can also suggest lines and scenes related to that genre. In this way, the selection unit can suggest more appropriate options by analyzing the user's past selection history. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's past selection history into AI, which can then suggest the most suitable options.

[0038] The selection function can filter famous lines and scenes based on the user's current interests and trends. For example, the selection function can prioritize lines and scenes related to topics the user has recently been interested in. The selection function can also suggest lines and scenes from currently trending or popular movies and anime. The selection function can also present lines and scenes introduced by influencers the user follows. In this way, the selection function can provide more relevant content by filtering based on the user's current interests and trends. Some or all of the above processing in the selection function may be performed using AI or not. For example, the selection function can input the user's current interests and trend information into AI, which can then perform the filtering.

[0039] The selection unit can prioritize and present highly relevant options when selecting famous lines or scenes, taking into account the user's geographical location. For example, if the user is in a specific region, the selection unit can present lines or scenes related to that region. If the user is traveling, the selection unit can also suggest lines or scenes related to their travel destination. If the user is participating in a specific event, the selection unit can also present lines or scenes related to that event. In this way, the selection unit can provide more relevant content by taking the user's geographical location into consideration. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's geographical location information into AI, which can then present highly relevant options.

[0040] The selection unit can analyze the user's social media activity and suggest relevant options when selecting famous lines or scenes. For example, the selection unit can suggest lines or scenes related to content the user has recently posted. The selection unit can also suggest lines or scenes based on the content of posts from accounts the user follows. The selection unit can also suggest lines or scenes related to topics in groups or communities the user participates in. In this way, the selection unit can provide more appropriate content by analyzing the user's social media activity. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's social media activity data into AI, which can then suggest relevant options.

[0041] The customization unit can adjust the level of detail of customization based on the importance of the lines and scenes during the customization process. For example, the customization unit can perform detailed customization for important lines and scenes. For lines and scenes that are not very important, the customization unit can perform simplified customization. The customization unit can also add special effects to lines and scenes that the user particularly likes. In this way, the customization unit can provide more appropriate content by adjusting the level of detail of customization based on the importance of the lines and scenes. Some or all of the above processing in the customization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the customization unit can input importance data of lines and scenes into a generative AI, and the generative AI can adjust the level of detail of the customization.

[0042] The customization unit can apply different customization algorithms depending on the category of the dialogue and scene during customization. For example, for dialogue and scenes in the comedy category, the customization unit can perform customizations that emphasize humor. For dialogue and scenes in the drama category, the customization unit can also perform customizations that emphasize emotion. For dialogue and scenes in the action category, the customization unit can also perform customizations that emphasize movement. In this way, the customization unit can provide more appropriate content by applying different customization algorithms depending on the category of the dialogue and scene. Some or all of the above processing in the customization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the customization unit can input the category data of the dialogue and scene into a generative AI, and the generative AI can apply different customization algorithms.

[0043] The customization unit can determine the priority of customization based on the timing of dialogue and scene selections during the customization process. For example, the customization unit can prioritize customizing dialogue and scenes recently selected by the user. The customization unit can also prioritize customizing dialogue and scenes selected by the user in conjunction with a specific event. The customization unit can also prioritize customizing dialogue and scenes selected by the user during a specific time period. This allows the customization unit to provide more appropriate content by determining the priority of customization based on the timing of dialogue and scene selections. Some or all of the above processing in the customization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the customization unit can input dialogue and scene selection timing data into a generative AI, which can then determine the priority of customization.

[0044] The customization unit can adjust the order of customization based on the relevance of lines and scenes during the customization process. For example, the customization unit may customize highly relevant lines and scenes first. It can also postpone the customization of less relevant lines and scenes. The customization unit can also prioritize customizing lines and scenes that the user particularly likes. In this way, the customization unit can provide more appropriate content by adjusting the order of customization based on the relevance of lines and scenes. Some or all of the above processing in the customization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the customization unit can input relevance data of lines and scenes into a generative AI, and the generative AI can adjust the order of customization.

[0045] The delivery unit can analyze the user's past usage history to select the optimal delivery method at the time of delivery. For example, the delivery unit may prioritize the delivery method for content that the user has previously preferred to use. The delivery unit may also prioritize the delivery method for content that the user used during a specific time period. The delivery unit may also prioritize the delivery method for content that the user used in conjunction with a specific event. This allows the delivery unit to select a more appropriate delivery method by analyzing the user's past usage history. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's past usage history data into AI, which can then select the optimal delivery method.

[0046] The service provider can adjust the timing of service delivery based on the user's current lifestyle. For example, if the user is busy, the service provider can provide content that can be used in a short amount of time. If the user is relaxed, the service provider can also provide content that can be enjoyed for a longer period of time. If the user is participating in a specific event, the service provider can also provide content related to that event. This allows the service provider to provide more appropriate content by adjusting the timing of service delivery based on the user's current lifestyle. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input data on the user's current lifestyle into the AI, which can then adjust the timing of service delivery.

[0047] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can provide content related to that region. If the user is traveling, the service provider can also provide content related to the travel destination. If the user is participating in a specific event, the service provider can also provide content related to that event. This allows the service provider to select a more appropriate delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location data into AI, which can then select the optimal delivery method.

[0048] The service provider can analyze the user's social media activity and suggest delivery methods at the time of delivery. For example, the service provider can provide content related to what the user has recently posted. The service provider can also provide content based on the posts of accounts the user follows. The service provider can also provide content related to topics in groups and communities the user participates in. In this way, the service provider can suggest more appropriate delivery methods by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into AI, and the AI ​​can suggest delivery methods.

[0049] The generation unit can adjust the level of detail in the generated content based on the importance of the lines and scenes. For example, the generation unit will perform detailed generation for important lines and scenes. For less important lines and scenes, the generation unit can perform simplified generation. The generation unit can also add special effects to lines and scenes that the user particularly likes. In this way, the generation unit can provide more appropriate content by adjusting the level of detail in the generated content based on the importance of the lines and scenes. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input importance data for lines and scenes into a generation AI, which can then adjust the level of detail in the generated content.

[0050] The generation unit can apply different generation algorithms depending on the category of the dialogue or scene during generation. For example, for dialogue or scenes in the comedy category, the generation unit can perform generation that emphasizes humor. For dialogue or scenes in the drama category, the generation unit can also perform generation that emphasizes emotion. For dialogue or scenes in the action category, the generation unit can also perform generation that emphasizes movement. In this way, the generation unit can provide more appropriate content by applying different generation algorithms depending on the category of the dialogue or scene. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the category data of the dialogue or scene into a generation AI, and the generation AI can apply different generation algorithms.

[0051] The generation unit can determine the generation priority based on the timing of dialogue and scene selections during generation. For example, the generation unit can prioritize generating dialogue and scenes recently selected by the user. The generation unit can also prioritize generating dialogue and scenes selected by the user in conjunction with a specific event. The generation unit can also prioritize generating dialogue and scenes selected by the user during a specific time period. This allows the generation unit to provide more appropriate content by determining the generation priority based on the timing of dialogue and scene selections. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input dialogue and scene selection timing data into a generation AI, which can then determine the generation priority.

[0052] The generation unit can adjust the generation order based on the relevance of lines and scenes during generation. For example, the generation unit may generate highly relevant lines and scenes first. It can also postpone the generation of less relevant lines and scenes. The generation unit can also prioritize the generation of lines and scenes that the user particularly likes. In this way, the generation unit can provide more appropriate content by adjusting the generation order based on the relevance of lines and scenes. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input relevance data of lines and scenes into a generation AI, and the generation AI can adjust the generation order.

[0053] The modification unit can optimize the changes to the dialogue based on a specific situation when making changes. For example, if the user is participating in a specific event, the modification unit will change the dialogue to something related to that event. If the user is in a specific location, the modification unit can also change the dialogue to something related to that location. If the user is in a specific time period, the modification unit can also change the dialogue to something related to that time period. In this way, the modification unit can provide more appropriate content by optimizing the changes to the dialogue based on a specific situation. Some or all of the above processing in the modification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the modification unit can input specific situation data into a generative AI, and the generative AI can optimize the changes to the dialogue.

[0054] The modification unit can apply different modification algorithms depending on the category of the dialogue when making changes. For example, for dialogue in the comedy category, the modification unit can make changes that emphasize humor. For dialogue in the drama category, the modification unit can also make changes that emphasize emotion. For dialogue in the action category, the modification unit can also make changes that emphasize movement. In this way, the modification unit can provide more appropriate content by applying different modification algorithms depending on the category of the dialogue. Some or all of the above processing in the modification unit may be performed using a generative AI or not. For example, the modification unit can input the category data of the dialogue into a generative AI, and the generative AI can apply different modification algorithms.

[0055] The modification unit can determine the priority of changes based on when the lines were selected. For example, the modification unit may prioritize changing lines recently selected by the user. It may also prioritize changing lines selected by the user in conjunction with a specific event. It may also prioritize changing lines selected by the user during a specific time period. This allows the modification unit to provide more appropriate content by prioritizing changes based on when the lines were selected. Some or all of the above processing in the modification unit may be performed using a generative AI, or not. For example, the modification unit can input the line selection timing data into a generative AI, which can then determine the priority of changes.

[0056] The editing unit can adjust the order of changes based on the relevance of the lines when making changes. For example, the editing unit might change highly relevant lines first. It might also postpone changing less relevant lines. The editing unit might also prioritize changing lines that the user particularly likes. In this way, the editing unit can provide more appropriate content by adjusting the order of changes based on the relevance of the lines. Some or all of the above processing in the editing unit may be performed using a generative AI, or not. For example, the editing unit can input line relevance data into a generative AI, which can then adjust the order of changes.

[0057] The replacement unit can optimize the replacement content of characters based on specific situations during replacement. For example, if the user is participating in a specific event, the replacement unit will replace the character with one associated with that event. If the user is in a specific location, the replacement unit can also replace the character with one associated with that location. If the user is in a specific time period, the replacement unit can also replace the character with one associated with that time period. In this way, the replacement unit can provide more appropriate content by optimizing the replacement content of characters based on specific situations. Some or all of the above processing in the replacement unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the replacement unit can input specific situation data into a generation AI, and the generation AI can optimize the replacement content of characters.

[0058] The replacement unit can apply different replacement algorithms depending on the character's category during replacement. For example, for a character in the comedy category, the replacement unit might perform replacements that emphasize humor. For a character in the drama category, the replacement unit might perform replacements that emphasize emotion. For a character in the action category, the replacement unit might perform replacements that emphasize movement. In this way, the replacement unit can provide more appropriate content by applying different replacement algorithms depending on the character's category. Some or all of the above processing in the replacement unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the replacement unit can input character category data into a generative AI, and the generative AI can apply different replacement algorithms.

[0059] The replacement unit can determine the replacement priority based on when the character was selected during the replacement process. For example, the replacement unit may prioritize replacing characters recently selected by the user. The replacement unit may also prioritize replacing characters selected by the user in conjunction with a specific event. The replacement unit may also prioritize replacing characters selected by the user during a specific time period. This allows the replacement unit to provide more appropriate content by prioritizing replacements based on when the character was selected. Some or all of the above processing in the replacement unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the replacement unit may input character selection timing data into a generation AI, which can then determine the replacement priority.

[0060] The replacement unit can adjust the replacement order based on the relationships between characters during the replacement process. For example, the replacement unit may replace highly relevant characters first. It may also postpone replacing less relevant characters. The replacement unit may also prioritize replacing characters that the user particularly likes. In this way, the replacement unit can provide more appropriate content by adjusting the replacement order based on the relationships between characters. Some or all of the above processing in the replacement unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the replacement unit can input character relationship data into a generative AI, which can then adjust the replacement order.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The selection unit can analyze the user's past selection history and suggest the most suitable options. For example, it can analyze the trends in the lines and scenes the user has previously selected and suggest similar options. If the user likes a particular character, the selection unit can also prioritize presenting lines and scenes related to that character. If the user likes a particular genre, the selection unit can also suggest lines and scenes related to that genre. In this way, the selection unit can suggest more appropriate options by analyzing the user's past selection history. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's past selection history into AI, which can then suggest the most suitable options.

[0063] The generation unit can apply different generation algorithms depending on the category of the dialogue or scene during generation. For example, for dialogue or scenes in the comedy category, the generation unit can perform generation that emphasizes humor. For dialogue or scenes in the drama category, the generation unit can also perform generation that emphasizes emotion. For dialogue or scenes in the action category, the generation unit can also perform generation that emphasizes movement. In this way, the generation unit can provide more appropriate content by applying different generation algorithms depending on the category of the dialogue or scene. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the category data of the dialogue or scene into a generation AI, and the generation AI can apply different generation algorithms.

[0064] The modification unit can apply different modification algorithms depending on the category of the dialogue when making changes. For example, for dialogue in the comedy category, the modification unit can make changes that emphasize humor. For dialogue in the drama category, the modification unit can also make changes that emphasize emotion. For dialogue in the action category, the modification unit can also make changes that emphasize movement. In this way, the modification unit can provide more appropriate content by applying different modification algorithms depending on the category of the dialogue. Some or all of the above processing in the modification unit may be performed using a generative AI or not. For example, the modification unit can input the category data of the dialogue into a generative AI, and the generative AI can apply different modification algorithms.

[0065] The replacement unit can apply different replacement algorithms depending on the character's category during replacement. For example, for a character in the comedy category, the replacement unit might perform replacements that emphasize humor. For a character in the drama category, the replacement unit might perform replacements that emphasize emotion. For a character in the action category, the replacement unit might perform replacements that emphasize movement. In this way, the replacement unit can provide more appropriate content by applying different replacement algorithms depending on the character's category. Some or all of the above processing in the replacement unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the replacement unit can input character category data into a generative AI, and the generative AI can apply different replacement algorithms.

[0066] The delivery unit can analyze the user's past usage history to select the optimal delivery method at the time of delivery. For example, the delivery unit may prioritize the delivery method for content that the user has previously preferred to use. The delivery unit may also prioritize the delivery method for content that the user used during a specific time period. The delivery unit may also prioritize the delivery method for content that the user used in conjunction with a specific event. This allows the delivery unit to select a more appropriate delivery method by analyzing the user's past usage history. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's past usage history data into AI, which can then select the optimal delivery method.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The selection section allows the user to choose famous lines or scenes from movies, anime, and manga from a database. The selection section allows users to select famous lines or scenes from movies, anime, and manga of a specific genre or era, and can also use AI to analyze the user's preferences and past selection history to suggest the best options. Step 2: The customization section uses a generative AI to customize the lines and scenes selected by the selection section. The customization section can change lines to suit specific situations based on user input, or replace lines of a specific character with those of another character. The generative AI uses natural language processing technology to generate dynamic content based on user input. Step 3: The service provider provides the content generated by the customization service provider for users to use in their daily communications and on social media. The service provider can send the generated content to the user's smartphone or personal computer, or post it directly to social media platforms.

[0069] (Example of form 2) The Dialogue Master Agent System according to an embodiment of the present invention is a system that databases famous lines and scenes from movies, anime, and manga, and uses a generating AI to generate customized content that users can use in their daily communication and on social media. The Dialogue Master Agent System allows users to select famous lines and scenes from movies, anime, and manga from the database, and the generating AI customizes the selected lines and scenes based on the user's input. For example, users can change lines to suit specific situations or replace lines from specific characters with those of other characters. The generating AI utilizes natural language processing and image recognition technology to generate dynamic content based on the user's input. The generated content can be used by users in their daily communication and on social media. This allows users to enjoy unique and engaging communication. For example, users can post lines tailored to specific situations on social media or use customized lines in chats with friends. This system addresses the needs of entertainment fans who want to incorporate parts of movies and anime into their daily lives, and can meet the needs of social media users who seek unique communication. Furthermore, it proposes new ways of using entertainment content and realizes an improved user experience through the use of AI technology. This allows the dialogue master agent system to make user communication more engaging and enjoyable.

[0070] The dialogue master agent system according to this embodiment comprises a selection unit, a customization unit, and a provision unit. The selection unit allows the user to select famous lines or scenes from movies, anime, and manga from a database. For example, the selection unit can allow the user to select famous lines or scenes from movies, anime, and manga of a specific genre or era. The selection unit can also use AI to analyze the user's preferences and past selection history and suggest the optimal choice. The customization unit uses generation AI to customize the lines or scenes selected by the selection unit. For example, the customization unit can change the lines to suit a specific situation based on user input. The customization unit can also replace the lines of a specific character with those of another character. The generation AI uses natural language processing technology to generate dynamic content based on user input. The provision unit provides the content generated by the customization unit for the user to use in daily communication and on social media. For example, the provision unit can send the generated content to the user's smartphone or personal computer. The provision unit can also directly post the generated content to social media platforms. As a result, the dialogue master agent system according to the embodiment can make user communication more engaging and enjoyable.

[0071] The selection function allows users to choose famous lines and scenes from movies, anime, and manga from a database. For example, users can select famous lines and scenes from movies, anime, and manga of a specific genre or era. Specifically, the selection function accesses a vast database and provides an interface for users to search for famous lines and scenes that may interest them. Users can easily find lines related to specific characters or situations using keyword search and filtering functions. Furthermore, the selection function can use AI to analyze user preferences and past selection history to suggest optimal choices. For example, it can learn patterns from previously selected lines and scenes and prioritize displaying similar lines and scenes. The selection function can also analyze user input in real time to suggest lines that match the user's current mood and situation. This allows the selection function to quickly and accurately provide the lines and scenes users are looking for, improving the user experience.

[0072] The customization section uses a generative AI to customize the lines and scenes selected by the selection section. For example, the customization section can change lines to suit specific situations based on user input. Specifically, the generative AI dynamically generates lines using natural language processing technology based on the situation and character information entered by the user. For example, if the user inputs "I want to use this in a situation where I'm encouraging a friend," the generative AI will generate encouraging lines appropriate for that situation. The customization section can also replace the lines of a specific character with those of another character. For example, it can change famous lines from movies to be spoken by anime characters. The generative AI learns the characteristics and speaking styles of characters, enabling it to generate lines in a natural way. Furthermore, the customization section can fine-tune the generated lines based on user feedback to provide content that better matches the user's intentions. In this way, the customization section can achieve flexible content generation that meets user needs and increase user satisfaction.

[0073] The service provider delivers content generated by the customization service provider for users to use in their daily communications and on social media. Specifically, the service provider can send the generated content to the user's smartphone or personal computer. For example, it can send generated dialogue or scenes as text messages or images to the user's device for easy access. The service provider can also directly post the generated content to social media platforms. For example, if a user wants to share generated dialogue on social media, the service provider can automatically post the dialogue and share it with the user's followers. Furthermore, the service provider has the functionality to finely adjust the sharing scope and visibility settings of content to protect user privacy. This allows the service provider to make generated content safe and convenient for users, making daily communications more engaging and enjoyable.

[0074] The generation unit generates dynamic content using a generation AI. The generation unit can, for example, provide content that is generated in real time. The generation unit can also generate content in response to user input. For example, the generation unit can use the generation AI to generate new content based on text and images entered by the user. The generation unit can use the generation AI to provide content that meets the user's needs. This allows the generation unit to provide dynamic content that meets the user's needs. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user input into the generation AI, and the generation AI can generate dynamic content.

[0075] The modification unit changes the dialogue to suit a specific situation based on user input. For example, the modification unit can change the dialogue based on a situation entered by the user. The modification unit can use a generative AI to change the dialogue based on user input. For example, the modification unit changes the content of the dialogue to suit a situation entered by the user. The modification unit can use a generative AI to change the dialogue based on user input. This allows the modification unit to provide more appropriate content by changing the dialogue based on user input. Some or all of the above processing in the modification unit may be performed using a generative AI or not. For example, the modification unit can input user input into a generative AI, and the generative AI can change the dialogue.

[0076] The replacement unit replaces the lines of a specific character with those of another character. For example, the replacement unit can replace the lines of a character selected by the user with those of another character. The replacement unit can use a generative AI to replace the lines of a specific character with those of another character. For example, the replacement unit replaces the lines of a character selected by the user with those of another character. The replacement unit can use a generative AI to replace the lines of a specific character with those of another character. This allows the replacement unit to provide content tailored to the user's preferences by replacing character lines. Some or all of the above-described processing in the replacement unit may be performed using a generative AI or not. For example, the replacement unit can input the lines of a character selected by the user into a generative AI, which can then replace them with the lines of another character.

[0077] The service provider enables users to use the generated content on social media. For example, the service provider can directly post the generated content to the user's social media account. The service provider can also send the generated content to the user's smartphone or personal computer. The service provider can directly post the generated content to social media platforms. This allows the service provider to enable users to enjoy personalized communication by using the generated content on social media. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the generated content into an AI, which can then post it to a social media platform.

[0078] The selection unit can estimate the user's emotions and present options for memorable lines and scenes based on the estimated emotions. For example, if the user is feeling sad, the selection unit may present scenes containing encouraging or comforting lines. If the user is happy, the selection unit may also present scenes containing congratulatory or empathetic lines. If the user is angry, the selection unit may also present scenes containing lines to help the user regain their composure. In this way, the selection unit can provide more appropriate content by presenting memorable lines and scenes that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input user emotion data into a generative AI, which can then present options based on those emotions.

[0079] The selection unit can analyze the user's past selection history and suggest the most suitable options. For example, it can analyze the trends in the lines and scenes the user has previously selected and suggest similar options. If the user likes a particular character, the selection unit can also prioritize presenting lines and scenes related to that character. If the user likes a particular genre, the selection unit can also suggest lines and scenes related to that genre. In this way, the selection unit can suggest more appropriate options by analyzing the user's past selection history. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's past selection history into AI, which can then suggest the most suitable options.

[0080] The selection function can filter famous lines and scenes based on the user's current interests and trends. For example, the selection function can prioritize lines and scenes related to topics the user has recently been interested in. The selection function can also suggest lines and scenes from currently trending or popular movies and anime. The selection function can also present lines and scenes introduced by influencers the user follows. In this way, the selection function can provide more relevant content by filtering based on the user's current interests and trends. Some or all of the above processing in the selection function may be performed using AI or not. For example, the selection function can input the user's current interests and trend information into AI, which can then perform the filtering.

[0081] The selection unit can estimate the user's emotions and adjust the display order of options based on the estimated emotions. For example, if the user is depressed, the selection unit can first display uplifting lines or scenes. If the user is excited, the selection unit can first display lines or scenes that help the user regain their composure. If the user is relaxed, the selection unit can first display lines or scenes that enhance the enjoyment. In this way, the selection unit can provide more appropriate content by adjusting the display order of options according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input user emotion data into a generative AI, which can then adjust the display order of options based on the emotions.

[0082] The selection unit can prioritize and present highly relevant options when selecting famous lines or scenes, taking into account the user's geographical location. For example, if the user is in a specific region, the selection unit can present lines or scenes related to that region. If the user is traveling, the selection unit can also suggest lines or scenes related to their travel destination. If the user is participating in a specific event, the selection unit can also present lines or scenes related to that event. In this way, the selection unit can provide more relevant content by taking the user's geographical location into consideration. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's geographical location information into AI, which can then present highly relevant options.

[0083] The selection unit can analyze the user's social media activity and suggest relevant options when selecting famous lines or scenes. For example, the selection unit can suggest lines or scenes related to content the user has recently posted. The selection unit can also suggest lines or scenes based on the content of posts from accounts the user follows. The selection unit can also suggest lines or scenes related to topics in groups or communities the user participates in. In this way, the selection unit can provide more appropriate content by analyzing the user's social media activity. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's social media activity data into AI, which can then suggest relevant options.

[0084] The customization unit can estimate the user's emotions and adjust the way the customization is expressed based on the estimated emotions. For example, if the user is feeling sad, the customization unit can customize the dialogue to a gentle tone. If the user is happy, the customization unit can also customize the dialogue to a cheerful tone. If the user is angry, the customization unit can also customize the dialogue to a tone that helps the user regain their composure. In this way, the customization unit can provide more appropriate content by adjusting the way the customization is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using or without a generative AI. For example, the customization unit can input user emotion data into a generative AI, which can then adjust the way the customization is expressed.

[0085] The customization unit can adjust the level of detail of customization based on the importance of the lines and scenes during the customization process. For example, the customization unit can perform detailed customization for important lines and scenes. For lines and scenes that are not very important, the customization unit can perform simplified customization. The customization unit can also add special effects to lines and scenes that the user particularly likes. In this way, the customization unit can provide more appropriate content by adjusting the level of detail of customization based on the importance of the lines and scenes. Some or all of the above processing in the customization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the customization unit can input importance data of lines and scenes into a generative AI, and the generative AI can adjust the level of detail of the customization.

[0086] The customization unit can apply different customization algorithms depending on the category of the dialogue and scene during customization. For example, for dialogue and scenes in the comedy category, the customization unit can perform customizations that emphasize humor. For dialogue and scenes in the drama category, the customization unit can also perform customizations that emphasize emotion. For dialogue and scenes in the action category, the customization unit can also perform customizations that emphasize movement. In this way, the customization unit can provide more appropriate content by applying different customization algorithms depending on the category of the dialogue and scene. Some or all of the above processing in the customization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the customization unit can input the category data of the dialogue and scene into a generative AI, and the generative AI can apply different customization algorithms.

[0087] The customization unit can estimate the user's emotions and adjust the length of the customization based on the estimated emotions. For example, if the user is in a hurry, the customization unit can create a short, concise customization. If the user is relaxed, the customization unit can create a longer customization that includes detailed explanations. If the user is excited, the customization unit can create a customization that includes visually stimulating effects. In this way, the customization unit can provide more appropriate content by adjusting the length of the customization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using or without generative AI. For example, the customization unit can input user emotion data into a generative AI, which can then adjust the length of the customization.

[0088] The customization unit can determine the priority of customization based on the timing of dialogue and scene selections during the customization process. For example, the customization unit can prioritize customizing dialogue and scenes recently selected by the user. The customization unit can also prioritize customizing dialogue and scenes selected by the user in conjunction with a specific event. The customization unit can also prioritize customizing dialogue and scenes selected by the user during a specific time period. This allows the customization unit to provide more appropriate content by determining the priority of customization based on the timing of dialogue and scene selections. Some or all of the above processing in the customization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the customization unit can input dialogue and scene selection timing data into a generative AI, which can then determine the priority of customization.

[0089] The customization unit can adjust the order of customization based on the relevance of lines and scenes during the customization process. For example, the customization unit may customize highly relevant lines and scenes first. It can also postpone the customization of less relevant lines and scenes. The customization unit can also prioritize customizing lines and scenes that the user particularly likes. In this way, the customization unit can provide more appropriate content by adjusting the order of customization based on the relevance of lines and scenes. Some or all of the above processing in the customization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the customization unit can input relevance data of lines and scenes into a generative AI, and the generative AI can adjust the order of customization.

[0090] The content delivery unit can estimate the user's emotions and adjust the content delivery method based on the estimated emotions. For example, if the user is feeling sad, the content delivery unit can deliver the content in a gentle tone. If the user is happy, the content delivery unit can also deliver the content in a cheerful tone. If the user is angry, the content delivery unit can deliver the content in a tone that helps the user regain their composure. In this way, the content delivery unit can provide more appropriate content by adjusting the content delivery method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the content delivery unit may be performed using AI or not. For example, the content delivery unit can input user emotion data into a generative AI, which can then adjust the content delivery method.

[0091] The delivery unit can analyze the user's past usage history to select the optimal delivery method at the time of delivery. For example, the delivery unit may prioritize the delivery method for content that the user has previously preferred to use. The delivery unit may also prioritize the delivery method for content that the user used during a specific time period. The delivery unit may also prioritize the delivery method for content that the user used in conjunction with a specific event. This allows the delivery unit to select a more appropriate delivery method by analyzing the user's past usage history. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's past usage history data into AI, which can then select the optimal delivery method.

[0092] The service provider can adjust the timing of service delivery based on the user's current lifestyle. For example, if the user is busy, the service provider can provide content that can be used in a short amount of time. If the user is relaxed, the service provider can also provide content that can be enjoyed for a longer period of time. If the user is participating in a specific event, the service provider can also provide content related to that event. This allows the service provider to provide more appropriate content by adjusting the timing of service delivery based on the user's current lifestyle. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input data on the user's current lifestyle into the AI, which can then adjust the timing of service delivery.

[0093] The service provider can estimate the user's emotions and prioritize the content to be provided based on those emotions. For example, if the user is feeling sad, the service provider will prioritize providing uplifting content. If the user is happy, the service provider may also prioritize providing empathetic content. If the user is angry, the service provider may also prioritize providing content that helps the user regain their composure. In this way, the service provider can provide more appropriate content by prioritizing the content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and determine the priority of the content that the generative AI will provide.

[0094] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can provide content related to that region. If the user is traveling, the service provider can also provide content related to the travel destination. If the user is participating in a specific event, the service provider can also provide content related to that event. This allows the service provider to select a more appropriate delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location data into AI, which can then select the optimal delivery method.

[0095] The service provider can analyze the user's social media activity and suggest delivery methods at the time of delivery. For example, the service provider can provide content related to what the user has recently posted. The service provider can also provide content based on the posts of accounts the user follows. The service provider can also provide content related to topics in groups and communities the user participates in. In this way, the service provider can suggest more appropriate delivery methods by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into AI, and the AI ​​can suggest delivery methods.

[0096] The generation unit can estimate the user's emotions and adjust the way it presents the generated content based on those emotions. For example, if the user is relaxed, the generation unit can generate content that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate short, concise content. If the user is excited, the generation unit can also generate content with visually stimulating effects. In this way, the generation unit can provide more appropriate content by adjusting the way it presents the generated content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generation AI. For example, the generation unit can input user emotion data into a generation AI, which can then adjust the way it presents the content.

[0097] The generation unit can adjust the level of detail in the generated content based on the importance of the lines and scenes. For example, the generation unit will perform detailed generation for important lines and scenes. For less important lines and scenes, the generation unit can perform simplified generation. The generation unit can also add special effects to lines and scenes that the user particularly likes. In this way, the generation unit can provide more appropriate content by adjusting the level of detail in the generated content based on the importance of the lines and scenes. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input importance data for lines and scenes into a generation AI, which can then adjust the level of detail in the generated content.

[0098] The generation unit can apply different generation algorithms depending on the category of the dialogue or scene during generation. For example, for dialogue or scenes in the comedy category, the generation unit can perform generation that emphasizes humor. For dialogue or scenes in the drama category, the generation unit can also perform generation that emphasizes emotion. For dialogue or scenes in the action category, the generation unit can also perform generation that emphasizes movement. In this way, the generation unit can provide more appropriate content by applying different generation algorithms depending on the category of the dialogue or scene. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the category data of the dialogue or scene into a generation AI, and the generation AI can apply different generation algorithms.

[0099] The generation unit can estimate the user's emotions and adjust the length of the content it generates based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise content. If the user is relaxed, the generation unit can also generate longer content with detailed explanations. If the user is excited, the generation unit can also generate content with visually stimulating effects. In this way, the generation unit can provide more appropriate content by adjusting the length of the content it generates according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generation AI. For example, the generation unit can input user emotion data into a generation AI, which can then adjust the length of the content.

[0100] The generation unit can determine the generation priority based on the timing of dialogue and scene selections during generation. For example, the generation unit can prioritize generating dialogue and scenes recently selected by the user. The generation unit can also prioritize generating dialogue and scenes selected by the user in conjunction with a specific event. The generation unit can also prioritize generating dialogue and scenes selected by the user during a specific time period. This allows the generation unit to provide more appropriate content by determining the generation priority based on the timing of dialogue and scene selections. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input dialogue and scene selection timing data into a generation AI, which can then determine the generation priority.

[0101] The generation unit can adjust the generation order based on the relevance of lines and scenes during generation. For example, the generation unit may generate highly relevant lines and scenes first. It can also postpone the generation of less relevant lines and scenes. The generation unit can also prioritize the generation of lines and scenes that the user particularly likes. In this way, the generation unit can provide more appropriate content by adjusting the generation order based on the relevance of lines and scenes. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input relevance data of lines and scenes into a generation AI, and the generation AI can adjust the generation order.

[0102] The editing unit can estimate the user's emotions and adjust how the dialogue is changed based on the estimated emotions. For example, if the user is feeling sad, the editing unit can change the dialogue to a gentler tone. If the user is happy, the editing unit can also change the dialogue to a brighter tone. If the user is angry, the editing unit can change the dialogue to a tone that helps the user regain their composure. In this way, the editing unit can provide more appropriate content by adjusting how the dialogue is changed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editing unit may be performed using or without a generative AI. For example, the editing unit can input user emotion data into a generative AI, which can then adjust how the dialogue is changed.

[0103] The modification unit can optimize the changes to the dialogue based on a specific situation when making changes. For example, if the user is participating in a specific event, the modification unit will change the dialogue to something related to that event. If the user is in a specific location, the modification unit can also change the dialogue to something related to that location. If the user is in a specific time period, the modification unit can also change the dialogue to something related to that time period. In this way, the modification unit can provide more appropriate content by optimizing the changes to the dialogue based on a specific situation. Some or all of the above processing in the modification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the modification unit can input specific situation data into a generative AI, and the generative AI can optimize the changes to the dialogue.

[0104] The modification unit can apply different modification algorithms depending on the category of the dialogue when making changes. For example, for dialogue in the comedy category, the modification unit can make changes that emphasize humor. For dialogue in the drama category, the modification unit can also make changes that emphasize emotion. For dialogue in the action category, the modification unit can also make changes that emphasize movement. In this way, the modification unit can provide more appropriate content by applying different modification algorithms depending on the category of the dialogue. Some or all of the above processing in the modification unit may be performed using a generative AI or not. For example, the modification unit can input the category data of the dialogue into a generative AI, and the generative AI can apply different modification algorithms.

[0105] The editing unit can estimate the user's emotions and adjust the frequency of dialogue changes based on the estimated emotions. For example, if the user is in a hurry, the editing unit will change the dialogue frequently. If the user is relaxed, the editing unit may change the dialogue slowly. If the user is excited, the editing unit may change the dialogue with visually stimulating effects. In this way, the editing unit can provide more appropriate content by adjusting the frequency of dialogue changes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editing unit may be performed using or without a generative AI. For example, the editing unit can input user emotion data into a generative AI, which can then adjust the frequency of dialogue changes.

[0106] The modification unit can determine the priority of changes based on when the lines were selected. For example, the modification unit may prioritize changing lines recently selected by the user. It may also prioritize changing lines selected by the user in conjunction with a specific event. It may also prioritize changing lines selected by the user during a specific time period. This allows the modification unit to provide more appropriate content by prioritizing changes based on when the lines were selected. Some or all of the above processing in the modification unit may be performed using a generative AI, or not. For example, the modification unit can input the line selection timing data into a generative AI, which can then determine the priority of changes.

[0107] The editing unit can adjust the order of changes based on the relevance of the lines when making changes. For example, the editing unit might change highly relevant lines first. It might also postpone changing less relevant lines. The editing unit might also prioritize changing lines that the user particularly likes. In this way, the editing unit can provide more appropriate content by adjusting the order of changes based on the relevance of the lines. Some or all of the above processing in the editing unit may be performed using a generative AI, or not. For example, the editing unit can input line relevance data into a generative AI, which can then adjust the order of changes.

[0108] The substitution unit can estimate the user's emotions and adjust the character substitution method based on the estimated user emotions. For example, if the user is feeling sad, the substitution unit will substitute a gentle character. If the user is happy, the substitution unit may substitute a cheerful character. If the user is angry, the substitution unit may substitute a character that helps the user regain their composure. In this way, the substitution unit can provide more appropriate content by adjusting the character substitution method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the substitution unit may be performed using the generative AI or not. For example, the substitution unit can input the user's emotion data into the generative AI, which can then adjust the character substitution method.

[0109] The replacement unit can optimize the replacement content of characters based on specific situations during replacement. For example, if the user is participating in a specific event, the replacement unit will replace the character with one associated with that event. If the user is in a specific location, the replacement unit can also replace the character with one associated with that location. If the user is in a specific time period, the replacement unit can also replace the character with one associated with that time period. In this way, the replacement unit can provide more appropriate content by optimizing the replacement content of characters based on specific situations. Some or all of the above processing in the replacement unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the replacement unit can input specific situation data into a generation AI, and the generation AI can optimize the replacement content of characters.

[0110] The replacement unit can apply different replacement algorithms depending on the character's category during replacement. For example, for a character in the comedy category, the replacement unit might perform replacements that emphasize humor. For a character in the drama category, the replacement unit might perform replacements that emphasize emotion. For a character in the action category, the replacement unit might perform replacements that emphasize movement. In this way, the replacement unit can provide more appropriate content by applying different replacement algorithms depending on the character's category. Some or all of the above processing in the replacement unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the replacement unit can input character category data into a generative AI, and the generative AI can apply different replacement algorithms.

[0111] The substitution unit can estimate the user's emotions and adjust the frequency of character substitutions based on the estimated emotions. For example, if the user is in a hurry, the substitution unit will frequently substitute characters. If the user is relaxed, the substitution unit can substitute characters slowly. If the user is excited, the substitution unit can substitute characters with visually stimulating effects. In this way, the substitution unit can provide more appropriate content by adjusting the frequency of character substitutions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the substitution unit may be performed using the generative AI or not. For example, the substitution unit can input user emotion data into the generative AI, which can then adjust the frequency of character substitutions.

[0112] The replacement unit can determine the replacement priority based on when the character was selected during the replacement process. For example, the replacement unit may prioritize replacing characters recently selected by the user. The replacement unit may also prioritize replacing characters selected by the user in conjunction with a specific event. The replacement unit may also prioritize replacing characters selected by the user during a specific time period. This allows the replacement unit to provide more appropriate content by prioritizing replacements based on when the character was selected. Some or all of the above processing in the replacement unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the replacement unit may input character selection timing data into a generation AI, which can then determine the replacement priority.

[0113] The replacement unit can adjust the replacement order based on the relationships between characters during the replacement process. For example, the replacement unit may replace highly relevant characters first. It may also postpone replacing less relevant characters. The replacement unit may also prioritize replacing characters that the user particularly likes. In this way, the replacement unit can provide more appropriate content by adjusting the replacement order based on the relationships between characters. Some or all of the above processing in the replacement unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the replacement unit can input character relationship data into a generative AI, which can then adjust the replacement order.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] The selection unit can analyze the user's past selection history and suggest the most suitable options. For example, it can analyze the trends in the lines and scenes the user has previously selected and suggest similar options. If the user likes a particular character, the selection unit can also prioritize presenting lines and scenes related to that character. If the user likes a particular genre, the selection unit can also suggest lines and scenes related to that genre. In this way, the selection unit can suggest more appropriate options by analyzing the user's past selection history. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the user's past selection history into AI, which can then suggest the most suitable options.

[0116] The generation unit can apply different generation algorithms depending on the category of the dialogue or scene during generation. For example, for dialogue or scenes in the comedy category, the generation unit can perform generation that emphasizes humor. For dialogue or scenes in the drama category, the generation unit can also perform generation that emphasizes emotion. For dialogue or scenes in the action category, the generation unit can also perform generation that emphasizes movement. In this way, the generation unit can provide more appropriate content by applying different generation algorithms depending on the category of the dialogue or scene. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the category data of the dialogue or scene into a generation AI, and the generation AI can apply different generation algorithms.

[0117] The modification unit can apply different modification algorithms depending on the category of the dialogue when making changes. For example, for dialogue in the comedy category, the modification unit can make changes that emphasize humor. For dialogue in the drama category, the modification unit can also make changes that emphasize emotion. For dialogue in the action category, the modification unit can also make changes that emphasize movement. In this way, the modification unit can provide more appropriate content by applying different modification algorithms depending on the category of the dialogue. Some or all of the above processing in the modification unit may be performed using a generative AI or not. For example, the modification unit can input the category data of the dialogue into a generative AI, and the generative AI can apply different modification algorithms.

[0118] The replacement unit can apply different replacement algorithms depending on the character's category during replacement. For example, for a character in the comedy category, the replacement unit might perform replacements that emphasize humor. For a character in the drama category, the replacement unit might perform replacements that emphasize emotion. For a character in the action category, the replacement unit might perform replacements that emphasize movement. In this way, the replacement unit can provide more appropriate content by applying different replacement algorithms depending on the character's category. Some or all of the above processing in the replacement unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the replacement unit can input character category data into a generative AI, and the generative AI can apply different replacement algorithms.

[0119] The delivery unit can analyze the user's past usage history to select the optimal delivery method at the time of delivery. For example, the delivery unit may prioritize the delivery method for content that the user has previously preferred to use. The delivery unit may also prioritize the delivery method for content that the user used during a specific time period. The delivery unit may also prioritize the delivery method for content that the user used in conjunction with a specific event. This allows the delivery unit to select a more appropriate delivery method by analyzing the user's past usage history. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's past usage history data into AI, which can then select the optimal delivery method.

[0120] The selection unit can estimate the user's emotions and present options for memorable lines and scenes based on the estimated emotions. For example, if the user is feeling sad, the selection unit may present scenes containing encouraging or comforting lines. If the user is happy, the selection unit may also present scenes containing congratulatory or empathetic lines. If the user is angry, the selection unit may also present scenes containing lines to help the user regain their composure. In this way, the selection unit can provide more appropriate content by presenting memorable lines and scenes that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input user emotion data into a generative AI, which can then present options based on those emotions.

[0121] The customization unit can estimate the user's emotions and adjust the way the customization is expressed based on the estimated emotions. For example, if the user is feeling sad, the customization unit can customize the dialogue to a gentle tone. If the user is happy, the customization unit can also customize the dialogue to a cheerful tone. If the user is angry, the customization unit can also customize the dialogue to a tone that helps the user regain their composure. In this way, the customization unit can provide more appropriate content by adjusting the way the customization is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using or without a generative AI. For example, the customization unit can input user emotion data into a generative AI, which can then adjust the way the customization is expressed.

[0122] The content delivery unit can estimate the user's emotions and adjust the content delivery method based on the estimated emotions. For example, if the user is feeling sad, the content delivery unit can deliver the content in a gentle tone. If the user is happy, the content delivery unit can also deliver the content in a cheerful tone. If the user is angry, the content delivery unit can deliver the content in a tone that helps the user regain their composure. In this way, the content delivery unit can provide more appropriate content by adjusting the content delivery method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the content delivery unit may be performed using AI or not. For example, the content delivery unit can input user emotion data into a generative AI, which can then adjust the content delivery method.

[0123] The generation unit can estimate the user's emotions and adjust the way it presents the generated content based on those emotions. For example, if the user is relaxed, the generation unit can generate content that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate short, concise content. If the user is excited, the generation unit can also generate content with visually stimulating effects. In this way, the generation unit can provide more appropriate content by adjusting the way it presents the generated content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generation AI. For example, the generation unit can input user emotion data into a generation AI, which can then adjust the way it presents the content.

[0124] The editing unit can estimate the user's emotions and adjust how the dialogue is changed based on the estimated emotions. For example, if the user is feeling sad, the editing unit can change the dialogue to a gentler tone. If the user is happy, the editing unit can also change the dialogue to a brighter tone. If the user is angry, the editing unit can change the dialogue to a tone that helps the user regain their composure. In this way, the editing unit can provide more appropriate content by adjusting how the dialogue is changed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editing unit may be performed using or without a generative AI. For example, the editing unit can input user emotion data into a generative AI, which can then adjust how the dialogue is changed.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The selection section allows the user to choose famous lines or scenes from movies, anime, and manga from a database. The selection section allows users to select famous lines or scenes from movies, anime, and manga of a specific genre or era, and can also use AI to analyze the user's preferences and past selection history to suggest the best options. Step 2: The customization section uses a generative AI to customize the lines and scenes selected by the selection section. The customization section can change lines to suit specific situations based on user input, or replace lines of a specific character with those of another character. The generative AI uses natural language processing technology to generate dynamic content based on user input. Step 3: The service provider provides the content generated by the customization service provider for users to use in their daily communications and on social media. The service provider can send the generated content to the user's smartphone or personal computer, or post it directly to social media platforms.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0129] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the selection unit, customization unit, provision unit, generation unit, modification unit, and replacement unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the smart device 14, allowing the user to select famous lines or scenes from movies, anime, or manga from a database. The customization unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, and customizes the selected lines or scenes using a generation AI. The provision unit is implemented by the control unit 46A of the smart device 14, for example, and provides the generated content to the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, and generates dynamic content using a generation AI. The modification unit is implemented by the control unit 46A of the smart device 14, for example, and modifies lines based on user input. The replacement unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, and replaces lines of a specific character with lines of another character. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the selection unit, customization unit, provision unit, generation unit, modification unit, and replacement unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the smart glasses 214, allowing the user to select famous lines or scenes from movies, anime, or manga from a database. The customization unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, and customizes the selected lines or scenes using a generation AI. The provision unit is implemented by the control unit 46A of the smart glasses 214, for example, and provides the generated content to the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, and generates dynamic content using a generation AI. The modification unit is implemented by the control unit 46A of the smart glasses 214, for example, and modifies lines based on user input. The replacement unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, and replaces lines of a specific character with lines of another character. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the selection unit, customization unit, provision unit, generation unit, modification unit, and replacement unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the headset terminal 314, allowing the user to select famous lines or scenes from movies, anime, or manga from a database. The customization unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, and customizes the selected lines or scenes using a generation AI. The provision unit is implemented by the control unit 46A of the headset terminal 314, for example, and provides the generated content to the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, and generates dynamic content using a generation AI. The modification unit is implemented by the control unit 46A of the headset terminal 314, for example, and modifies lines based on user input. The replacement unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, and replaces lines of a specific character with lines of another character. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0165] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0166] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0167] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0169] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0170] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0171] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0172] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0173] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0174] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0175] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0176] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0177] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0178] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0179] Each of the multiple elements described above, including the selection unit, customization unit, provision unit, generation unit, modification unit, and replacement unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the robot 414, allowing the user to select famous lines or scenes from movies, anime, or manga from a database. The customization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which customizes the selected lines or scenes using a generation AI. The provision unit is implemented by, for example, the control unit 46A of the robot 414, which provides the generated content to the user. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates dynamic content using a generation AI. The modification unit is implemented by, for example, the control unit 46A of the robot 414, which modifies lines based on user input. The replacement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which replaces lines of a specific character with lines of another character. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0180] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0181] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0182] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0183] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0184] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0185] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0186] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0187] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0188] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0189] 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.

[0190] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0191] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0192] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0193] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0194] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0195] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0196] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0197] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0198] (Note 1) A selection section where the user selects famous lines or scenes from movies, anime, and manga from a database, A customization unit in which the AI ​​generates and customizes the lines and scenes selected by the selection unit, The system comprises a provisioning unit that allows users to use the content generated by the customization unit in their daily communication and on social media. A system characterized by the following features. (Note 2) It features a generation unit that generates dynamic content using generation AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a modification section that changes the dialogue to suit a specific situation based on user input. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a substitution function that replaces the lines of a specific character with those of another character. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Users can use the generated content on social media. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned selection unit is It estimates the user's emotions and presents options for memorable lines and scenes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned selection unit is It analyzes the user's past selection history and suggests the optimal choice. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned selection unit is When selecting famous lines or scenes, filtering is performed based on the user's current interests and trends. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned selection unit is It estimates the user's emotions and adjusts the display order of options based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned selection unit is When selecting famous lines or scenes, the system prioritizes presenting highly relevant options by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned selection unit is When selecting famous lines or scenes, the system analyzes the user's social media activity and presents relevant options. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned customization unit is It estimates the user's emotions and adjusts the way customization is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned customization unit is During customization, the level of detail is adjusted based on the importance of the dialogue and scenes. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned customization unit is During customization, different customization algorithms are applied depending on the category of dialogue and scene. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned customization unit is It estimates the user's emotions and adjusts the length of customization based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned customization unit is During customization, the priority of customization is determined based on the timing of dialogue and scene selection. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned customization unit is During customization, the order of customizations is adjusted based on the relevance of the dialogue and scenes. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, We estimate user sentiment and adjust how content is delivered based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the service, we analyze the user's past usage history to select the optimal delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, the timing of delivery will be adjusted based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the content to be delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and propose a delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is It estimates user emotions and adjusts how generated content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During generation, the level of detail is adjusted based on the importance of the dialogue and scenes. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is During generation, different generation algorithms are applied depending on the category of dialogue and scene. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is It estimates the user's emotions and adjusts the length of the generated content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is During generation, the generation priority is determined based on the timing of dialogue and scene selection. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is During generation, the generation order is adjusted based on the relevance of dialogue and scenes. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned modified part is, It estimates the user's emotions and adjusts how the dialogue is changed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned modified part is, When making changes, the dialogue changes are optimized based on the specific situation. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned modified part is, When making changes, different modification algorithms are applied depending on the category of the dialogue. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned modified part is, The system estimates the user's emotions and adjusts the frequency of dialogue changes based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned modified part is, When making changes, prioritize the changes based on when the lines were selected. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned modified part is, When making changes, adjust the order of changes based on the relevance of the dialogue. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned replacement part is It estimates the user's emotions and adjusts how characters are replaced based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned replacement part is During replacement, the replacement content of the character is optimized based on the specific situation. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned replacement part is During replacement, different replacement algorithms are applied depending on the character category. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned replacement part is The system estimates the user's emotions and adjusts the frequency of character replacement based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned replacement part is During replacement, the replacement priority is determined based on when the character was selected. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned replacement part is During replacement, adjust the replacement order based on the relationships between characters. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0199] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A selection section where the user selects famous lines or scenes from movies, anime, and manga from a database, A customization unit in which the AI ​​generates and customizes the lines and scenes selected by the selection unit, The system comprises a provisioning unit that allows users to use the content generated by the customization unit in their daily communication and on social media. A system characterized by the following features.

2. It includes a generation unit that generates dynamic content using generation AI. The system according to feature 1.

3. It includes a modification section that changes the dialogue to suit a specific situation based on user input. The system according to feature 1.

4. It includes a substitution function that replaces the lines of a specific character with those of another character. The system according to feature 1.

5. The aforementioned supply unit is, Users can use the generated content on social media. The system according to feature 1.

6. The aforementioned selection unit is It estimates the user's emotions and presents options for memorable lines and scenes based on those estimated emotions. The system according to feature 1.

7. The aforementioned selection unit is It analyzes the user's past selection history and suggests the optimal choice. The system according to feature 1.

8. The aforementioned selection unit is When selecting famous lines or scenes, filtering is performed based on the user's current interests and trends. The system according to feature 1.

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