system

Abstract

The system according to this embodiment aims to allow users to incorporate lines and scenes from their favorite works into their daily communication. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user input. The analysis unit analyzes the information received by the reception unit. The generation unit generates optimal lines and scenes based on the information analyzed by the analysis unit. The provision unit provides the lines and scenes generated by the generation unit.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] [[ID=关于专利文献1]]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for a user to incorporate lines or scenes from their favorite works into daily communication.

[0005] The system according to the embodiment aims to enable a user to incorporate lines or scenes from their favorite works into daily communication.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user input. The analysis unit analyzes the information received by the reception unit. The generation unit generates optimal dialogue and scenes based on the information analyzed by the analysis unit. The provision unit provides the dialogue and scenes generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to incorporate lines and scenes from their favorite works into their daily communication. [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 famous quote / scene generation system according to an embodiment of the present invention is a system that utilizes data on famous quotes and scenes from movies, anime, and manga, and uses a generation AI to allow users to incorporate quotes and scenes from their favorite works into their daily communication and content. In this system, the user inputs a famous quote or scene they want to use in a messaging app or social media, and the generation AI analyzes the input and generates the most suitable quote or scene. The generated quote or scene is provided in GIF or text format, and the user can use it in their daily communication. For example, the user inputs a famous quote or scene they want to use in a messaging app or social media. For example, they input a specific request such as "I want to use this quote" or "I want to recreate this scene." This information is input to the generation AI. Next, the generation AI analyzes the input information. The generation AI refers to a database of famous quotes and scenes from movies, anime, and manga, and generates the most suitable quote or scene for the user's request. For example, if the user inputs "I want to recreate an emotional scene," the generation AI extracts an emotional scene from the database and generates a GIF or text based on it. The generated lines and scenes are provided in GIF and text formats. Users can use these in messaging apps and social media. For example, they can send generated GIFs to friends or use generated lines in social media posts. This system allows users to incorporate lines and scenes from their favorite works into their daily communication. This enriches communication and allows for the sharing of emotions. For example, sending a GIF recreating a famous scene from a movie to a friend can create a common topic and deepen communication. This system is also very appealing to fans of movies, anime, and manga. Because they can freely customize and use lines and scenes from their favorite works, it encourages interaction among fans. For example, they can share lines from their favorite works on social media and share emotions with other fans. Furthermore, this system is also beneficial for businesses. Companies can use this system to promote their products and services.For example, creating advertisements using famous movie scenes and spreading them on social media can increase product awareness. In this way, AI-powered systems for generating famous lines and scenes enrich user communication and provide a new means of sharing emotions. Thus, these famous line and scene generation systems can enrich user communication and facilitate the sharing of emotions.

[0029] The famous quote / scene generation system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user input. User input includes, for example, requests for famous quotes or scenes to be used, but is not limited to such examples. The reception unit provides, for example, an interface for the user to input famous quotes or scenes to be used in messaging apps or social networking services. The reception unit can also send the user input to the generation AI. The analysis unit uses the generation AI to analyze the information received by the reception unit. The analysis unit, for example, refers to a database of famous quotes and scenes from movies, anime, and manga based on the user's request. The generation AI extracts relevant information from the database in order to generate the best quotes and scenes for the user's request. The generation unit generates the best quotes and scenes based on the information analyzed by the analysis unit. The generation unit uses, for example, the generation AI to generate quotes and scenes in response to the user's request. The generated quotes and scenes are provided in GIF or text format. The providing unit provides the lines and scenes generated by the generating unit. The providing unit provides, for example, an interface for providing the generated lines and scenes to the user. The providing unit also has a function for sharing the generated lines and scenes on social networking services (SNS) or messaging apps. As a result, the famous lines and famous scenes generation system according to the embodiment can analyze user input and generate and provide the most suitable lines and scenes. For example, the receiving unit provides an interface for the user to input famous lines and scenes they want to use on messaging apps or SNS. The analysis unit uses a generation AI to analyze the information received by the receiving unit. The generation unit generates the most suitable lines and scenes based on the information analyzed by the analysis unit. The providing unit provides the lines and scenes generated by the generation unit. As a result, the famous lines and famous scenes generation system according to the embodiment can analyze user input and generate and provide the most suitable lines and scenes.

[0030] The reception desk receives user input. User input may include, but is not limited to, requests for famous lines or scenes that users wish to use. The reception desk provides an interface for users to input famous lines or scenes they wish to use in messaging apps or social media. Specifically, the reception desk designs an intuitive and user-friendly interface so that users can easily input requests. For example, it provides an interface with text boxes, dropdown menus, and voice input functions so that users can input details of the lines or scenes they wish to use. The reception desk can also send user input to a generating AI. The data sent to the generating AI includes the user's request and related metadata (e.g., the date and time of the request, user profile information, etc.). This allows the generating AI to obtain information to generate lines or scenes that best suit the user's needs. Furthermore, the reception desk has the ability to check user input in real time and prompt for corrections or additional information as needed. For example, if the user's request is unclear or additional information is needed, it displays pop-up messages and guidelines to help users input accurate requests. This allows the reception desk to efficiently and accurately receive user input and provide the necessary information for the generating AI.

[0031] The analysis unit uses generative AI to analyze the information received by the reception unit. For example, based on a user's request, the analysis unit consults a database of famous lines and scenes from movies, anime, and manga. Specifically, the analysis unit uses natural language processing technology to analyze the user's request and understand its content. The generative AI extracts relevant information from the database to generate the most suitable lines and scenes for the user's request. For example, if a user requests an "emotional scene," the analysis unit searches the database for lines and scenes related to emotional scenes and extracts the most suitable ones. Furthermore, the analysis unit generates multiple candidates in response to the user's request and evaluates the relevance of each candidate. The evaluation uses an algorithm that the generative AI has learned based on past data and user feedback. This allows the analysis unit to identify the most suitable lines and scenes for the user's request and provide them to the generation unit. In addition, the analysis unit utilizes database indexing and caching functions to improve the response speed to user requests. This allows the analysis unit to quickly and accurately analyze user requests and provide information to generate optimal dialogue and scenes.

[0032] The generation unit generates optimal dialogue and scenes based on the information analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate dialogue and scenes that meet user requests. Specifically, the generation AI uses natural language generation technology to generate the most suitable dialogue and scenes for user requests. For example, if a user requests an "emotional scene," the generation AI generates new dialogue and scenes based on information related to emotional scenes extracted from a database. The generated dialogue and scenes are provided in GIF or text format. The generation unit evaluates the quality of the generated dialogue and scenes and makes corrections or improvements as needed. For example, if the generated dialogue and scenes do not perfectly match the user's request, the generation AI runs the generation process again to generate more suitable dialogue and scenes. The generation unit also has a function to review and edit the content before providing the generated dialogue and scenes to the user. This allows the generation unit to generate and provide high-quality dialogue and scenes that are optimal for user requests. Furthermore, the generation unit regularly updates the AI's training data to reflect the latest information, thereby improving the efficiency of the generation process. This allows the generation unit to consistently generate and provide users with high-quality dialogue and scenes based on the most up-to-date information.

[0033] The provider unit provides the dialogue and scenes generated by the generation unit. For example, the provider unit provides an interface for providing the generated dialogue and scenes to the user. Specifically, the provider unit designs an intuitive and user-friendly interface so that users can easily view, save, and share the generated dialogue and scenes. For example, it provides an interface with a dedicated viewer for displaying the generated dialogue and scenes, and buttons for sharing on social media and messaging apps. The provider unit also includes functionality for sharing the generated dialogue and scenes on social media and messaging apps. For example, it provides a function that allows users to easily generate a link and post when sharing generated dialogue and scenes on social media. Furthermore, the provider unit also has the functionality to collect user feedback and provide it to the generation and analysis units. This allows the provider unit to provide optimal dialogue and scenes that meet user needs and improve the overall system quality. Additionally, the provider unit includes functions for saving and managing generated dialogue and scenes, allowing users to easily reuse previously generated dialogue and scenes. This allows the service provider to deliver generated dialogue and scenes to users quickly and reliably, thereby improving user satisfaction.

[0034] The generation unit uses a generation AI to reference a database of famous lines and scenes from movies, anime, and manga, and generate the most suitable lines and scenes. For example, the generation unit uses the generation AI to generate lines and scenes in response to user requests. The generation AI references a database of famous lines and scenes from movies, anime, and manga, and generates the most suitable lines and scenes based on the user's request. For example, if a user inputs "I want to recreate an emotional scene," the generation AI extracts an emotional scene from the database and generates a GIF or text based on it. Thus, the generation AI can generate the most suitable lines and scenes. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit inputs the user's request into the generation AI, which then generates the most suitable lines and scenes. Based on the user's request, the generation AI references a database of famous lines and scenes from movies, anime, and manga, and generates the most suitable lines and scenes. The generated lines and scenes are provided in GIF or text format. This allows the generation unit to use generation AI to generate optimal dialogue and scenes.

[0035] The service provider can provide the generated dialogue and scenes in GIF or text format. The service provider, for example, provides an interface for providing the generated dialogue and scenes to the user. The service provider provides the generated dialogue and scenes in GIF or text format. For example, the generated GIF can be sent to a friend, or the generated dialogue can be used in a social media post. This allows users to use the generated dialogue and scenes in various formats by providing them in GIF or text format. Some or all of the above processing in the service provider is performed using a generation AI. For example, the service provider inputs the generated dialogue and scenes into the generation AI, and the generation AI provides them in the most optimal format. The generation AI provides the generated dialogue and scenes in GIF or text format based on the user's request. This allows the service provider to provide the generated dialogue and scenes in GIF or text format using the generation AI.

[0036] The service provider may include a customization unit for customizing the generated dialogue and scenes. The service provider may, for example, provide an interface for customizing the generated dialogue and scenes to meet user needs. The customization unit customizes the generated dialogue and scenes. For example, users can change the color of the generated dialogue and scenes, edit the text, or adjust the layout. This allows for the provision of content tailored to user needs by customizing the generated dialogue and scenes. Some or all of the above-described processes in the customization unit are performed using a generation AI. For example, the customization unit inputs the generated dialogue and scenes into the generation AI, which then performs the optimal customization. The generation AI customizes the generated dialogue and scenes based on user requests. This allows the customization unit to customize the generated dialogue and scenes using the generation AI.

[0037] The provisioning unit may include a sharing unit that shares the generated dialogue and scenes on social media and messaging apps. The provisioning unit, for example, provides an interface for sharing the generated dialogue and scenes on social media and messaging apps. The sharing unit shares the generated dialogue and scenes on social media and messaging apps. For example, a user can send a generated GIF to a friend or use the generated dialogue in a social media post. This allows users to easily share the generated dialogue and scenes with others by sharing them on social media and messaging apps. Some or all of the above processing in the sharing unit is performed using a generation AI. For example, the sharing unit inputs the generated dialogue and scenes into the generation AI, which then provides the optimal sharing method. Based on the user's request, the generation AI shares the generated dialogue and scenes on social media and messaging apps. This allows the sharing unit to share the generated dialogue and scenes on social media and messaging apps using the generation AI.

[0038] The service provider may include an advertising department that utilizes the generated dialogue and scenes as advertisements. The service provider may, for example, provide an interface for using the generated dialogue and scenes as advertisements. The advertising department utilizes the generated dialogue and scenes as advertisements. For example, a company can create an advertisement using the generated dialogue and scenes and spread it on social media to increase product awareness. This allows companies to effectively advertise by utilizing the generated dialogue and scenes as advertisements. Some or all of the above processing in the advertising department is performed using a generation AI. For example, the advertising department inputs the generated dialogue and scenes into the generation AI, and the generation AI generates the optimal advertisement. The generation AI utilizes the generated dialogue and scenes as advertisements based on the user's request. This allows the advertising department to use the generated dialogue and scenes as advertisements using the generation AI.

[0039] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. It can also analyze patterns of dialogue and scenes previously entered by the user and suggest similar input methods. Furthermore, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. This allows the reception desk to suggest the optimal input method by analyzing the user's past input history. Some or all of the above processes in the reception desk are performed using AI. For example, the reception desk inputs the user's past input history into the AI, which then selects the optimal input method. This allows the reception desk to use AI to analyze the user's past input history and select the optimal input method.

[0040] The reception system can filter input based on the user's current areas of interest. For example, it can prioritize displaying relevant lines and scenes based on the genres of movies and anime the user has recently viewed. It can also analyze posts from accounts the user follows on social media and suggest relevant lines and scenes. Furthermore, it can filter relevant lines and scenes based on keywords the user has recently searched for. This allows the system to prioritize input that is highly relevant by filtering based on the user's current areas of interest. Some or all of the above processing in the reception system is performed using AI. For example, the reception system inputs the user's areas of interest into the AI, which then performs optimal filtering. This allows the reception system to filter based on the user's current areas of interest using AI.

[0041] The reception desk can prioritize accepting highly relevant inputs by considering the user's geographical location when receiving input. For example, if the user is in a specific region, the reception desk can prioritize suggesting lines and scenes from movies or anime related to that region. Similarly, if the user is traveling, the reception desk can prioritize suggesting lines and scenes related to their travel destination. Furthermore, if the user is participating in a specific event, the reception desk can prioritize suggesting lines and scenes related to that event. This allows the reception desk to prioritize accepting highly relevant inputs by considering the user's geographical location. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the user's geographical location into the AI, which then selects the most appropriate input. This allows the reception desk to prioritize accepting highly relevant inputs by considering the user's geographical location using AI.

[0042] The reception desk can analyze the user's social media activity and accept relevant input when receiving input. For example, the reception desk can suggest relevant lines and scenes based on the content the user frequently posts on social media. It can also analyze the content of posts from accounts the user follows on social media and suggest relevant lines and scenes. Furthermore, the reception desk can suggest relevant lines and scenes based on the activities of groups and communities the user participates in on social media. This allows the reception desk to prioritize the acceptance of relevant input by analyzing the user's social media activity. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the user's social media activity into the AI, and the AI ​​selects the optimal input. This allows the reception desk to use AI to analyze the user's social media activity and accept relevant input.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the input during the analysis. For example, the analysis unit performs a detailed analysis for important lines of dialogue or scenes. It can also perform a concise analysis for general lines of dialogue or scenes. Furthermore, it can perform a detailed analysis for lines of dialogue or scenes of particular interest to the user. This allows for more appropriate analysis results by adjusting the level of detail based on the importance of the input. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit inputs the importance of the input into the AI, which then determines the optimal level of detail. This allows the analysis unit to use AI to adjust the level of detail based on the importance of the input.

[0044] The analysis unit can apply different analysis algorithms depending on the input category during analysis. For example, in the case of movie dialogue or scenes, the analysis unit applies a movie-specific analysis algorithm. Similarly, in the case of anime dialogue or scenes, the analysis unit can apply an anime-specific analysis algorithm. Furthermore, in the case of manga dialogue or scenes, the analysis unit can apply a manga-specific analysis algorithm. This allows the analysis unit to provide more appropriate analysis results by applying different analysis algorithms depending on the input category. Some or all of the above-described processes in the analysis unit are performed using AI. For example, the analysis unit inputs the input category into the AI, and the AI ​​applies the most suitable analysis algorithm. This allows the analysis unit to use AI to apply different analysis algorithms depending on the input category.

[0045] The analysis unit can determine the priority of analysis based on the input submission date during the analysis process. For example, the analysis unit prioritizes the analysis of the most recent input. The analysis unit can also determine the priority of analysis based on a deadline specified by the user. Furthermore, the analysis unit can determine the priority of analysis by referring to the user's past input history. This allows for the provision of more appropriate analysis results by determining the priority of analysis based on the input submission date. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit inputs the input submission date into the AI, which then determines the optimal priority. This allows the analysis unit to use AI to determine the priority of analysis based on the input submission date.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the inputs during analysis. For example, the analysis unit prioritizes the analysis of highly relevant inputs. The analysis unit can also determine the order of analysis based on user-specified relevance. Furthermore, the analysis unit can prioritize the analysis of highly relevant inputs by referring to the user's past input history. By adjusting the order of analysis based on the relevance of the inputs, it is possible to provide more appropriate analysis results. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit inputs the relevance of the inputs into the AI, and the AI ​​determines the optimal order. In this way, the analysis unit can use AI to adjust the order of analysis based on the relevance of the inputs.

[0047] The generation unit can adjust the level of detail in the generated text based on the importance of the lines and scenes. For example, the generation unit will perform detailed generation for important lines and scenes. It can also perform concise generation for general lines and scenes. Furthermore, it can perform detailed generation for lines and scenes of particular interest to the user. By adjusting the level of detail in the generated text based on the importance of the lines and scenes, it can provide more appropriate generated results. Some or all of the above processing in the generation unit is performed using AI. For example, the generation unit inputs the importance of the lines and scenes into the AI, and the AI ​​determines the optimal level of detail. This allows the generation unit to use AI to adjust the level of detail in the generated text based on the importance of the lines and scenes.

[0048] The generation unit can apply different generation algorithms depending on the category of dialogue or scene during generation. For example, in the case of dialogue or scenes from a movie, the generation unit applies a generation algorithm specific to movies. Similarly, in the case of dialogue or scenes from anime, the generation unit can apply a generation algorithm specific to anime. Furthermore, in the case of dialogue or scenes from manga, the generation unit can apply a generation algorithm specific to manga. This allows for the application of different generation algorithms depending on the category of dialogue or scene, thereby providing more appropriate generation results. Some or all of the above-described processes in the generation unit are performed using AI. For example, the generation unit inputs the category of dialogue or scene into the AI, which then applies the optimal generation algorithm. This allows the generation unit to use AI to apply different generation algorithms depending on the category of dialogue or scene.

[0049] The generation unit can determine the generation priority based on the submission timing of lines and scenes during generation. For example, the generation unit will prioritize the generation of the most recent lines and scenes. The generation unit can also determine the generation priority based on deadlines specified by the user. Furthermore, the generation unit can determine the generation priority by referring to the user's past input history. This allows for more appropriate generation results by determining the generation priority based on the submission timing of lines and scenes. Some or all of the above processes in the generation unit are performed using AI. For example, the generation unit inputs the submission timing of lines and scenes into the AI, and the AI ​​determines the optimal priority. This allows the generation unit to use AI to determine the generation priority based on the submission timing of lines and scenes.

[0050] The generation unit can adjust the generation order based on the relevance of lines and scenes during generation. For example, the generation unit can prioritize generating lines and scenes with high relevance. The generation unit can also determine the generation order based on relevance specified by the user. Furthermore, the generation unit can prioritize generating lines and scenes with high relevance by referring to the user's past input history. By adjusting the generation order based on the relevance of lines and scenes, it can provide more appropriate generation results. Some or all of the above processing in the generation unit is performed using AI. For example, the generation unit inputs the relevance of lines and scenes into the AI, and the AI ​​determines the optimal order. In this way, the generation unit can use AI to adjust the generation order based on the relevance of lines and scenes.

[0051] The service delivery unit can select the optimal delivery method by referring to the user's past usage history at the time of delivery. For example, the service delivery unit will prioritize suggesting delivery methods (GIFs, text, etc.) that the user has frequently used in the past. The service delivery unit can also analyze patterns of dialogue and scenes used by the user in the past and suggest similar delivery methods. Furthermore, the service delivery unit can predict and suggest delivery methods to be used at specific times based on the user's past usage history. In this way, the service delivery unit can select the optimal delivery method by referring to the user's past usage history. Some or all of the above processing in the service delivery unit is performed using AI. For example, the service delivery unit inputs the user's past usage history into the AI, and the AI ​​selects the optimal delivery method. In this way, the service delivery unit can use AI to select the optimal delivery method by referring to the user's past usage history.

[0052] The service provider can customize the content offered based on the user's current areas of interest at the time of delivery. For example, it can prioritize displaying relevant lines and scenes based on the genres of movies and anime the user has recently viewed. It can also analyze the content of posts from accounts the user follows on social media and suggest relevant lines and scenes. Furthermore, it can customize relevant lines and scenes based on keywords the user has recently searched for. This allows the service provider to provide more appropriate results by customizing the content based on the user's current areas of interest. Some or all of the above processing in the service provider is performed using AI. For example, the service provider inputs the user's areas of interest into the AI, which then performs the optimal customization. This allows the service provider to use AI to customize the content offered based on the user's current areas of interest.

[0053] The service provider can select the optimal service 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 prioritize displaying lines and scenes from movies or anime related to that region. Furthermore, if the user is traveling, the service provider can prioritize displaying lines and scenes related to the travel destination. Additionally, if the user is participating in a specific event, the service provider can prioritize displaying lines and scenes related to that event. This allows the service provider to select the optimal service delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider is performed using AI. For example, the service provider inputs the user's geographical location information into the AI, which then selects the optimal service delivery method. This allows the service provider to use AI to select the optimal service delivery method by considering the user's geographical location information.

[0054] The service provider can analyze the user's social media activity and customize the content offered at the time of delivery. For example, the service provider can suggest relevant lines and scenes based on the content the user frequently posts on social media. It can also analyze the content of accounts the user follows on social media and suggest relevant lines and scenes. Furthermore, the service provider can suggest relevant lines and scenes based on the activities of groups and communities the user participates in on social media. In this way, by analyzing the user's social media activity, more appropriate content can be provided. Some or all of the above processing in the service provider is performed using AI. For example, the service provider inputs the user's social media activity into the AI, and the AI ​​customizes the optimal content. In this way, the service provider can use AI to analyze the user's social media activity and customize the content offered.

[0055] The customization unit can select the optimal customization method by referring to the user's past customization history during the customization process. For example, the customization unit can prioritize suggesting customization methods that the user has frequently used in the past. It can also analyze patterns of dialogue and scenes that the user has customized in the past and suggest similar customization methods. Furthermore, the customization unit can predict and suggest customization methods to be used at specific times based on the user's past customization history. This allows the customization unit to select the optimal customization method by referring to the user's past customization history. Some or all of the above processes in the customization unit are performed using AI. For example, the customization unit inputs the user's past customization history into the AI, which then selects the optimal customization method. This allows the customization unit to select the optimal customization method by referring to the user's past customization history using AI.

[0056] The customization section can select the optimal customization method by considering the user's geographical location during the customization process. For example, if the user is in a specific region, the customization section can prioritize displaying lines and scenes from movies and anime related to that region. Furthermore, if the user is traveling, the customization section can prioritize displaying lines and scenes related to the travel destination. Additionally, if the user is participating in a specific event, the customization section can prioritize displaying lines and scenes related to that event. This allows the customization section to select the optimal customization method by considering the user's geographical location. Some or all of the above processing in the customization section is performed using AI. For example, the customization section inputs the user's geographical location information into the AI, which then selects the optimal customization method. This allows the customization section to use AI to select the optimal customization method by considering the user's geographical location.

[0057] The sharing function can select the optimal sharing method by referring to the user's past sharing history when sharing. For example, the sharing function prioritizes suggesting sharing methods that the user has frequently used in the past (such as social media or messaging apps). It can also analyze patterns of dialogue and scenes that the user has shared in the past and suggest similar sharing methods. Furthermore, the sharing function can predict and suggest sharing methods to be used at specific times based on the user's past sharing history. In this way, the optimal sharing method can be selected by referring to the user's past sharing history. Some or all of the above processing in the sharing function is performed using AI. For example, the sharing function inputs the user's past sharing history into the AI, and the AI ​​selects the optimal sharing method. In this way, the sharing function can use AI to refer to the user's past sharing history and select the optimal sharing method.

[0058] The sharing function can select the optimal sharing method by considering the user's geographical location when sharing. For example, if the user is in a specific region, the sharing function can prioritize displaying lines and scenes from movies or anime related to that region. Furthermore, if the user is traveling, the sharing function can prioritize displaying lines and scenes related to their travel destination. Additionally, if the user is participating in a specific event, the sharing function can prioritize displaying lines and scenes related to that event. This allows the sharing function to select the optimal sharing method by considering the user's geographical location. Some or all of the above processing in the sharing function is performed using AI. For example, the sharing function inputs the user's geographical location information into the AI, which then selects the optimal sharing method. This allows the sharing function to use AI to select the optimal sharing method by considering the user's geographical location.

[0059] The advertising department can select the most suitable advertisement by referring to the user's past ad viewing history when displaying an advertisement. For example, the advertising department can analyze patterns of advertisements that the user has frequently viewed in the past and suggest similar advertisements. The advertising department can also suggest relevant advertisements based on the user's past click history. Furthermore, the advertising department can predict and suggest advertisements to display at specific times based on the user's past ad viewing history. In this way, the advertising department can select the most suitable advertisement by referring to the user's past ad viewing history. Some or all of the above processes in the advertising department are performed using AI. For example, the advertising department inputs the user's past ad viewing history into the AI, and the AI ​​selects the most suitable advertisement. In this way, the advertising department can use AI to select the most suitable advertisement by referring to the user's past ad viewing history.

[0060] The advertising department can select the most relevant advertisements by considering the user's geographical location when displaying ads. For example, if a user is in a specific region, the advertising department can prioritize displaying ads related to that region. Furthermore, if a user is traveling, the advertising department can prioritize displaying ads related to their travel destination. Additionally, if a user is participating in a specific event, the advertising department can prioritize displaying ads related to that event. This allows the advertising department to select the most relevant advertisements by considering the user's geographical location. Some or all of the above processes in the advertising department are performed using AI. For example, the advertising department inputs the user's geographical location information into the AI, which then selects the most relevant advertisements. This allows the advertising department to use AI to select the most relevant advertisements by considering the user's geographical location.

[0061] The advertising department can analyze a user's social media activity when displaying an ad to select the most relevant ad. For example, the advertising department can suggest relevant ads based on the content a user frequently posts on social media. It can also analyze the content of accounts a user follows on social media and suggest relevant ads. Furthermore, the advertising department can suggest relevant ads based on the activities of groups and communities a user participates in on social media. This allows the advertising department to provide more appropriate ads by analyzing the user's social media activity. Some or all of the above processes in the advertising department are performed using AI. For example, the advertising department inputs the user's social media activity into the AI, which then selects the most relevant ad. This allows the advertising department to use AI to analyze the user's social media activity and select the most relevant ad.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The reception desk can suggest the optimal input method by referring to the user's past input history when receiving user input. For example, it can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also analyze patterns of dialogue and scenes that the user has previously entered and suggest similar input methods. Furthermore, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. In this way, the reception desk can suggest the optimal input method by analyzing the user's past input history. Some or all of the above processes in the reception desk are performed using AI. For example, the reception desk inputs the user's past input history into the AI, and the AI ​​selects the optimal input method. In this way, the reception desk can use AI to analyze the user's past input history and select the optimal input method.

[0064] The generation unit can adjust the level of detail in the generated text based on the importance of the lines and scenes. For example, it can generate detailed text for important lines and scenes, and concise text for general lines and scenes. Furthermore, it can generate detailed text for lines and scenes of particular interest to the user. By adjusting the level of detail based on the importance of the lines and scenes, it can provide more appropriate generated results. Some or all of the above processing in the generation unit is performed using AI. For example, the generation unit inputs the importance of the lines and scenes into the AI, which then determines the optimal level of detail. This allows the generation unit to use AI to adjust the level of detail in the generated text based on the importance of the lines and scenes.

[0065] The reception desk can filter input based on the user's current areas of interest. For example, it can prioritize displaying relevant lines and scenes based on the genres of movies and anime the user has recently viewed. The reception desk can also analyze the content of posts from accounts the user follows on social media and suggest relevant lines and scenes. Furthermore, the reception desk can filter relevant lines and scenes based on keywords the user has recently searched for. This allows the reception desk to prioritize receiving highly relevant input by filtering based on the user's current areas of interest. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the user's areas of interest into the AI, which then performs optimal filtering. This allows the reception desk to filter based on the user's current areas of interest using AI.

[0066] The analysis unit can adjust the level of detail of the analysis based on the importance of the input. For example, it can perform a detailed analysis for important lines of dialogue or scenes, and a concise analysis for general lines of dialogue or scenes. Furthermore, it can perform a detailed analysis for lines of dialogue or scenes of particular interest to the user. By adjusting the level of detail of the analysis based on the importance of the input, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit inputs the importance of the input into the AI, and the AI ​​determines the optimal level of detail. This allows the analysis unit to use the AI ​​to adjust the level of detail of the analysis based on the importance of the input.

[0067] The service delivery unit can select the optimal delivery method by referring to the user's past usage history at the time of delivery. For example, it can prioritize suggesting delivery methods (GIFs, text, etc.) that the user has frequently used in the past. The service delivery unit can also analyze patterns of dialogue and scenes used by the user in the past and suggest similar delivery methods. Furthermore, the service delivery unit can predict and suggest delivery methods to be used at specific times based on the user's past usage history. In this way, the service delivery unit can select the optimal delivery method by referring to the user's past usage history. Some or all of the above processing in the service delivery unit is performed using AI. For example, the service delivery unit inputs the user's past usage history into the AI, and the AI ​​selects the optimal delivery method. In this way, the service delivery unit can use AI to select the optimal delivery method by referring to the user's past usage history.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The reception desk receives user input. User input includes requests for famous lines or scenes they want to use. The reception desk provides an interface for users to input the famous lines or scenes they want to use in messaging apps or social media, and sends the user input to the generation AI. Step 2: The analysis unit uses a generation AI to analyze the information received by the reception unit. Based on the user's request, the analysis unit refers to a database of famous lines and scenes from movies, anime, and manga, and extracts relevant information. Step 3: The generation unit generates the optimal dialogue and scenes based on the information analyzed by the analysis unit. The generation unit uses a generation AI to generate dialogue and scenes according to the user's request, and the generated dialogue and scenes are provided in GIF or text format. Step 4: The provider unit provides the dialogue and scenes generated by the generator unit. The provider unit provides an interface for providing the generated dialogue and scenes to the user and includes functions for sharing on social media and messaging apps.

[0070] (Example of form 2) The famous quote / scene generation system according to an embodiment of the present invention is a system that utilizes data on famous quotes and scenes from movies, anime, and manga, and uses a generation AI to allow users to incorporate quotes and scenes from their favorite works into their daily communication and content. In this system, the user inputs a famous quote or scene they want to use in a messaging app or social media, and the generation AI analyzes the input and generates the most suitable quote or scene. The generated quote or scene is provided in GIF or text format, and the user can use it in their daily communication. For example, the user inputs a famous quote or scene they want to use in a messaging app or social media. For example, they input a specific request such as "I want to use this quote" or "I want to recreate this scene." This information is input to the generation AI. Next, the generation AI analyzes the input information. The generation AI refers to a database of famous quotes and scenes from movies, anime, and manga, and generates the most suitable quote or scene for the user's request. For example, if the user inputs "I want to recreate an emotional scene," the generation AI extracts an emotional scene from the database and generates a GIF or text based on it. The generated lines and scenes are provided in GIF and text formats. Users can use these in messaging apps and social media. For example, they can send generated GIFs to friends or use generated lines in social media posts. This system allows users to incorporate lines and scenes from their favorite works into their daily communication. This enriches communication and allows for the sharing of emotions. For example, sending a GIF recreating a famous scene from a movie to a friend can create a common topic and deepen communication. This system is also very appealing to fans of movies, anime, and manga. Because they can freely customize and use lines and scenes from their favorite works, it encourages interaction among fans. For example, they can share lines from their favorite works on social media and share emotions with other fans. Furthermore, this system is also beneficial for businesses. Companies can use this system to promote their products and services.For example, creating advertisements using famous movie scenes and spreading them on social media can increase product awareness. In this way, AI-powered systems for generating famous lines and scenes enrich user communication and provide a new means of sharing emotions. Thus, these famous line and scene generation systems can enrich user communication and facilitate the sharing of emotions.

[0071] The famous quote / scene generation system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user input. User input includes, for example, requests for famous quotes or scenes to be used, but is not limited to such examples. The reception unit provides, for example, an interface for the user to input famous quotes or scenes to be used in messaging apps or social networking services. The reception unit can also send the user input to the generation AI. The analysis unit uses the generation AI to analyze the information received by the reception unit. The analysis unit, for example, refers to a database of famous quotes and scenes from movies, anime, and manga based on the user's request. The generation AI extracts relevant information from the database in order to generate the best quotes and scenes for the user's request. The generation unit generates the best quotes and scenes based on the information analyzed by the analysis unit. The generation unit uses, for example, the generation AI to generate quotes and scenes in response to the user's request. The generated quotes and scenes are provided in GIF or text format. The providing unit provides the lines and scenes generated by the generating unit. The providing unit provides, for example, an interface for providing the generated lines and scenes to the user. The providing unit also has a function for sharing the generated lines and scenes on social networking services (SNS) or messaging apps. As a result, the famous lines and famous scenes generation system according to the embodiment can analyze user input and generate and provide the most suitable lines and scenes. For example, the receiving unit provides an interface for the user to input famous lines and scenes they want to use on messaging apps or SNS. The analysis unit uses a generation AI to analyze the information received by the receiving unit. The generation unit generates the most suitable lines and scenes based on the information analyzed by the analysis unit. The providing unit provides the lines and scenes generated by the generation unit. As a result, the famous lines and famous scenes generation system according to the embodiment can analyze user input and generate and provide the most suitable lines and scenes.

[0072] The reception desk receives user input. User input may include, but is not limited to, requests for famous lines or scenes that users wish to use. The reception desk provides an interface for users to input famous lines or scenes they wish to use in messaging apps or social media. Specifically, the reception desk designs an intuitive and user-friendly interface so that users can easily input requests. For example, it provides an interface with text boxes, dropdown menus, and voice input functions so that users can input details of the lines or scenes they wish to use. The reception desk can also send user input to a generating AI. The data sent to the generating AI includes the user's request and related metadata (e.g., the date and time of the request, user profile information, etc.). This allows the generating AI to obtain information to generate lines or scenes that best suit the user's needs. Furthermore, the reception desk has the ability to check user input in real time and prompt for corrections or additional information as needed. For example, if the user's request is unclear or additional information is needed, it displays pop-up messages and guidelines to help users input accurate requests. This allows the reception desk to efficiently and accurately receive user input and provide the necessary information for the generating AI.

[0073] The analysis unit uses generative AI to analyze the information received by the reception unit. For example, based on a user's request, the analysis unit consults a database of famous lines and scenes from movies, anime, and manga. Specifically, the analysis unit uses natural language processing technology to analyze the user's request and understand its content. The generative AI extracts relevant information from the database to generate the most suitable lines and scenes for the user's request. For example, if a user requests an "emotional scene," the analysis unit searches the database for lines and scenes related to emotional scenes and extracts the most suitable ones. Furthermore, the analysis unit generates multiple candidates in response to the user's request and evaluates the relevance of each candidate. The evaluation uses an algorithm that the generative AI has learned based on past data and user feedback. This allows the analysis unit to identify the most suitable lines and scenes for the user's request and provide them to the generation unit. In addition, the analysis unit utilizes database indexing and caching functions to improve the response speed to user requests. This allows the analysis unit to quickly and accurately analyze user requests and provide information to generate optimal dialogue and scenes.

[0074] The generation unit generates optimal dialogue and scenes based on the information analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate dialogue and scenes that meet user requests. Specifically, the generation AI uses natural language generation technology to generate the most suitable dialogue and scenes for user requests. For example, if a user requests an "emotional scene," the generation AI generates new dialogue and scenes based on information related to emotional scenes extracted from a database. The generated dialogue and scenes are provided in GIF or text format. The generation unit evaluates the quality of the generated dialogue and scenes and makes corrections or improvements as needed. For example, if the generated dialogue and scenes do not perfectly match the user's request, the generation AI runs the generation process again to generate more suitable dialogue and scenes. The generation unit also has a function to review and edit the content before providing the generated dialogue and scenes to the user. This allows the generation unit to generate and provide high-quality dialogue and scenes that are optimal for user requests. Furthermore, the generation unit regularly updates the AI's training data to reflect the latest information, thereby improving the efficiency of the generation process. This allows the generation unit to consistently generate and provide users with high-quality dialogue and scenes based on the most up-to-date information.

[0075] The provider unit provides the dialogue and scenes generated by the generation unit. For example, the provider unit provides an interface for providing the generated dialogue and scenes to the user. Specifically, the provider unit designs an intuitive and user-friendly interface so that users can easily view, save, and share the generated dialogue and scenes. For example, it provides an interface with a dedicated viewer for displaying the generated dialogue and scenes, and buttons for sharing on social media and messaging apps. The provider unit also includes functionality for sharing the generated dialogue and scenes on social media and messaging apps. For example, it provides a function that allows users to easily generate a link and post when sharing generated dialogue and scenes on social media. Furthermore, the provider unit also has the functionality to collect user feedback and provide it to the generation and analysis units. This allows the provider unit to provide optimal dialogue and scenes that meet user needs and improve the overall system quality. Additionally, the provider unit includes functions for saving and managing generated dialogue and scenes, allowing users to easily reuse previously generated dialogue and scenes. This allows the service provider to deliver generated dialogue and scenes to users quickly and reliably, thereby improving user satisfaction.

[0076] The generation unit uses a generation AI to reference a database of famous lines and scenes from movies, anime, and manga, and generate the most suitable lines and scenes. For example, the generation unit uses the generation AI to generate lines and scenes in response to user requests. The generation AI references a database of famous lines and scenes from movies, anime, and manga, and generates the most suitable lines and scenes based on the user's request. For example, if a user inputs "I want to recreate an emotional scene," the generation AI extracts an emotional scene from the database and generates a GIF or text based on it. Thus, the generation AI can generate the most suitable lines and scenes. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit inputs the user's request into the generation AI, which then generates the most suitable lines and scenes. Based on the user's request, the generation AI references a database of famous lines and scenes from movies, anime, and manga, and generates the most suitable lines and scenes. The generated lines and scenes are provided in GIF or text format. This allows the generation unit to use generation AI to generate optimal dialogue and scenes.

[0077] The service provider can provide the generated dialogue and scenes in GIF or text format. The service provider, for example, provides an interface for providing the generated dialogue and scenes to the user. The service provider provides the generated dialogue and scenes in GIF or text format. For example, the generated GIF can be sent to a friend, or the generated dialogue can be used in a social media post. This allows users to use the generated dialogue and scenes in various formats by providing them in GIF or text format. Some or all of the above processing in the service provider is performed using a generation AI. For example, the service provider inputs the generated dialogue and scenes into the generation AI, and the generation AI provides them in the most optimal format. The generation AI provides the generated dialogue and scenes in GIF or text format based on the user's request. This allows the service provider to provide the generated dialogue and scenes in GIF or text format using the generation AI.

[0078] The service provider may include a customization unit for customizing the generated dialogue and scenes. The service provider may, for example, provide an interface for customizing the generated dialogue and scenes to meet user needs. The customization unit customizes the generated dialogue and scenes. For example, users can change the color of the generated dialogue and scenes, edit the text, or adjust the layout. This allows for the provision of content tailored to user needs by customizing the generated dialogue and scenes. Some or all of the above-described processes in the customization unit are performed using a generation AI. For example, the customization unit inputs the generated dialogue and scenes into the generation AI, which then performs the optimal customization. The generation AI customizes the generated dialogue and scenes based on user requests. This allows the customization unit to customize the generated dialogue and scenes using the generation AI.

[0079] The provisioning unit may include a sharing unit that shares the generated dialogue and scenes on social media and messaging apps. The provisioning unit, for example, provides an interface for sharing the generated dialogue and scenes on social media and messaging apps. The sharing unit shares the generated dialogue and scenes on social media and messaging apps. For example, a user can send a generated GIF to a friend or use the generated dialogue in a social media post. This allows users to easily share the generated dialogue and scenes with others by sharing them on social media and messaging apps. Some or all of the above processing in the sharing unit is performed using a generation AI. For example, the sharing unit inputs the generated dialogue and scenes into the generation AI, which then provides the optimal sharing method. Based on the user's request, the generation AI shares the generated dialogue and scenes on social media and messaging apps. This allows the sharing unit to share the generated dialogue and scenes on social media and messaging apps using the generation AI.

[0080] The service provider may include an advertising department that utilizes the generated dialogue and scenes as advertisements. The service provider may, for example, provide an interface for using the generated dialogue and scenes as advertisements. The advertising department utilizes the generated dialogue and scenes as advertisements. For example, a company can create an advertisement using the generated dialogue and scenes and spread it on social media to increase product awareness. This allows companies to effectively advertise by utilizing the generated dialogue and scenes as advertisements. Some or all of the above processing in the advertising department is performed using a generation AI. For example, the advertising department inputs the generated dialogue and scenes into the generation AI, and the generation AI generates the optimal advertisement. The generation AI utilizes the generated dialogue and scenes as advertisements based on the user's request. This allows the advertising department to use the generated dialogue and scenes as advertisements using the generation AI.

[0081] The reception unit can estimate the user's emotions and adjust the timing of input acceptance based on the estimated emotions. For example, if the user is excited, the reception unit can provide an interface that accepts input immediately. Alternatively, if the user is relaxed, the reception unit can provide an interface that accepts input at a slower pace. Furthermore, if the user is stressed, the reception unit can provide a concise and intuitive input interface. This allows for more appropriate timing of input acceptance by adjusting the timing 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 reception unit is performed using generative AI. For example, the reception unit inputs the user's emotion data into the generative AI, which accepts input at the optimal timing. This allows the reception unit to adjust the timing of input acceptance based on the user's emotions using the generative AI.

[0082] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. It can also analyze patterns of dialogue and scenes previously entered by the user and suggest similar input methods. Furthermore, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. This allows the reception desk to suggest the optimal input method by analyzing the user's past input history. Some or all of the above processes in the reception desk are performed using AI. For example, the reception desk inputs the user's past input history into the AI, which then selects the optimal input method. This allows the reception desk to use AI to analyze the user's past input history and select the optimal input method.

[0083] The reception system can filter input based on the user's current areas of interest. For example, it can prioritize displaying relevant lines and scenes based on the genres of movies and anime the user has recently viewed. It can also analyze posts from accounts the user follows on social media and suggest relevant lines and scenes. Furthermore, it can filter relevant lines and scenes based on keywords the user has recently searched for. This allows the system to prioritize input that is highly relevant by filtering based on the user's current areas of interest. Some or all of the above processing in the reception system is performed using AI. For example, the reception system inputs the user's areas of interest into the AI, which then performs optimal filtering. This allows the reception system to filter based on the user's current areas of interest using AI.

[0084] The reception unit can estimate the user's emotions and determine the priority of inputs to be received based on the estimated emotions. For example, if the user is excited, the reception unit will prioritize receiving emotionally moving lines or scenes. Similarly, if the user is relaxed, it can prioritize receiving humorous lines or scenes. Furthermore, if the user is stressed, it can prioritize receiving soothing lines or scenes. This allows the reception unit to prioritize more appropriate inputs by determining the priority of inputs 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 include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit is performed using generative AI. For example, the reception unit inputs the user's emotion data into the generative AI, which then determines the optimal priority. This allows the reception unit to use the generative AI to determine the priority of inputs based on the user's emotions.

[0085] The reception desk can prioritize accepting highly relevant inputs by considering the user's geographical location when receiving input. For example, if the user is in a specific region, the reception desk can prioritize suggesting lines and scenes from movies or anime related to that region. Similarly, if the user is traveling, the reception desk can prioritize suggesting lines and scenes related to their travel destination. Furthermore, if the user is participating in a specific event, the reception desk can prioritize suggesting lines and scenes related to that event. This allows the reception desk to prioritize accepting highly relevant inputs by considering the user's geographical location. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the user's geographical location into the AI, which then selects the most appropriate input. This allows the reception desk to prioritize accepting highly relevant inputs by considering the user's geographical location using AI.

[0086] The reception desk can analyze the user's social media activity and accept relevant input when receiving input. For example, the reception desk can suggest relevant lines and scenes based on the content the user frequently posts on social media. It can also analyze the content of posts from accounts the user follows on social media and suggest relevant lines and scenes. Furthermore, the reception desk can suggest relevant lines and scenes based on the activities of groups and communities the user participates in on social media. This allows the reception desk to prioritize the acceptance of relevant input by analyzing the user's social media activity. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the user's social media activity into the AI, and the AI ​​selects the optimal input. This allows the reception desk to use AI to analyze the user's social media activity and accept relevant input.

[0087] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit inputs the user's emotion data into the generative AI, and the generative AI determines the optimal presentation method. This allows the analysis unit to adjust the presentation of the analysis based on the user's emotions using the generative AI.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the input during the analysis. For example, the analysis unit performs a detailed analysis for important lines of dialogue or scenes. It can also perform a concise analysis for general lines of dialogue or scenes. Furthermore, it can perform a detailed analysis for lines of dialogue or scenes of particular interest to the user. This allows for more appropriate analysis results by adjusting the level of detail based on the importance of the input. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit inputs the importance of the input into the AI, which then determines the optimal level of detail. This allows the analysis unit to use AI to adjust the level of detail based on the importance of the input.

[0089] The analysis unit can apply different analysis algorithms depending on the input category during analysis. For example, in the case of movie dialogue or scenes, the analysis unit applies a movie-specific analysis algorithm. Similarly, in the case of anime dialogue or scenes, the analysis unit can apply an anime-specific analysis algorithm. Furthermore, in the case of manga dialogue or scenes, the analysis unit can apply a manga-specific analysis algorithm. This allows the analysis unit to provide more appropriate analysis results by applying different analysis algorithms depending on the input category. Some or all of the above-described processes in the analysis unit are performed using AI. For example, the analysis unit inputs the input category into the AI, and the AI ​​applies the most suitable analysis algorithm. This allows the analysis unit to use AI to apply different analysis algorithms depending on the input category.

[0090] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit inputs the user's emotion data into the generative AI, and the generative AI determines the optimal length. This allows the analysis unit to use the generative AI to adjust the length of the analysis based on the user's emotions.

[0091] The analysis unit can determine the priority of analysis based on the input submission date during the analysis process. For example, the analysis unit prioritizes the analysis of the most recent input. The analysis unit can also determine the priority of analysis based on a deadline specified by the user. Furthermore, the analysis unit can determine the priority of analysis by referring to the user's past input history. This allows for the provision of more appropriate analysis results by determining the priority of analysis based on the input submission date. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit inputs the input submission date into the AI, which then determines the optimal priority. This allows the analysis unit to use AI to determine the priority of analysis based on the input submission date.

[0092] The analysis unit can adjust the order of analysis based on the relevance of the inputs during analysis. For example, the analysis unit prioritizes the analysis of highly relevant inputs. The analysis unit can also determine the order of analysis based on user-specified relevance. Furthermore, the analysis unit can prioritize the analysis of highly relevant inputs by referring to the user's past input history. By adjusting the order of analysis based on the relevance of the inputs, it is possible to provide more appropriate analysis results. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit inputs the relevance of the inputs into the AI, and the AI ​​determines the optimal order. In this way, the analysis unit can use AI to adjust the order of analysis based on the relevance of the inputs.

[0093] The generation unit can estimate the user's emotions and adjust the expression of the dialogue and scenes it generates based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate dialogue and scenes in a calm tone. If the user is excited, the generation unit can also generate dialogue and scenes in an energetic tone. Furthermore, if the user is sad, the generation unit can generate dialogue and scenes in an emotional tone. By adjusting the expression of the dialogue and scenes generated according to the user's emotions, more appropriate generation results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's emotion data into the generation AI, and the generation AI determines the optimal expression method. This allows the generation unit to use the generation AI to adjust the expression of the dialogue and scenes it generates based on the user's emotions.

[0094] The generation unit can adjust the level of detail in the generated text based on the importance of the lines and scenes. For example, the generation unit will perform detailed generation for important lines and scenes. It can also perform concise generation for general lines and scenes. Furthermore, it can perform detailed generation for lines and scenes of particular interest to the user. By adjusting the level of detail in the generated text based on the importance of the lines and scenes, it can provide more appropriate generated results. Some or all of the above processing in the generation unit is performed using AI. For example, the generation unit inputs the importance of the lines and scenes into the AI, and the AI ​​determines the optimal level of detail. This allows the generation unit to use AI to adjust the level of detail in the generated text based on the importance of the lines and scenes.

[0095] The generation unit can apply different generation algorithms depending on the category of dialogue or scene during generation. For example, in the case of dialogue or scenes from a movie, the generation unit applies a generation algorithm specific to movies. Similarly, in the case of dialogue or scenes from anime, the generation unit can apply a generation algorithm specific to anime. Furthermore, in the case of dialogue or scenes from manga, the generation unit can apply a generation algorithm specific to manga. This allows for the application of different generation algorithms depending on the category of dialogue or scene, thereby providing more appropriate generation results. Some or all of the above-described processes in the generation unit are performed using AI. For example, the generation unit inputs the category of dialogue or scene into the AI, which then applies the optimal generation algorithm. This allows the generation unit to use AI to apply different generation algorithms depending on the category of dialogue or scene.

[0096] The generation unit can estimate the user's emotions and adjust the length of the lines and scenes it generates based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise lines and scenes. If the user is relaxed, the generation unit can also generate detailed lines and scenes. Furthermore, if the user is excited, the generation unit can generate visually stimulating lines and scenes. By adjusting the length of the lines and scenes generated according to the user's emotions, more appropriate generation results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's emotion data into the generation AI, and the generation AI determines the optimal length. This allows the generation unit to use the generation AI to adjust the length of the lines and scenes it generates based on the user's emotions.

[0097] The generation unit can determine the generation priority based on the submission timing of lines and scenes during generation. For example, the generation unit will prioritize the generation of the most recent lines and scenes. The generation unit can also determine the generation priority based on deadlines specified by the user. Furthermore, the generation unit can determine the generation priority by referring to the user's past input history. This allows for more appropriate generation results by determining the generation priority based on the submission timing of lines and scenes. Some or all of the above processes in the generation unit are performed using AI. For example, the generation unit inputs the submission timing of lines and scenes into the AI, and the AI ​​determines the optimal priority. This allows the generation unit to use AI to determine the generation priority based on the submission timing of lines and scenes.

[0098] The generation unit can adjust the generation order based on the relevance of lines and scenes during generation. For example, the generation unit can prioritize generating lines and scenes with high relevance. The generation unit can also determine the generation order based on relevance specified by the user. Furthermore, the generation unit can prioritize generating lines and scenes with high relevance by referring to the user's past input history. By adjusting the generation order based on the relevance of lines and scenes, it can provide more appropriate generation results. Some or all of the above processing in the generation unit is performed using AI. For example, the generation unit inputs the relevance of lines and scenes into the AI, and the AI ​​determines the optimal order. In this way, the generation unit can use AI to adjust the generation order based on the relevance of lines and scenes.

[0099] The service provider can estimate the user's emotions and adjust the display method of the lines and scenes based on the estimated emotions. For example, if the user is relaxed, the service provider will display in a calm tone. If the user is excited, the service provider can also display in an energetic tone. Furthermore, if the user is sad, the service provider can display in an emotional tone. By adjusting the display method of the lines and scenes according to the user's emotions, a more appropriate display result can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 service provider is performed using the generative AI. For example, the service provider inputs the user's emotion data into the generative AI, and the generative AI determines the optimal display method. This allows the service provider to use the generative AI to adjust the display method of the lines and scenes based on the user's emotions.

[0100] The service delivery unit can select the optimal delivery method by referring to the user's past usage history at the time of delivery. For example, the service delivery unit will prioritize suggesting delivery methods (GIFs, text, etc.) that the user has frequently used in the past. The service delivery unit can also analyze patterns of dialogue and scenes used by the user in the past and suggest similar delivery methods. Furthermore, the service delivery unit can predict and suggest delivery methods to be used at specific times based on the user's past usage history. In this way, the service delivery unit can select the optimal delivery method by referring to the user's past usage history. Some or all of the above processing in the service delivery unit is performed using AI. For example, the service delivery unit inputs the user's past usage history into the AI, and the AI ​​selects the optimal delivery method. In this way, the service delivery unit can use AI to select the optimal delivery method by referring to the user's past usage history.

[0101] The service provider can customize the content offered based on the user's current areas of interest at the time of delivery. For example, it can prioritize displaying relevant lines and scenes based on the genres of movies and anime the user has recently viewed. It can also analyze the content of posts from accounts the user follows on social media and suggest relevant lines and scenes. Furthermore, it can customize relevant lines and scenes based on keywords the user has recently searched for. This allows the service provider to provide more appropriate results by customizing the content based on the user's current areas of interest. Some or all of the above processing in the service provider is performed using AI. For example, the service provider inputs the user's areas of interest into the AI, which then performs the optimal customization. This allows the service provider to use AI to customize the content offered based on the user's current areas of interest.

[0102] The service provider can estimate the user's emotions and determine the priority of lines and scenes to deliver based on the estimated emotions. For example, if the user is excited, the service provider will prioritize emotionally moving lines and scenes. Similarly, if the user is relaxed, the service provider can prioritize humorous lines and scenes. Furthermore, if the user is stressed, the service provider can prioritize soothing lines and scenes. This allows for more appropriate delivery results by prioritizing lines and scenes 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 include, 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 is performed using generative AI. For example, the service provider inputs user emotion data into the generative AI, which then determines the optimal priority. This allows the service provider to use the generative AI to determine the priority of lines and scenes to deliver based on the user's emotions.

[0103] The service provider can select the optimal service 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 prioritize displaying lines and scenes from movies or anime related to that region. Furthermore, if the user is traveling, the service provider can prioritize displaying lines and scenes related to the travel destination. Additionally, if the user is participating in a specific event, the service provider can prioritize displaying lines and scenes related to that event. This allows the service provider to select the optimal service delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider is performed using AI. For example, the service provider inputs the user's geographical location information into the AI, which then selects the optimal service delivery method. This allows the service provider to use AI to select the optimal service delivery method by considering the user's geographical location information.

[0104] The service provider can analyze the user's social media activity and customize the content offered at the time of delivery. For example, the service provider can suggest relevant lines and scenes based on the content the user frequently posts on social media. It can also analyze the content of accounts the user follows on social media and suggest relevant lines and scenes. Furthermore, the service provider can suggest relevant lines and scenes based on the activities of groups and communities the user participates in on social media. In this way, by analyzing the user's social media activity, more appropriate content can be provided. Some or all of the above processing in the service provider is performed using AI. For example, the service provider inputs the user's social media activity into the AI, and the AI ​​customizes the optimal content. In this way, the service provider can use AI to analyze the user's social media activity and customize the content offered.

[0105] The customization unit can estimate the user's emotions and adjust the customization method based on the estimated emotions. For example, if the user is relaxed, the customization unit will customize in a calm tone. If the user is excited, the customization unit can customize in an energetic tone. Furthermore, if the user is sad, the customization unit can customize in an emotional tone. By adjusting the customization method according to the user's emotions, a more appropriate customization result can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit is performed using generative AI. For example, the customization unit inputs the user's emotion data into the generative AI, and the generative AI determines the optimal customization method. This allows the customization unit to adjust the customization method based on the user's emotions using the generative AI.

[0106] The customization unit can select the optimal customization method by referring to the user's past customization history during the customization process. For example, the customization unit can prioritize suggesting customization methods that the user has frequently used in the past. It can also analyze patterns of dialogue and scenes that the user has customized in the past and suggest similar customization methods. Furthermore, the customization unit can predict and suggest customization methods to be used at specific times based on the user's past customization history. This allows the customization unit to select the optimal customization method by referring to the user's past customization history. Some or all of the above processes in the customization unit are performed using AI. For example, the customization unit inputs the user's past customization history into the AI, which then selects the optimal customization method. This allows the customization unit to select the optimal customization method by referring to the user's past customization history using AI.

[0107] The customization unit can estimate the user's emotions and determine customization priorities based on those emotions. For example, if the user is excited, the customization unit will prioritize emotionally moving customizations. It can also prioritize humorous customizations if the user is relaxed. Furthermore, if the user is stressed, it can prioritize soothing customizations. This allows for more appropriate customization results by prioritizing customizations 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 include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the customization unit are performed using generative AI. For example, the customization unit inputs user emotion data into the generative AI, which then determines the optimal priority. This allows the customization unit to use the generative AI to determine customization priorities based on the user's emotions.

[0108] The customization section can select the optimal customization method by considering the user's geographical location during the customization process. For example, if the user is in a specific region, the customization section can prioritize displaying lines and scenes from movies and anime related to that region. Furthermore, if the user is traveling, the customization section can prioritize displaying lines and scenes related to the travel destination. Additionally, if the user is participating in a specific event, the customization section can prioritize displaying lines and scenes related to that event. This allows the customization section to select the optimal customization method by considering the user's geographical location. Some or all of the above processing in the customization section is performed using AI. For example, the customization section inputs the user's geographical location information into the AI, which then selects the optimal customization method. This allows the customization section to use AI to select the optimal customization method by considering the user's geographical location.

[0109] The sharing function can estimate the user's emotions and adjust the sharing method based on the estimated emotions. For example, if the user is relaxed, the sharing function will share in a calm tone. If the user is excited, it can also share in an energetic tone. Furthermore, if the user is sad, it can share in an emotional tone. By adjusting the sharing method according to the user's emotions, a more appropriate sharing result can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing function is performed using generative AI. For example, the sharing function inputs the user's emotion data into the generative AI, and the generative AI determines the optimal sharing method. This allows the sharing function to adjust the sharing method based on the user's emotions using the generative AI.

[0110] The sharing function can select the optimal sharing method by referring to the user's past sharing history when sharing. For example, the sharing function prioritizes suggesting sharing methods that the user has frequently used in the past (such as social media or messaging apps). It can also analyze patterns of dialogue and scenes that the user has shared in the past and suggest similar sharing methods. Furthermore, the sharing function can predict and suggest sharing methods to be used at specific times based on the user's past sharing history. In this way, the optimal sharing method can be selected by referring to the user's past sharing history. Some or all of the above processing in the sharing function is performed using AI. For example, the sharing function inputs the user's past sharing history into the AI, and the AI ​​selects the optimal sharing method. In this way, the sharing function can use AI to refer to the user's past sharing history and select the optimal sharing method.

[0111] The sharing function can estimate the user's emotions and determine sharing priorities based on those estimated emotions. For example, if the user is excited, the sharing function will prioritize sharing emotionally moving lines or scenes. Similarly, if the user is relaxed, it can prioritize sharing humorous lines or scenes. Furthermore, if the user is stressed, it can prioritize sharing soothing lines or scenes. This allows for more appropriate sharing results by prioritizing sharing 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 include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing function is performed using generative AI. For example, the sharing function inputs user emotion data into the generative AI, which then determines the optimal priority. This allows the sharing function to use the generative AI to determine sharing priorities based on the user's emotions.

[0112] The sharing function can select the optimal sharing method by considering the user's geographical location when sharing. For example, if the user is in a specific region, the sharing function can prioritize displaying lines and scenes from movies or anime related to that region. Furthermore, if the user is traveling, the sharing function can prioritize displaying lines and scenes related to their travel destination. Additionally, if the user is participating in a specific event, the sharing function can prioritize displaying lines and scenes related to that event. This allows the sharing function to select the optimal sharing method by considering the user's geographical location. Some or all of the above processing in the sharing function is performed using AI. For example, the sharing function inputs the user's geographical location information into the AI, which then selects the optimal sharing method. This allows the sharing function to use AI to select the optimal sharing method by considering the user's geographical location.

[0113] The advertising department can estimate the user's emotions and adjust how ads are displayed based on those emotions. For example, if the user is relaxed, the advertising department can display ads in a calm tone. If the user is excited, the advertising department can display ads in an energetic tone. Furthermore, if the user is sad, the advertising department can display ads in an emotional tone. By adjusting how ads are displayed according to the user's emotions, a more appropriate ad display can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advertising department is performed using generative AI. For example, the advertising department inputs user emotion data into the generative AI, and the generative AI determines the optimal display method. This allows the advertising department to adjust how ads are displayed based on the user's emotions using the generative AI.

[0114] The advertising department can select the most suitable advertisement by referring to the user's past ad viewing history when displaying an advertisement. For example, the advertising department can analyze patterns of advertisements that the user has frequently viewed in the past and suggest similar advertisements. The advertising department can also suggest relevant advertisements based on the user's past click history. Furthermore, the advertising department can predict and suggest advertisements to display at specific times based on the user's past ad viewing history. In this way, the advertising department can select the most suitable advertisement by referring to the user's past ad viewing history. Some or all of the above processes in the advertising department are performed using AI. For example, the advertising department inputs the user's past ad viewing history into the AI, and the AI ​​selects the most suitable advertisement. In this way, the advertising department can use AI to select the most suitable advertisement by referring to the user's past ad viewing history.

[0115] The advertising department can estimate the user's emotions and prioritize ads based on those emotions. For example, if a user is excited, the advertising department will prioritize displaying emotionally moving ads. Similarly, if a user is relaxed, the advertising department can prioritize displaying humorous ads. Furthermore, if a user is stressed, the advertising department can prioritize displaying calming ads. This allows for more appropriate ad display by prioritizing ads according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advertising department is performed using generative AI. For example, the advertising department inputs user emotion data into the generative AI, which then determines the optimal priority. This allows the advertising department to use generative AI to prioritize ads based on the user's emotions.

[0116] The advertising department can select the most relevant advertisements by considering the user's geographical location when displaying ads. For example, if a user is in a specific region, the advertising department can prioritize displaying ads related to that region. Furthermore, if a user is traveling, the advertising department can prioritize displaying ads related to their travel destination. Additionally, if a user is participating in a specific event, the advertising department can prioritize displaying ads related to that event. This allows the advertising department to select the most relevant advertisements by considering the user's geographical location. Some or all of the above processes in the advertising department are performed using AI. For example, the advertising department inputs the user's geographical location information into the AI, which then selects the most relevant advertisements. This allows the advertising department to use AI to select the most relevant advertisements by considering the user's geographical location.

[0117] The advertising department can analyze a user's social media activity when displaying an ad to select the most relevant ad. For example, the advertising department can suggest relevant ads based on the content a user frequently posts on social media. It can also analyze the content of accounts a user follows on social media and suggest relevant ads. Furthermore, the advertising department can suggest relevant ads based on the activities of groups and communities a user participates in on social media. This allows the advertising department to provide more appropriate ads by analyzing the user's social media activity. Some or all of the above processes in the advertising department are performed using AI. For example, the advertising department inputs the user's social media activity into the AI, which then selects the most relevant ad. This allows the advertising department to use AI to analyze the user's social media activity and select the most relevant ad.

[0118] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0119] The reception desk can suggest the optimal input method by referring to the user's past input history when receiving user input. For example, it can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also analyze patterns of dialogue and scenes that the user has previously entered and suggest similar input methods. Furthermore, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. In this way, the reception desk can suggest the optimal input method by analyzing the user's past input history. Some or all of the above processes in the reception desk are performed using AI. For example, the reception desk inputs the user's past input history into the AI, and the AI ​​selects the optimal input method. In this way, the reception desk can use AI to analyze the user's past input history and select the optimal input method.

[0120] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results. Furthermore, if the user is excited, it can provide visually stimulating analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit inputs the user's emotion data into the generative AI, and the generative AI determines the optimal presentation method. This allows the analysis unit to adjust the presentation of the analysis based on the user's emotions using the generative AI.

[0121] The generation unit can adjust the level of detail in the generated text based on the importance of the lines and scenes. For example, it can generate detailed text for important lines and scenes, and concise text for general lines and scenes. Furthermore, it can generate detailed text for lines and scenes of particular interest to the user. By adjusting the level of detail based on the importance of the lines and scenes, it can provide more appropriate generated results. Some or all of the above processing in the generation unit is performed using AI. For example, the generation unit inputs the importance of the lines and scenes into the AI, which then determines the optimal level of detail. This allows the generation unit to use AI to adjust the level of detail in the generated text based on the importance of the lines and scenes.

[0122] The service provider can estimate the user's emotions and adjust the display method of the lines and scenes based on the estimated emotions. For example, if the user is relaxed, the lines and scenes can be displayed in a calm tone. If the user is excited, the lines and scenes can be displayed in an energetic tone. Furthermore, if the user is sad, the lines and scenes can be displayed in an emotional tone. By adjusting the display method of the lines and scenes according to the user's emotions, more appropriate display results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the service provider is performed using generative AI. For example, the service provider inputs the user's emotion data into the generative AI, and the generative AI determines the optimal display method. This allows the service provider to use the generative AI to adjust the display method of the lines and scenes based on the user's emotions.

[0123] The sharing unit can estimate the user's emotions and adjust the sharing method based on the estimated emotions. For example, if the user is relaxed, it can share in a calm tone. If the user is excited, it can share in an energetic tone. Furthermore, if the user is sad, it can share in an emotional tone. By adjusting the sharing method according to the user's emotions, it is possible to provide more appropriate sharing results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing unit is performed using generative AI. For example, the sharing unit inputs the user's emotion data into the generative AI, and the generative AI determines the optimal sharing method. This allows the sharing unit to adjust the sharing method based on the user's emotions using the generative AI.

[0124] The reception desk can filter input based on the user's current areas of interest. For example, it can prioritize displaying relevant lines and scenes based on the genres of movies and anime the user has recently viewed. The reception desk can also analyze the content of posts from accounts the user follows on social media and suggest relevant lines and scenes. Furthermore, the reception desk can filter relevant lines and scenes based on keywords the user has recently searched for. This allows the reception desk to prioritize receiving highly relevant input by filtering based on the user's current areas of interest. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the user's areas of interest into the AI, which then performs optimal filtering. This allows the reception desk to filter based on the user's current areas of interest using AI.

[0125] The analysis unit can adjust the level of detail of the analysis based on the importance of the input. For example, it can perform a detailed analysis for important lines of dialogue or scenes, and a concise analysis for general lines of dialogue or scenes. Furthermore, it can perform a detailed analysis for lines of dialogue or scenes of particular interest to the user. By adjusting the level of detail of the analysis based on the importance of the input, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit inputs the importance of the input into the AI, and the AI ​​determines the optimal level of detail. This allows the analysis unit to use the AI ​​to adjust the level of detail of the analysis based on the importance of the input.

[0126] The generation unit can estimate the user's emotions and adjust the expression of the generated dialogue and scenes based on the estimated user emotions. For example, if the user is relaxed, it can generate dialogue and scenes in a calm tone. If the user is excited, it can generate dialogue and scenes in an energetic tone. Furthermore, if the user is sad, it can generate dialogue and scenes in an emotional tone. By adjusting the expression of the generated dialogue and scenes according to the user's emotions, more appropriate generation results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's emotion data into the generation AI, and the generation AI determines the optimal expression method. This allows the generation unit to use the generation AI to adjust the expression of the generated dialogue and scenes based on the user's emotions.

[0127] The service delivery unit can select the optimal delivery method by referring to the user's past usage history at the time of delivery. For example, it can prioritize suggesting delivery methods (GIFs, text, etc.) that the user has frequently used in the past. The service delivery unit can also analyze patterns of dialogue and scenes used by the user in the past and suggest similar delivery methods. Furthermore, the service delivery unit can predict and suggest delivery methods to be used at specific times based on the user's past usage history. In this way, the service delivery unit can select the optimal delivery method by referring to the user's past usage history. Some or all of the above processing in the service delivery unit is performed using AI. For example, the service delivery unit inputs the user's past usage history into the AI, and the AI ​​selects the optimal delivery method. In this way, the service delivery unit can use AI to select the optimal delivery method by referring to the user's past usage history.

[0128] The advertising department can estimate the user's emotions and adjust how ads are displayed based on those emotions. For example, if the user is relaxed, ads can be displayed in a calm tone. If the user is excited, ads can be displayed in an energetic tone. Furthermore, if the user is sad, ads can be displayed in an emotional tone. By adjusting how ads are displayed according to the user's emotions, more appropriate ad displays can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advertising department is performed using generative AI. For example, the advertising department inputs user emotion data into the generative AI, and the generative AI determines the optimal display method. This allows the advertising department to adjust how ads are displayed based on the user's emotions using the generative AI.

[0129] The following briefly describes the processing flow for example form 2.

[0130] Step 1: The reception desk receives user input. User input includes requests for famous lines or scenes they want to use. The reception desk provides an interface for users to input the famous lines or scenes they want to use in messaging apps or social media, and sends the user input to the generation AI. Step 2: The analysis unit uses a generation AI to analyze the information received by the reception unit. Based on the user's request, the analysis unit refers to a database of famous lines and scenes from movies, anime, and manga, and extracts relevant information. Step 3: The generation unit generates the optimal dialogue and scenes based on the information analyzed by the analysis unit. The generation unit uses a generation AI to generate dialogue and scenes according to the user's request, and the generated dialogue and scenes are provided in GIF or text format. Step 4: The provider unit provides the dialogue and scenes generated by the generator unit. The provider unit provides an interface for providing the generated dialogue and scenes to the user and includes functions for sharing on social media and messaging apps.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives user input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user input using a generation AI. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates optimal dialogue and scenes based on the analyzed information. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated dialogue and scenes to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives user input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user input using generation AI. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates optimal dialogue and scenes based on the analyzed information. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated dialogue and scenes to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives user input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user input using a generation AI. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates optimal lines and scenes based on the analyzed information. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated lines and scenes to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0167] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives user input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user input using a generation AI. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates optimal lines and scenes based on the analyzed information. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated lines and scenes to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] (Note 1) A reception area that receives user input, An analysis unit that analyzes the information received by the reception unit, A generation unit generates optimal lines and scenes based on the information analyzed by the aforementioned analysis unit, The system comprises a providing unit that provides the lines and scenes generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is The AI ​​generates the most suitable lines and scenes by referencing a database of famous lines and scenes from movies, anime, and manga. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, The generated dialogue and scenes are provided in GIF and text formats. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, It includes a customization section for customizing generated dialogue and scenes. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, It includes a sharing section that allows users to share generated dialogue and scenes on social media and messaging apps. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, It includes an advertising department that uses the generated dialogue and scenes as advertisements. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving input, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving input, the system prioritizes accepting inputs that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving input, the system analyzes the user's social media activity and accepts relevant input. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the input. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the input category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the analysis priority is determined based on the timing of input submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the inputs. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the way dialogue and scenes are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) 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 21) 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 22) The generating unit is It estimates the user's emotions and adjusts the length of the generated dialogue and scenes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the generation priority is determined based on the submission timing of lines and scenes. The system described in Appendix 1, characterized by the features described herein. (Note 24) 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 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way dialogue and scenes are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the content will be customized based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the lines and scenes to be delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) 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 30) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity to customize the content offered. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned customization unit is It estimates the user's emotions and adjusts the customization method based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 32) The aforementioned customization unit is During customization, the system selects the optimal customization method by referring to the user's past customization history. The system described in Appendix 4, characterized by the features described herein. (Note 33) The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned customization unit is During customization, the optimal customization method is selected by considering the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned shared portion is, It estimates the user's emotions and adjusts the sharing method based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 36) The aforementioned shared portion is, When sharing, the system will refer to the user's past sharing history to select the most suitable sharing method. The system described in Appendix 5, characterized by the features described herein. (Note 37) The aforementioned shared portion is, It estimates the user's emotions and determines sharing priorities based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 38) The aforementioned shared portion is, When sharing, the system selects the optimal sharing method by considering the user's geographical location. The system described in Appendix 5, characterized by the features described herein. (Note 39) The aforementioned advertising department, It estimates the user's emotions and adjusts how ads are displayed based on those estimated emotions. The system described in Appendix 6, characterized by the features described herein. (Note 40) The aforementioned advertising department, When displaying ads, the system selects the most suitable ads by referring to the user's past ad viewing history. The system described in Appendix 6, characterized by the features described herein. (Note 41) The aforementioned advertising department, It estimates user sentiment and prioritizes ads based on that estimated sentiment. The system described in Appendix 6, characterized by the features described herein. (Note 42) The aforementioned advertising department, When displaying ads, the system selects the most suitable ads by considering the user's geographical location. The system described in Appendix 6, characterized by the features described herein. (Note 43) The aforementioned advertising department, When displaying ads, the system analyzes the user's social media activity to select the most suitable ad. The system described in Appendix 6, characterized by the features described herein. [Explanation of Symbols]

[0203] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that receives user input, An analysis unit that analyzes the information received by the reception unit, A generation unit generates optimal lines and scenes based on the information analyzed by the aforementioned analysis unit, The system comprises a providing unit that provides the lines and scenes generated by the generation unit. A system characterized by the following features.

2. The generating unit is The AI ​​generates the most suitable lines and scenes by referencing a database of famous lines and scenes from movies, anime, and manga. The system according to feature 1.

3. The aforementioned supply unit is, The generated dialogue and scenes are provided in GIF and text formats. The system according to feature 1.

4. The aforementioned supply unit is, It includes a customization section for customizing generated dialogue and scenes. The system according to feature 1.

5. The aforementioned supply unit is, It includes a sharing section that allows users to share generated dialogue and scenes on social media and messaging apps. The system according to feature 1.

6. The aforementioned supply unit is, It includes an advertising department that uses the generated dialogue and scenes as advertisements. The system according to feature 1.

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

8. The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system according to feature 1.

Images