System

Abstract

An object of the system according to the embodiment is to allow a user to create a story by himself / herself and enjoy the story as moving image content.SOLUTION: A system includes a reception unit, a proposal unit, a generation unit, a creation unit, a voice unit, and a music unit. The receiving unit receives an input from a user. The proposal unit proposes the setting item based on the information received by the reception unit. The generation unit generates a picture on the basis of the setting item proposed by the proposal unit. The creation unit converts the generated image into a moving image as continuous frames. The sound unit adds sound to the moving image created by the creation unit. The music unit adds music to the moving image based on the sound added by the sound unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it is difficult for users to create their own stories and enjoy them as video content.

[0005] The system according to the embodiment aims to enable users to create their own stories and enjoy them as video content. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a proposal unit, a generation unit, a creation unit, an audio unit, and a music unit. The reception unit accepts input from a user. The proposal unit proposes setting items based on the information accepted by the reception unit. The generation unit generates an image based on the setting items proposed by the proposal unit. The creation unit converts the generated image into a video as successive frames. The audio unit adds audio to the video created by the creation unit. The music unit adds music to the video based on the audio added by the audio unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to create their own stories and enjoy them as video content. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An entertainment content generation system according to an embodiment of the present invention allows users to create their own stories and enjoy simulated experiences. The entertainment content generation system accepts user input, suggests settings, and uses a generation AI to generate images, create videos, add audio, and music, ultimately completing the video with movie-like coding. For example, in the entertainment content generation system, the user configures content in the form of a question. For example, if the user inputs "fantasy," the system suggests settings such as "Character: Wizard, Background: Magical Forest, Story: Adventure." The generation AI then generates images based on the configured story. For example, if the background is a "magical forest," the generation AI generates an image of the magical forest. When creating a video based on the generated images, the generation AI generates consecutive frames of the images to create the video. For example, if the character is a "magician," the generation AI generates consecutive frames of a scene in which the wizard uses magic. When adding audio to the main character of the video, the generation AI uses a pre-trained audio database to generate the character's lines. For example, in a scene in which the wizard says, "I'm going to use magic," the generation AI generates the line. Finally, when adding music to a video, the generation AI selects music that suits the scene. For example, exciting music is selected for an adventure scene. This allows the entertainment content generation system to allow users to create their own stories and enjoy simulated experiences. For example, users can create a drama or movie in which they are the main character, and simulate an imaginary world. Furthermore, by substituting themselves for an exciting story, users can stimulate their imagination and enjoy exciting content.

[0029] An entertainment content generation system according to an embodiment includes a reception unit, a suggestion unit, a generation unit, a creation unit, an audio unit, and a music unit. The reception unit accepts input from a user. The user input includes, but is not limited to, text input, voice input, and image input. The suggestion unit suggests setting items based on the information accepted by the reception unit. For example, if a user inputs "fantasy," the suggestion unit suggests setting items such as "Character: Wizard, Background: Magical Forest, Story: Adventure." The generation unit uses a generation AI to generate images based on the setting items suggested by the suggestion unit. The generation AI generates images that match the settings using technologies such as a deep learning model or a generative adversarial network (GAN). The creation unit converts the generated images into a video as consecutive frames. The creation unit generates the consecutive frames based on, for example, a frame rate and a transition method between frames. The audio unit generates lines for characters using a pre-trained audio database. The audio unit generates lines for characters using, for example, voice synthesis technology. The music section selects appropriate music based on the content of the scene. For example, the music section selects music based on the theme or atmosphere of the scene. In this way, the entertainment content generation system according to the embodiment allows users to create their own stories and enjoy simulated experiences.

[0030] The suggestion unit can suggest specific setting items based on the user's input. For example, if the user inputs "fantasy," the suggestion unit suggests specific setting items such as "Character: Wizard, Background: Magical Forest, Story: Adventure." The suggestion unit can also suggest setting items such as character personalities, background scenes, and story developments based on the user's input. This makes it possible to suggest appropriate setting items based on the user's input.

[0031] The generation unit can use a generation AI to generate images that match the setting. The generation unit, for example, uses a generation AI to generate images that match the setting. The generation AI generates images that match the setting using technologies such as a deep learning model or a generative artificial network (GAN). For example, if the background is a "magical forest," the generation AI generates an image of the magical forest. The generation unit can also use a generation AI to generate images of characters. For example, if the character is a "wizard," the generation AI generates an image of a wizard. In this way, by using the generation AI, an image that matches the setting can be generated.

[0032] The creation unit can convert the generated images into a video as successive frames. For example, the creation unit converts the generated images into a video as successive frames. The creation unit generates successive frames based on, for example, the frame rate and the transition method between frames. For example, if a character is a "wizard," the creation unit generates a scene in which the wizard uses magic as successive frames. The creation unit can also add effects when converting the generated images into a video as successive frames. For example, an effect is added to a scene in which the wizard uses magic. In this way, a video can be created by generating successive frames of the generated images.

[0033] The voice unit can generate lines for characters using a pre-trained voice database. The voice unit generates lines for characters using, for example, a pre-trained voice database. The voice unit generates lines for characters using, for example, voice synthesis technology. For example, in a scene where a wizard says, "I'm going to use magic," the voice unit generates those lines. The voice unit can also adjust the tone and expression of the lines according to the emotions of the characters. For example, if the wizard is excited, the voice unit generates lines in an energetic tone. In this way, lines for characters can be generated by using a pre-trained voice database.

[0034] The music department can select appropriate music based on the content of a scene. For example, the music department selects appropriate music based on the content of a scene. For example, the music department selects music based on the theme or atmosphere of a scene. For example, exciting music is selected for an adventure scene. The music department can also adjust the tone and tempo of the music depending on the emotion of the scene. For example, music that brings out the emotion is selected for an emotional scene. In this way, the atmosphere of the video can be enhanced by selecting music that suits the scene.

[0035] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit analyzes the user's past input history and suggests the optimal input method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also automatically display setting items that the user has previously input as candidates. Furthermore, the reception unit can predict and suggest setting items to be used in a specific time period from the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history.

[0036] The reception unit can filter the input content based on the user's current areas of interest. The reception unit filters the input content based on, for example, the user's current areas of interest. The reception unit suggests related setting items based on, for example, keywords recently searched by the user. The reception unit can also filter the setting items based on genres in which the user is interested (fantasy, science fiction, etc.). Furthermore, the reception unit can also suggest related setting items based on content recently viewed by the user. In this way, by filtering the input content based on the user's areas of interest, highly relevant setting items can be suggested.

[0037] The reception unit can select the optimum input means depending on the user's input method. For example, the reception unit selects the optimum input means depending on the user's input method. For example, if the user prefers voice input, the reception unit can provide voice input preferentially. Also, if the user prefers text input, the reception unit can provide text input preferentially. Furthermore, if the user prefers image input, the reception unit can provide image input preferentially. In this way, by selecting the optimum input means depending on the user's input method, convenience for the user is improved.

[0038] The reception unit can preferentially accept highly relevant input content in consideration of the user's geographical location information. The reception unit, for example, preferentially accepts highly relevant input content in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit can preferentially display setting items related to that area. Also, when the user is traveling, the reception unit can preferentially display setting items related to the trip. Furthermore, when the user is at home, the reception unit can preferentially display setting items related to the home. In this way, highly relevant setting items can be preferentially displayed by taking the user's geographical location information into consideration.

[0039] The reception unit can analyze the user's social media activity and receive related input content. The reception unit, for example, analyzes the user's social media activity and receives related input content. The reception unit, for example, suggests setting items related to places where the user has checked in on social media. The reception unit can also analyze the content posted by the user on social media and suggest related setting items. Furthermore, the reception unit can also suggest related setting items by taking into account the activities of the user's friends on social media. In this way, highly relevant setting items can be suggested by analyzing the user's social media activity.

[0040] The reception unit can customize the input method by reflecting the user's past feedback. The reception unit, for example, customizes the input method by reflecting the user's past feedback. The reception unit, for example, improves the input method based on feedback provided by the user in the past. The reception unit can also avoid input methods that the user has been dissatisfied with in the past and suggest a preferred input method. Furthermore, the reception unit can analyze the user's past feedback and customize an optimal input method. In this way, the optimal input method can be provided by reflecting the user's past feedback.

[0041] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the setting item. The suggestion unit can adjust the level of detail of the suggestion based on, for example, the importance of the setting item. The suggestion unit can provide a detailed explanation for an important setting item. Also, the suggestion unit can provide a concise explanation for a less important setting item. Furthermore, the suggestion unit can adjust the level of detail of the suggestion according to the importance of the setting item selected by the user. In this way, by adjusting the level of detail of the suggestion based on the importance of the setting item, it is possible to make a suggestion that is suitable for the user.

[0042] The suggestion unit can apply different suggestion algorithms depending on the category of the setting item. For example, the suggestion unit applies different suggestion algorithms depending on the category of the setting item. For example, the suggestion unit applies a character generation algorithm to setting items for characters. Also, the suggestion unit can apply a background generation algorithm to setting items for backgrounds. Furthermore, the suggestion unit can apply a story generation algorithm to setting items for stories. In this way, by applying different suggestion algorithms depending on the category of the setting item, suggestions suitable for the user can be made.

[0043] The suggestion unit can improve the accuracy of the suggestions by referring to the user's past suggestion results. The suggestion unit, for example, improves the accuracy of the suggestions by referring to the user's past suggestion results. The suggestion unit makes optimal suggestions by referring to setting items selected by the user in the past. The suggestion unit can also analyze the user's past suggestion results and improve the accuracy of the suggestions. Furthermore, the suggestion unit can also improve the accuracy of the suggestions by referring to feedback provided by the user in the past. In this way, the accuracy of the suggestions can be improved by referring to the user's past suggestion results.

[0044] The suggestion unit can determine the priority of suggestions based on the submission time of the setting items. The suggestion unit determines the priority of suggestions based on, for example, the submission time of the setting items. The suggestion unit, for example, gives priority to suggestions for urgent setting items. The suggestion unit can also give priority to suggestions for setting items whose submission deadline is approaching. Furthermore, the suggestion unit can also determine the priority of suggestions based on the submission time specified by the user. In this way, by determining the priority of suggestions based on the submission time of the setting items, it is possible to give priority to suggestions for setting items with high urgency.

[0045] The suggestion unit can adjust the order of suggestions based on the relevance of the setting items. The suggestion unit adjusts the order of suggestions based on, for example, the relevance of the setting items. The suggestion unit, for example, preferentially suggests highly relevant setting items. It can also postpone less relevant setting items. Furthermore, the suggestion unit can adjust the order of suggestions based on the relevance of the setting items selected by the user. In this way, by adjusting the order of suggestions based on the relevance of the setting items, it is possible to preferentially suggest highly relevant setting items.

[0046] The suggestion unit can adjust the use of technical terminology in the suggestions according to the user's level of expertise. The suggestion unit, for example, adjusts the use of technical terminology in the suggestions according to the user's level of expertise. For example, if the user has technical knowledge, the suggestion unit makes the suggestions using technical terminology. Also, if the user does not have technical knowledge, the suggestion unit can make the suggestions in easy-to-understand language. Furthermore, the suggestion unit can adjust the use of technical terminology in the suggestions according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the suggestions according to the user's level of expertise, suggestions suitable for the user can be made.

[0047] The generation unit can adjust the precision of the image to be generated based on the level of detail of the setting item. The generation unit adjusts the precision of the image to be generated based on, for example, the level of detail of the setting item. The generation unit, for example, generates a highly precise image for a detailed setting item. Also, the generation unit can generate a simplified image for a less detailed setting item. Furthermore, the generation unit can adjust the precision of the image to be generated according to the level of detail of the setting item selected by the user. In this way, by adjusting the precision of the image to be generated based on the level of detail of the setting item, it is possible to generate an image that is suitable for the user.

[0048] The generation unit can apply different generation algorithms depending on the category of the setting item. For example, the generation unit applies different generation algorithms depending on the category of the setting item. For example, the generation unit applies a character generation algorithm to setting items for characters. Also, the generation unit can apply a background generation algorithm to setting items for backgrounds. Furthermore, the generation unit can apply a story generation algorithm to setting items for stories. In this way, by applying different generation algorithms depending on the category of the setting item, it is possible to generate an image that is suitable for the user.

[0049] The generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, improves the accuracy of generation by referring to the user's past generation results. The generation unit, for example, generates an optimal image by referring to images generated by the user in the past. The generation unit can also analyze the user's past generation results and improve the accuracy of generation. Furthermore, the generation unit can also improve the accuracy of generation by referring to feedback provided by the user in the past. In this way, the accuracy of generation can be improved by referring to the user's past generation results.

[0050] The generation unit can determine the priority of images to be generated based on the submission time of the setting item. The generation unit determines the priority of images to be generated based on, for example, the submission time of the setting item. The generation unit, for example, generates images with priority for urgent setting items. Also, the generation unit can generate images with priority for setting items whose submission deadline is approaching. Furthermore, the generation unit can determine the priority of images to be generated based on the submission time specified by the user. In this way, by determining the priority of images to be generated based on the submission time of the setting item, images with high urgency can be generated with priority.

[0051] The generation unit can adjust the order of images to be generated based on the relevance of the setting items. The generation unit adjusts the order of images to be generated based on, for example, the relevance of the setting items. The generation unit, for example, generates images with priority for highly relevant setting items. It can also postpone generating images with low relevance. Furthermore, the generation unit can adjust the order of images to be generated based on the relevance of the setting items selected by the user. In this way, by adjusting the order of images to be generated based on the relevance of the setting items, it is possible to generate images with high relevance with priority.

[0052] The generation unit can adjust the level of detail of the image to be generated according to the user's level of expertise. The generation unit adjusts the level of detail of the image to be generated according to the user's level of expertise, for example. The generation unit generates a detailed image when the user has specialized knowledge, for example. Also, the generation unit can generate a simplified image when the user does not have specialized knowledge. Furthermore, the generation unit can adjust the level of detail of the image to be generated according to the user's level of expertise. In this way, by adjusting the level of detail of the image to be generated according to the user's level of expertise, it is possible to generate an image that is suitable for the user.

[0053] The creation unit can adjust the level of detail of the video based on the importance of the generated images. The creation unit adjusts the level of detail of the video based on, for example, the importance of the generated images. The creation unit can create detailed video for important images, for example. Also, it can create simplified video for less important images. Furthermore, the creation unit can adjust the level of detail of the video according to the importance of images selected by the user. In this way, by adjusting the level of detail of the video based on the importance of the generated images, it is possible to create a video that is suitable for the user.

[0054] The creation unit can apply different creation algorithms depending on the category of the generated images. For example, the creation unit applies different creation algorithms depending on the category of the generated images. For example, the creation unit applies a character creation algorithm to images of characters. Also, it can apply a background creation algorithm to background images. Furthermore, it can apply a story creation algorithm to images of a story. In this way, by applying different creation algorithms depending on the category of the generated images, it is possible to create a video that is suitable for the user.

[0055] The creation unit can improve the accuracy of creation by referring to the user's past creation results. The creation unit, for example, improves the accuracy of creation by referring to the user's past creation results. The creation unit, for example, creates an optimal video by referring to videos created by the user in the past. The creation unit can also analyze the user's past creation results and improve the accuracy of creation. Furthermore, the creation unit can also improve the accuracy of creation by referring to feedback provided by the user in the past. In this way, the accuracy of creation can be improved by referring to the user's past creation results.

[0056] The creation unit can determine the priority of videos based on the submission time of the generated images. The creation unit determines the priority of videos based on, for example, the submission time of the generated images. The creation unit, for example, prioritizes the creation of videos for urgent images. It can also prioritize the creation of videos for images with an approaching submission deadline. Furthermore, the creation unit can also determine the priority of videos based on the submission time specified by the user. In this way, by determining the priority of videos based on the submission time of the generated images, videos with high urgency can be created with priority.

[0057] The creation unit can adjust the order of the videos based on the relevance of the generated images. The creation unit adjusts the order of the videos based on, for example, the relevance of the generated images. The creation unit, for example, preferentially incorporates highly relevant images into the video. It can also postpone images with low relevance. Furthermore, the creation unit can adjust the order of the videos based on the relevance of images selected by the user. In this way, by adjusting the order of the videos based on the relevance of the generated images, it is possible to create highly relevant videos with priority.

[0058] The creation unit can adjust the level of detail of the video according to the user's level of expertise. The creation unit adjusts the level of detail of the video according to, for example, the user's level of expertise. For example, if the user has specialized knowledge, the creation unit can create a detailed video. Also, if the user does not have specialized knowledge, the creation unit can create a simplified video. Furthermore, the creation unit can adjust the level of detail of the video according to the user's level of expertise. In this way, by adjusting the level of detail of the video according to the user's level of expertise, a video suitable for the user can be created.

[0059] The audio unit can adjust the level of detail of the audio based on the importance of the characters. For example, the audio unit adjusts the level of detail of the audio based on the importance of the characters. For example, the audio unit can provide detailed audio for important characters. For less important characters, the audio unit can also provide simplified audio. Furthermore, the audio unit can adjust the level of detail of the audio according to the importance of the characters selected by the user. In this way, by adjusting the level of detail of the audio based on the importance of the characters, audio suitable for the user can be provided.

[0060] The voice unit can apply different voice generation algorithms depending on the category of the character. For example, the voice unit applies different voice generation algorithms depending on the category of the character. For example, the voice unit applies a voice generation algorithm for the main character to the voice of the main character. Also, it can apply a voice generation algorithm for supporting characters to the voice of supporting characters. Furthermore, it can apply a voice generation algorithm for enemy characters to the voice of enemy characters. In this way, by applying different voice generation algorithms depending on the category of the character, it is possible to provide voices that are suitable for the user.

[0061] The voice unit can improve the accuracy of the voice by referring to the user's past voice generation results. The voice unit, for example, improves the accuracy of the voice by referring to the user's past voice generation results. The voice unit, for example, generates an optimal voice by referring to voices generated by the user in the past. The voice unit can also analyze the user's past voice generation results and improve the accuracy of the voice. Furthermore, the voice unit can also improve the accuracy of the voice by referring to feedback provided by the user in the past. In this way, the accuracy of the voice can be improved by referring to the user's past voice generation results.

[0062] The audio unit can determine the priority of audio based on the time of submission of the characters. The audio unit determines the priority of audio based on, for example, the time of submission of the characters. The audio unit, for example, generates audio preferentially for urgent characters. The audio unit can also generate audio preferentially for characters whose submission deadline is approaching. Furthermore, the audio unit can determine the priority of audio based on the submission time specified by the user. In this way, by determining the priority of audio based on the time of submission of the characters, audio with high urgency can be generated preferentially.

[0063] The audio unit can adjust the order of the audio based on the relevance of the characters. For example, the audio unit adjusts the order of the audio based on the relevance of the characters. For example, the audio unit prioritizes incorporating highly relevant characters into the audio. It can also postpone incorporating less relevant characters. Furthermore, the audio unit can adjust the order of the audio based on the relevance of the characters selected by the user. In this way, by adjusting the order of the audio based on the relevance of the characters, it is possible to generate highly relevant audio with priority.

[0064] The audio unit can adjust the level of detail of the audio according to the user's level of expertise. For example, the audio unit adjusts the level of detail of the audio according to the user's level of expertise. For example, the audio unit provides detailed audio when the user has expertise. Alternatively, the audio unit can provide simplified audio when the user does not have expertise. Furthermore, the audio unit can adjust the level of detail of the audio according to the user's level of expertise. In this way, audio suitable for the user can be provided by adjusting the level of detail of the audio according to the user's level of expertise.

[0065] The music unit can adjust the level of detail of the music based on the importance of the scene. For example, the music unit adjusts the level of detail of the music based on the importance of the scene. For example, the music unit selects detailed music for important scenes. Alternatively, the music unit can select simplified music for less important scenes. Furthermore, the music unit can adjust the level of detail of the music according to the importance of the scene selected by the user. In this way, by adjusting the level of detail of the music based on the importance of the scene, music suitable for the user can be provided.

[0066] The music unit can apply different music selection algorithms depending on the scene category. For example, the music unit applies different music selection algorithms depending on the scene category. For example, the music unit applies an action music selection algorithm to an action scene. Also, it can apply a romance music selection algorithm to a romance scene. Furthermore, it can apply a comedy music selection algorithm to a comedy scene. In this way, by applying different music selection algorithms depending on the scene category, it is possible to provide music that is suitable for the user.

[0067] The music unit can improve the accuracy of the music by referring to the user's past music selection results. The music unit, for example, improves the accuracy of the music by referring to the user's past music selection results. The music unit, for example, selects optimal music by referring to music that the user has selected in the past. The music unit can also analyze the user's past music selection results to improve the accuracy of the music. Furthermore, the music unit can also improve the accuracy of the music by referring to feedback that the user has provided in the past. In this way, the accuracy of the music can be improved by referring to the user's past music selection results.

[0068] The music department can determine the priority of music based on the time of scene submission. The music department determines the priority of music based on, for example, the time of scene submission. The music department, for example, prioritizes music selection for urgent scenes. The music department can also prioritize music selection for scenes with an approaching submission deadline. Furthermore, the music department can also determine the priority of music based on the submission time specified by the user. In this way, by determining the priority of music based on the time of scene submission, it is possible to prioritize the selection of music with high urgency.

[0069] The music unit can adjust the order of music based on the relevance of scenes. For example, the music unit adjusts the order of music based on the relevance of scenes. For example, the music unit prioritizes incorporating highly relevant scenes into the music. It can also postpone less relevant scenes. Furthermore, the music unit can adjust the order of music based on the relevance of scenes selected by the user. In this way, by adjusting the order of music based on the relevance of scenes, it is possible to prioritize the selection of highly relevant music.

[0070] The music unit can adjust the level of detail of the music according to the user's level of expertise. For example, the music unit adjusts the level of detail of the music according to the user's level of expertise. For example, the music unit can provide detailed music if the user has specialized knowledge. Alternatively, the music unit can provide simplified music if the user does not have specialized knowledge. Furthermore, the music unit can adjust the level of detail of the music according to the user's level of expertise. In this way, music suitable for the user can be provided by adjusting the level of detail of the music according to the user's level of expertise.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The reception unit can analyze the user's input in real time and provide immediate feedback based on the input. For example, if the user inputs "fantasy," the reception unit immediately suggests related setting items and guides the user on the next step based on the setting item selected by the user. In addition, if the user is unsure of what to input, the reception unit can refer to past input history and general trends to make appropriate suggestions. Furthermore, the reception unit can provide an optimal interface depending on the user's input speed and input method (voice, text, etc.). This allows the user to create content smoothly.

[0073] The suggestion unit can display samples of related content based on the user's input. For example, if the user inputs "adventure," the suggestion unit can display samples of previously created adventure stories for the user to refer to. The suggestion unit can also display related images and videos based on the user's input. Furthermore, the suggestion unit can suggest related character and background options based on the setting items selected by the user. This allows the user to create content while having a specific image in mind.

[0074] The generation unit can customize the style of the image to be generated based on the user's input. For example, if the user inputs "retro," the generation unit can generate a retro-style image. Alternatively, if the user inputs "modern," the generation unit can generate a modern-style image. Furthermore, the generation unit can apply a specific art style (e.g., anime style, realistic style, etc.) based on the settings selected by the user. This allows the user to generate an image in a style that suits their preferences.

[0075] The creation unit can create an interactive video based on the generated images. For example, an interactive video can be created in which the development of a story changes depending on the user's choice in a particular scene. The creation unit can also add interactive elements (e.g., clickable objects or options) to the video based on settings selected by the user. Furthermore, the creation unit can create more personalized content by allowing the user to edit specific parts of the video. This allows the user to create a unique video that is unique to them.

[0076] The voice unit can customize the tone and accent of a character's voice based on user input. For example, if the user inputs "authoritative voice," the voice unit can generate lines in an authoritative tone. Alternatively, if the user inputs "gentle voice," the voice unit can generate lines in a gentle tone. Additionally, the voice unit can apply a specific accent or dialect based on user-selected settings. This allows users to customize the character's voice to their liking.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The reception unit receives input from the user, which may include text input, voice input, image input, and the like. Step 2: The suggestion unit suggests setting items based on the information received by the reception unit. For example, if the user inputs "fantasy," the suggestion unit suggests setting items such as "Character: Wizard, Background: Magical Forest, Story: Adventure." Step 3: The generator generates an image based on the settings proposed by the suggester. Using generative AI, techniques such as deep learning models and generative artificial network (GAN) are used to generate an image that matches the settings. Step 4: The creator converts the generated images into a video as a series of frames. The creator generates the series of frames based on the frame rate and the transition method between frames. Step 5: The voice department uses a pre-trained voice database to generate the characters' lines. The voice synthesis technology is used to generate the characters' lines. Step 6: The music department selects appropriate music based on the content of the scene. Music is selected based on the theme and atmosphere of the scene.

[0079] (Example 2) An entertainment content generation system according to an embodiment of the present invention allows users to create their own stories and enjoy simulated experiences. The entertainment content generation system accepts user input, suggests settings, and uses a generation AI to generate images, create videos, add audio, and music, ultimately completing the video with movie-like coding. For example, in the entertainment content generation system, the user configures content in the form of a question. For example, if the user inputs "fantasy," the system suggests settings such as "Character: Wizard, Background: Magical Forest, Story: Adventure." The generation AI then generates images based on the configured story. For example, if the background is a "magical forest," the generation AI generates an image of the magical forest. When creating a video based on the generated images, the generation AI generates consecutive frames of the images to create the video. For example, if the character is a "magician," the generation AI generates consecutive frames of a scene in which the wizard uses magic. When adding audio to the main character of the video, the generation AI uses a pre-trained audio database to generate the character's lines. For example, in a scene in which the wizard says, "I'm going to use magic," the generation AI generates the line. Finally, when adding music to a video, the generation AI selects music that suits the scene. For example, exciting music is selected for an adventure scene. This allows the entertainment content generation system to allow users to create their own stories and enjoy simulated experiences. For example, users can create a drama or movie in which they are the main character, and simulate an imaginary world. Furthermore, by substituting themselves for an exciting story, users can stimulate their imagination and enjoy exciting content.

[0080] An entertainment content generation system according to an embodiment includes a reception unit, a suggestion unit, a generation unit, a creation unit, an audio unit, and a music unit. The reception unit accepts input from a user. The user input includes, but is not limited to, text input, voice input, and image input. The suggestion unit suggests setting items based on the information accepted by the reception unit. For example, if a user inputs "fantasy," the suggestion unit suggests setting items such as "Character: Wizard, Background: Magical Forest, Story: Adventure." The generation unit uses a generation AI to generate images based on the setting items suggested by the suggestion unit. The generation AI generates images that match the settings using technologies such as a deep learning model or a generative adversarial network (GAN). The creation unit converts the generated images into a video as consecutive frames. The creation unit generates the consecutive frames based on, for example, a frame rate and a transition method between frames. The audio unit generates lines for characters using a pre-trained audio database. The audio unit generates lines for characters using, for example, voice synthesis technology. The music section selects appropriate music based on the content of the scene. For example, the music section selects music based on the theme or atmosphere of the scene. In this way, the entertainment content generation system according to the embodiment allows users to create their own stories and enjoy simulated experiences.

[0081] The suggestion unit can suggest specific setting items based on the user's input. For example, if the user inputs "fantasy," the suggestion unit suggests specific setting items such as "Character: Wizard, Background: Magical Forest, Story: Adventure." The suggestion unit can also suggest setting items such as character personalities, background scenes, and story developments based on the user's input. This makes it possible to suggest appropriate setting items based on the user's input.

[0082] The generation unit can use a generation AI to generate images that match the setting. The generation unit, for example, uses a generation AI to generate images that match the setting. The generation AI generates images that match the setting using technologies such as a deep learning model or a generative artificial network (GAN). For example, if the background is a "magical forest," the generation AI generates an image of the magical forest. The generation unit can also use a generation AI to generate images of characters. For example, if the character is a "wizard," the generation AI generates an image of a wizard. In this way, by using the generation AI, an image that matches the setting can be generated.

[0083] The creation unit can convert the generated images into a video as successive frames. For example, the creation unit converts the generated images into a video as successive frames. The creation unit generates successive frames based on, for example, the frame rate and the transition method between frames. For example, if a character is a "wizard," the creation unit generates a scene in which the wizard uses magic as successive frames. The creation unit can also add effects when converting the generated images into a video as successive frames. For example, an effect is added to a scene in which the wizard uses magic. In this way, a video can be created by generating successive frames of the generated images.

[0084] The voice unit can generate lines for characters using a pre-trained voice database. The voice unit generates lines for characters using, for example, a pre-trained voice database. The voice unit generates lines for characters using, for example, voice synthesis technology. For example, in a scene where a wizard says, "I'm going to use magic," the voice unit generates those lines. The voice unit can also adjust the tone and expression of the lines according to the emotions of the characters. For example, if the wizard is excited, the voice unit generates lines in an energetic tone. In this way, lines for characters can be generated by using a pre-trained voice database.

[0085] The music department can select appropriate music based on the content of a scene. For example, the music department selects appropriate music based on the content of a scene. For example, the music department selects music based on the theme or atmosphere of a scene. For example, exciting music is selected for an adventure scene. The music department can also adjust the tone and tempo of the music depending on the emotion of the scene. For example, music that brings out the emotion is selected for an emotional scene. In this way, the atmosphere of the video can be enhanced by selecting music that suits the scene.

[0086] The reception unit can estimate the user's emotion and adjust the timing of input based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the timing of input based on the estimated user's emotion. The reception unit estimates the user's emotion using techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is excited, the timing of input can be made faster to provide smooth operation. Also, if the user is relaxed, the timing of input can be made slower to provide time for detailed setting. Furthermore, if the user is stressed, the timing of input can be minimized to provide simple operation. In this way, smooth operation can be provided by adjusting the timing of input according to the user's emotion.

[0087] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit analyzes the user's past input history and suggests the optimal input method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also automatically display setting items that the user has previously input as candidates. Furthermore, the reception unit can predict and suggest setting items to be used in a specific time period from the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history.

[0088] The reception unit can filter the input content based on the user's current areas of interest. The reception unit filters the input content based on, for example, the user's current areas of interest. The reception unit suggests related setting items based on, for example, keywords recently searched by the user. The reception unit can also filter the setting items based on genres in which the user is interested (fantasy, science fiction, etc.). Furthermore, the reception unit can also suggest related setting items based on content recently viewed by the user. In this way, by filtering the input content based on the user's areas of interest, highly relevant setting items can be suggested.

[0089] The reception unit can select the optimum input means depending on the user's input method. For example, the reception unit selects the optimum input means depending on the user's input method. For example, if the user prefers voice input, the reception unit can provide voice input preferentially. Also, if the user prefers text input, the reception unit can provide text input preferentially. Furthermore, if the user prefers image input, the reception unit can provide image input preferentially. In this way, by selecting the optimum input means depending on the user's input method, convenience for the user is improved.

[0090] The reception unit can estimate the user's emotion and determine the priority of input contents based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of input contents based on the estimated user's emotion. The reception unit estimates the user's emotion using techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is excited, important setting items can be displayed with priority. Also, if the user is relaxed, detailed setting items can be displayed with priority. Furthermore, if the user is stressed, simple setting items can be displayed with priority. In this way, by determining the priority of input contents according to the user's emotion, important setting items can be displayed with priority.

[0091] The reception unit can preferentially accept highly relevant input content in consideration of the user's geographical location information. The reception unit, for example, preferentially accepts highly relevant input content in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit can preferentially display setting items related to that area. Also, when the user is traveling, the reception unit can preferentially display setting items related to the trip. Furthermore, when the user is at home, the reception unit can preferentially display setting items related to the home. In this way, highly relevant setting items can be preferentially displayed by taking the user's geographical location information into consideration.

[0092] The reception unit can analyze the user's social media activity and receive related input content. The reception unit, for example, analyzes the user's social media activity and receives related input content. The reception unit, for example, suggests setting items related to places where the user has checked in on social media. The reception unit can also analyze the content posted by the user on social media and suggest related setting items. Furthermore, the reception unit can also suggest related setting items by taking into account the activities of the user's friends on social media. In this way, highly relevant setting items can be suggested by analyzing the user's social media activity.

[0093] The reception unit can customize the input method by reflecting the user's past feedback. The reception unit, for example, customizes the input method by reflecting the user's past feedback. The reception unit, for example, improves the input method based on feedback provided by the user in the past. The reception unit can also avoid input methods that the user has been dissatisfied with in the past and suggest a preferred input method. Furthermore, the reception unit can analyze the user's past feedback and customize an optimal input method. In this way, the optimal input method can be provided by reflecting the user's past feedback.

[0094] The suggestion unit can estimate the user's emotion and adjust the way in which suggestions are expressed based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the way in which suggestions are expressed based on the estimated user's emotion. The suggestion unit estimates the user's emotion using techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is excited, the suggestion can be made in an exciting way. Also, if the user is relaxed, the suggestion can be made in a calm way. Furthermore, if the user is stressed, the suggestion can be made in a simple and easy-to-understand way. In this way, by adjusting the way in which suggestions are expressed according to the user's emotion, suggestions suitable for the user can be made.

[0095] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the setting item. The suggestion unit can adjust the level of detail of the suggestion based on, for example, the importance of the setting item. The suggestion unit can provide a detailed explanation for an important setting item. Also, the suggestion unit can provide a concise explanation for a less important setting item. Furthermore, the suggestion unit can adjust the level of detail of the suggestion according to the importance of the setting item selected by the user. In this way, by adjusting the level of detail of the suggestion based on the importance of the setting item, it is possible to make a suggestion that is suitable for the user.

[0096] The suggestion unit can apply different suggestion algorithms depending on the category of the setting item. For example, the suggestion unit applies different suggestion algorithms depending on the category of the setting item. For example, the suggestion unit applies a character generation algorithm to setting items for characters. Also, the suggestion unit can apply a background generation algorithm to setting items for backgrounds. Furthermore, the suggestion unit can apply a story generation algorithm to setting items for stories. In this way, by applying different suggestion algorithms depending on the category of the setting item, suggestions suitable for the user can be made.

[0097] The suggestion unit can improve the accuracy of the suggestions by referring to the user's past suggestion results. The suggestion unit, for example, improves the accuracy of the suggestions by referring to the user's past suggestion results. The suggestion unit makes optimal suggestions by referring to setting items selected by the user in the past. The suggestion unit can also analyze the user's past suggestion results and improve the accuracy of the suggestions. Furthermore, the suggestion unit can also improve the accuracy of the suggestions by referring to feedback provided by the user in the past. In this way, the accuracy of the suggestions can be improved by referring to the user's past suggestion results.

[0098] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the length of the suggestion based on the estimated user's emotion. The suggestion unit estimates the user's emotion using techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is excited, the suggestion unit can make a short and to-the-point suggestion. Also, if the user is relaxed, the suggestion unit can make a detailed suggestion. Furthermore, if the user is stressed, the suggestion unit can make a concise and easy-to-understand suggestion. In this way, by adjusting the length of the suggestion according to the user's emotion, it is possible to make a suggestion that is suitable for the user.

[0099] The suggestion unit can determine the priority of suggestions based on the submission time of the setting items. The suggestion unit determines the priority of suggestions based on, for example, the submission time of the setting items. The suggestion unit, for example, gives priority to suggestions for urgent setting items. The suggestion unit can also give priority to suggestions for setting items whose submission deadline is approaching. Furthermore, the suggestion unit can also determine the priority of suggestions based on the submission time specified by the user. In this way, by determining the priority of suggestions based on the submission time of the setting items, it is possible to give priority to suggestions for setting items with high urgency.

[0100] The suggestion unit can adjust the order of suggestions based on the relevance of the setting items. The suggestion unit adjusts the order of suggestions based on, for example, the relevance of the setting items. The suggestion unit, for example, preferentially suggests highly relevant setting items. It can also postpone less relevant setting items. Furthermore, the suggestion unit can adjust the order of suggestions based on the relevance of the setting items selected by the user. In this way, by adjusting the order of suggestions based on the relevance of the setting items, it is possible to preferentially suggest highly relevant setting items.

[0101] The suggestion unit can adjust the use of technical terminology in the suggestions according to the user's level of expertise. The suggestion unit, for example, adjusts the use of technical terminology in the suggestions according to the user's level of expertise. For example, if the user has technical knowledge, the suggestion unit makes the suggestions using technical terminology. Also, if the user does not have technical knowledge, the suggestion unit can make the suggestions in easy-to-understand language. Furthermore, the suggestion unit can adjust the use of technical terminology in the suggestions according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the suggestions according to the user's level of expertise, suggestions suitable for the user can be made.

[0102] The generation unit can estimate the user's emotion and adjust the style of the image to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the style of the image to be generated based on the estimated user's emotion. The generation unit estimates the user's emotion using techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is excited, the generation unit can generate an image in a vivid and exciting style. Also, if the user is relaxed, the generation unit can generate an image in a calm color style. Furthermore, if the user is stressed, the generation unit can generate an image in a simple and highly visible style. In this way, by adjusting the style of the image to be generated according to the user's emotion, it is possible to generate an image that is suitable for the user.

[0103] The generation unit can adjust the precision of the image to be generated based on the level of detail of the setting item. The generation unit adjusts the precision of the image to be generated based on, for example, the level of detail of the setting item. The generation unit, for example, generates a highly precise image for a detailed setting item. Also, the generation unit can generate a simplified image for a less detailed setting item. Furthermore, the generation unit can adjust the precision of the image to be generated according to the level of detail of the setting item selected by the user. In this way, by adjusting the precision of the image to be generated based on the level of detail of the setting item, it is possible to generate an image that is suitable for the user.

[0104] The generation unit can apply different generation algorithms depending on the category of the setting item. For example, the generation unit applies different generation algorithms depending on the category of the setting item. For example, the generation unit applies a character generation algorithm to setting items for characters. Also, the generation unit can apply a background generation algorithm to setting items for backgrounds. Furthermore, the generation unit can apply a story generation algorithm to setting items for stories. In this way, by applying different generation algorithms depending on the category of the setting item, it is possible to generate an image that is suitable for the user.

[0105] The generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, improves the accuracy of generation by referring to the user's past generation results. The generation unit, for example, generates an optimal image by referring to images generated by the user in the past. The generation unit can also analyze the user's past generation results and improve the accuracy of generation. Furthermore, the generation unit can also improve the accuracy of generation by referring to feedback provided by the user in the past. In this way, the accuracy of generation can be improved by referring to the user's past generation results.

[0106] The generation unit can estimate the user's emotion and adjust the color tone of the generated image based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the color tone of the generated image based on the estimated user's emotion. The generation unit estimates the user's emotion using techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is excited, the generation unit can generate an image with vivid and exciting colors. Also, if the user is relaxed, the generation unit can generate an image with calm colors. Furthermore, if the user is stressed, the generation unit can generate an image with simple and highly visible colors. In this way, by adjusting the color tone of the generated image according to the user's emotion, it is possible to generate an image that is suitable for the user.

[0107] The generation unit can determine the priority of images to be generated based on the submission time of the setting item. The generation unit determines the priority of images to be generated based on, for example, the submission time of the setting item. The generation unit, for example, generates images with priority for urgent setting items. Also, the generation unit can generate images with priority for setting items whose submission deadline is approaching. Furthermore, the generation unit can determine the priority of images to be generated based on the submission time specified by the user. In this way, by determining the priority of images to be generated based on the submission time of the setting item, images with high urgency can be generated with priority.

[0108] The generation unit can adjust the order of images to be generated based on the relevance of the setting items. The generation unit adjusts the order of images to be generated based on, for example, the relevance of the setting items. The generation unit, for example, generates images with priority for highly relevant setting items. It can also postpone generating images with low relevance. Furthermore, the generation unit can adjust the order of images to be generated based on the relevance of the setting items selected by the user. In this way, by adjusting the order of images to be generated based on the relevance of the setting items, it is possible to generate images with high relevance with priority.

[0109] The generation unit can adjust the level of detail of the image to be generated according to the user's level of expertise. The generation unit adjusts the level of detail of the image to be generated according to the user's level of expertise, for example. The generation unit generates a detailed image when the user has specialized knowledge, for example. Also, the generation unit can generate a simplified image when the user does not have specialized knowledge. Furthermore, the generation unit can adjust the level of detail of the image to be generated according to the user's level of expertise. In this way, by adjusting the level of detail of the image to be generated according to the user's level of expertise, it is possible to generate an image that is suitable for the user.

[0110] The creation unit can estimate the user's emotions and adjust the video creation method based on the estimated user's emotions. The creation unit, for example, estimates the user's emotions and adjusts the video creation method based on the estimated user's emotions. The creation unit estimates the user's emotions using techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is excited, a video with a fast tempo can be created. Also, if the user is relaxed, a video with a slow tempo can be created. Furthermore, if the user is stressed, a simple video with high visibility can be created. In this way, by adjusting the video creation method according to the user's emotions, a video suitable for the user can be created.

[0111] The creation unit can adjust the level of detail of the video based on the importance of the generated images. The creation unit adjusts the level of detail of the video based on, for example, the importance of the generated images. The creation unit can create detailed video for important images, for example. Also, it can create simplified video for less important images. Furthermore, the creation unit can adjust the level of detail of the video according to the importance of images selected by the user. In this way, by adjusting the level of detail of the video based on the importance of the generated images, it is possible to create a video that is suitable for the user.

[0112] The creation unit can apply different creation algorithms depending on the category of the generated images. For example, the creation unit applies different creation algorithms depending on the category of the generated images. For example, the creation unit applies a character creation algorithm to images of characters. Also, it can apply a background creation algorithm to background images. Furthermore, it can apply a story creation algorithm to images of a story. In this way, by applying different creation algorithms depending on the category of the generated images, it is possible to create a video that is suitable for the user.

[0113] The creation unit can improve the accuracy of creation by referring to the user's past creation results. The creation unit, for example, improves the accuracy of creation by referring to the user's past creation results. The creation unit, for example, creates an optimal video by referring to videos created by the user in the past. The creation unit can also analyze the user's past creation results and improve the accuracy of creation. Furthermore, the creation unit can also improve the accuracy of creation by referring to feedback provided by the user in the past. In this way, the accuracy of creation can be improved by referring to the user's past creation results.

[0114] The creation unit can estimate the user's emotions and adjust the length of the video based on the estimated user's emotions. The creation unit, for example, estimates the user's emotions and adjusts the length of the video based on the estimated user's emotions. The creation unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is excited, the creation unit can create a short, to-the-point video. Also, if the user is relaxed, the creation unit can create a longer video with detailed explanations. Furthermore, if the user is stressed, the creation unit can create a concise, easy-to-understand video. In this way, by adjusting the length of the video according to the user's emotions, a video suitable for the user can be created.

[0115] The creation unit can determine the priority of videos based on the submission time of the generated images. The creation unit determines the priority of videos based on, for example, the submission time of the generated images. The creation unit, for example, prioritizes the creation of videos for urgent images. It can also prioritize the creation of videos for images with an approaching submission deadline. Furthermore, the creation unit can also determine the priority of videos based on the submission time specified by the user. In this way, by determining the priority of videos based on the submission time of the generated images, videos with high urgency can be created with priority.

[0116] The creation unit can adjust the order of the videos based on the relevance of the generated images. The creation unit adjusts the order of the videos based on, for example, the relevance of the generated images. The creation unit, for example, preferentially incorporates highly relevant images into the video. It can also postpone images with low relevance. Furthermore, the creation unit can adjust the order of the videos based on the relevance of images selected by the user. In this way, by adjusting the order of the videos based on the relevance of the generated images, it is possible to create highly relevant videos with priority.

[0117] The creation unit can adjust the level of detail of the video according to the user's level of expertise. The creation unit adjusts the level of detail of the video according to, for example, the user's level of expertise. For example, if the user has specialized knowledge, the creation unit can create a detailed video. Also, if the user does not have specialized knowledge, the creation unit can create a simplified video. Furthermore, the creation unit can adjust the level of detail of the video according to the user's level of expertise. In this way, by adjusting the level of detail of the video according to the user's level of expertise, a video suitable for the user can be created.

[0118] The voice unit can estimate the user's emotion and adjust the voice expression method based on the estimated user's emotion. The voice unit, for example, estimates the user's emotion and adjusts the voice expression method based on the estimated user's emotion. The voice unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is excited, an energetic voice can be provided. Also, if the user is relaxed, a calm voice can be provided. Furthermore, if the user is stressed, a simple and easy-to-understand voice can be provided. In this way, by adjusting the voice expression method according to the user's emotion, a voice suitable for the user can be provided.

[0119] The audio unit can adjust the level of detail of the audio based on the importance of the characters. For example, the audio unit adjusts the level of detail of the audio based on the importance of the characters. For example, the audio unit can provide detailed audio for important characters. For less important characters, the audio unit can also provide simplified audio. Furthermore, the audio unit can adjust the level of detail of the audio according to the importance of the characters selected by the user. In this way, by adjusting the level of detail of the audio based on the importance of the characters, audio suitable for the user can be provided.

[0120] The voice unit can apply different voice generation algorithms depending on the category of the character. For example, the voice unit applies different voice generation algorithms depending on the category of the character. For example, the voice unit applies a voice generation algorithm for the main character to the voice of the main character. Also, it can apply a voice generation algorithm for supporting characters to the voice of supporting characters. Furthermore, it can apply a voice generation algorithm for enemy characters to the voice of enemy characters. In this way, by applying different voice generation algorithms depending on the category of the character, it is possible to provide voices that are suitable for the user.

[0121] The voice unit can improve the accuracy of the voice by referring to the user's past voice generation results. The voice unit, for example, improves the accuracy of the voice by referring to the user's past voice generation results. The voice unit, for example, generates an optimal voice by referring to voices generated by the user in the past. The voice unit can also analyze the user's past voice generation results and improve the accuracy of the voice. Furthermore, the voice unit can also improve the accuracy of the voice by referring to feedback provided by the user in the past. In this way, the accuracy of the voice can be improved by referring to the user's past voice generation results.

[0122] The audio unit can estimate the user's emotion and adjust the length of the audio based on the estimated user's emotion. The audio unit, for example, estimates the user's emotion and adjusts the length of the audio based on the estimated user's emotion. The audio unit estimates the user's emotion using techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is excited, the audio unit can provide short, to-the-point audio. If the user is relaxed, the audio unit can provide longer audio with detailed explanations. Furthermore, if the user is stressed, the audio unit can provide concise, easy-to-understand audio. In this way, the audio length can be adjusted according to the user's emotion, thereby providing audio that is suitable for the user.

[0123] The audio unit can determine the priority of audio based on the time of submission of the characters. The audio unit determines the priority of audio based on, for example, the time of submission of the characters. The audio unit, for example, generates audio preferentially for urgent characters. The audio unit can also generate audio preferentially for characters whose submission deadline is approaching. Furthermore, the audio unit can determine the priority of audio based on the submission time specified by the user. In this way, by determining the priority of audio based on the time of submission of the characters, audio with high urgency can be generated preferentially.

[0124] The audio unit can adjust the order of the audio based on the relevance of the characters. For example, the audio unit adjusts the order of the audio based on the relevance of the characters. For example, the audio unit prioritizes incorporating highly relevant characters into the audio. It can also postpone incorporating less relevant characters. Furthermore, the audio unit can adjust the order of the audio based on the relevance of the characters selected by the user. In this way, by adjusting the order of the audio based on the relevance of the characters, it is possible to generate highly relevant audio with priority.

[0125] The audio unit can adjust the level of detail of the audio according to the user's level of expertise. For example, the audio unit adjusts the level of detail of the audio according to the user's level of expertise. For example, the audio unit provides detailed audio when the user has expertise. Alternatively, the audio unit can provide simplified audio when the user does not have expertise. Furthermore, the audio unit can adjust the level of detail of the audio according to the user's level of expertise. In this way, audio suitable for the user can be provided by adjusting the level of detail of the audio according to the user's level of expertise.

[0126] The music unit can estimate the user's emotions and adjust the music selection method based on the estimated user's emotions. The music unit, for example, estimates the user's emotions and adjusts the music selection method based on the estimated user's emotions. The music unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is excited, exciting music can be selected. Also, if the user is relaxed, calming music can be selected. Furthermore, if the user is feeling stressed, simple and easy-to-understand music can be selected. In this way, by adjusting the music selection method according to the user's emotions, music suitable for the user can be provided.

[0127] The music unit can adjust the level of detail of the music based on the importance of the scene. For example, the music unit adjusts the level of detail of the music based on the importance of the scene. For example, the music unit selects detailed music for important scenes. Alternatively, the music unit can select simplified music for less important scenes. Furthermore, the music unit can adjust the level of detail of the music according to the importance of the scene selected by the user. In this way, by adjusting the level of detail of the music based on the importance of the scene, music suitable for the user can be provided.

[0128] The music unit can apply different music selection algorithms depending on the scene category. For example, the music unit applies different music selection algorithms depending on the scene category. For example, the music unit applies an action music selection algorithm to an action scene. Also, it can apply a romance music selection algorithm to a romance scene. Furthermore, it can apply a comedy music selection algorithm to a comedy scene. In this way, by applying different music selection algorithms depending on the scene category, it is possible to provide music that is suitable for the user.

[0129] The music unit can improve the accuracy of the music by referring to the user's past music selection results. The music unit, for example, improves the accuracy of the music by referring to the user's past music selection results. The music unit, for example, selects optimal music by referring to music that the user has selected in the past. The music unit can also analyze the user's past music selection results to improve the accuracy of the music. Furthermore, the music unit can also improve the accuracy of the music by referring to feedback that the user has provided in the past. In this way, the accuracy of the music can be improved by referring to the user's past music selection results.

[0130] The music unit can estimate the user's emotion and adjust the length of the music based on the estimated user's emotion. The music unit, for example, estimates the user's emotion and adjusts the length of the music based on the estimated user's emotion. The music unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is excited, short, to-the-point music can be provided. Alternatively, if the user is relaxed, longer music with detailed explanations can be provided. Furthermore, if the user is stressed, concise, easy-to-understand music can be provided. In this way, music suitable for the user can be provided by adjusting the length of the music according to the user's emotion.

[0131] The music department can determine the priority of music based on the time of scene submission. The music department determines the priority of music based on, for example, the time of scene submission. The music department, for example, prioritizes music selection for urgent scenes. The music department can also prioritize music selection for scenes with an approaching submission deadline. Furthermore, the music department can also determine the priority of music based on the submission time specified by the user. In this way, by determining the priority of music based on the time of scene submission, it is possible to prioritize the selection of music with high urgency.

[0132] The music unit can adjust the order of music based on the relevance of scenes. For example, the music unit adjusts the order of music based on the relevance of scenes. For example, the music unit prioritizes incorporating highly relevant scenes into the music. It can also postpone less relevant scenes. Furthermore, the music unit can adjust the order of music based on the relevance of scenes selected by the user. In this way, by adjusting the order of music based on the relevance of scenes, it is possible to prioritize the selection of highly relevant music.

[0133] The music unit can adjust the level of detail of the music according to the user's level of expertise. For example, the music unit adjusts the level of detail of the music according to the user's level of expertise. For example, the music unit can provide detailed music if the user has specialized knowledge. Alternatively, the music unit can provide simplified music if the user does not have specialized knowledge. Furthermore, the music unit can adjust the level of detail of the music according to the user's level of expertise. In this way, music suitable for the user can be provided by adjusting the level of detail of the music according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, suggestion unit, generation unit, creation unit, audio unit, and music unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit accepts input from a user via the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests setting items based on information from the reception unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates images using a generation AI. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and converts the generated images into a video as consecutive frames. The audio unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates lines of characters using an audio database. The music unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects appropriate music based on the content of the scene. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, suggestion unit, generation unit, creation unit, audio unit, and music unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit accepts input from a user via the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests setting items based on information from the reception unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates images using a generation AI. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and converts the generated images into a video as consecutive frames. The audio unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates lines of characters using an audio database. The music unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects appropriate music based on the content of the scene. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, suggestion unit, generation unit, creation unit, audio unit, and music unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives input from a user via the microphone 238 of the headset-type terminal 314 or the communication I / F 26 of the data processing device 12. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests setting items based on information from the reception unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates images using a generation AI. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and converts the generated images into a video as consecutive frames. The audio unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates lines for characters using an audio database. The music unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects appropriate music based on the content of the scene. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, suggestion unit, generation unit, creation unit, audio unit, and music unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives input from a user via the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests setting items based on information from the reception unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates images using a generation AI. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and converts the generated images into a video as consecutive frames. The audio unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates lines for characters using a voice database. The music unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects appropriate music based on the content of the scene.

[0134] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0135] The reception unit can analyze the user's input in real time and provide immediate feedback based on the input. For example, if the user inputs "fantasy," the reception unit immediately suggests related setting items and guides the user on the next step based on the setting item selected by the user. In addition, if the user is unsure of what to input, the reception unit can refer to past input history and general trends to make appropriate suggestions. Furthermore, the reception unit can provide an optimal interface depending on the user's input speed and input method (voice, text, etc.). This allows the user to create content smoothly.

[0136] The suggestion unit can display samples of related content based on the user's input. For example, if the user inputs "adventure," the suggestion unit can display samples of previously created adventure stories for the user to refer to. The suggestion unit can also display related images and videos based on the user's input. Furthermore, the suggestion unit can suggest related character and background options based on the setting items selected by the user. This allows the user to create content while having a specific image in mind.

[0137] The generation unit can customize the style of the image to be generated based on the user's input. For example, if the user inputs "retro," the generation unit can generate a retro-style image. Alternatively, if the user inputs "modern," the generation unit can generate a modern-style image. Furthermore, the generation unit can apply a specific art style (e.g., anime style, realistic style, etc.) based on the settings selected by the user. This allows the user to generate an image in a style that suits their preferences.

[0138] The creation unit can create an interactive video based on the generated images. For example, an interactive video can be created in which the development of a story changes depending on the user's choice in a particular scene. The creation unit can also add interactive elements (e.g., clickable objects or options) to the video based on settings selected by the user. Furthermore, the creation unit can create more personalized content by allowing the user to edit specific parts of the video. This allows the user to create a unique video that is unique to them.

[0139] The voice unit can customize the tone and accent of a character's voice based on user input. For example, if the user inputs "authoritative voice," the voice unit can generate lines in an authoritative tone. Alternatively, if the user inputs "gentle voice," the voice unit can generate lines in a gentle tone. Additionally, the voice unit can apply a specific accent or dialect based on user-selected settings. This allows users to customize the character's voice to their liking.

[0140] The music unit can estimate the user's emotions and adjust the tempo and rhythm of the music based on the estimated user's emotions. For example, if the user is excited, the music unit can select exciting music with a fast tempo. If the user is relaxed, the music unit can select calming music with a slow tempo. Furthermore, if the user is stressed, the music unit can select simple, relaxing music. This makes it possible to provide optimal music according to the user's emotions.

[0141] The reception unit can estimate the user's emotions and adjust the difficulty of input based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can provide simple questions and options to help the user relax. Alternatively, if the user is excited, the reception unit can provide detailed setting items to help the user customize content more deeply. Furthermore, if the user is relaxed, the reception unit can provide input with a medium level of difficulty to help the user create content while having fun. This makes it possible to provide an optimal input experience according to the user's emotions.

[0142] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user's emotions. For example, if the user is excited, the suggestion unit can quickly suggest the next setting item to maintain the user's excitement. Also, if the user is relaxed, the suggestion unit can make suggestions at a slow pace to allow the user to proceed with the settings in a relaxed state. Furthermore, if the user is stressed, the suggestion unit can make minimal suggestions to allow the user to proceed with the settings without feeling stressed. This makes it possible to provide optimal suggestion timing according to the user's emotions.

[0143] The generation unit can estimate the user's emotion and adjust the color of the generated image based on the estimated user's emotion. For example, if the user is excited, the generation unit can generate an image with vivid and energetic colors. If the user is relaxed, the generation unit can also generate an image with calm colors. Furthermore, if the user is stressed, the generation unit can also generate an image with simple, highly visible colors. This makes it possible to provide an image with optimal colors according to the user's emotion.

[0144] The creation unit can estimate the user's emotions and adjust the video editing method based on the estimated user's emotions. For example, if the user is excited, the creation unit can edit the video at a fast tempo to create an exciting video. If the user is relaxed, the creation unit can edit the video at a slower tempo to create a calmer video. Furthermore, if the user is stressed, the creation unit can edit the video in a simple, highly visible way so that the user can enjoy the video without feeling stressed. This makes it possible to provide the optimal editing method according to the user's emotions.

[0145] The processing flow of the second embodiment will be briefly explained below.

[0146] Step 1: The reception unit receives input from the user, which may include text input, voice input, image input, and the like. Step 2: The suggestion unit suggests setting items based on the information received by the reception unit. For example, if the user inputs "fantasy," the suggestion unit suggests setting items such as "Character: Wizard, Background: Magical Forest, Story: Adventure." Step 3: The generator generates an image based on the settings proposed by the suggester. Using generative AI, techniques such as deep learning models and generative artificial network (GAN) are used to generate an image that matches the settings. Step 4: The creator converts the generated images into a video as a series of frames. The creator generates the series of frames based on the frame rate and the transition method between frames. Step 5: The voice department uses a pre-trained voice database to generate the characters' lines. The voice synthesis technology is used to generate the characters' lines. Step 6: The music department selects appropriate music based on the content of the scene. Music is selected based on the theme and atmosphere of the scene.

[0147] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0152] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0158] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0168] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0169] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0170] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0171] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0173] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0174] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0175] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0176] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0177] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0178] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0179] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0184] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0185] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0186] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0187] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0188] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0189] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0190] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0191] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0192] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0193] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0194] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0195] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0196] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0197] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0198] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0199] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0200] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0201] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0202] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0203] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0204] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0205] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0206] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0207] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0208] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0209] 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.

[0210] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0211] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0212] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0213] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0214] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0215] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0216] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0217] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0218] [Explanation of symbols]

[0219] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input from a user; a suggestion unit that suggests setting items based on the information received by the reception unit; a generation unit that generates an image based on the setting items proposed by the proposal unit; a creating unit that converts the generated images into a video as a series of frames; an audio unit that adds audio to the video created by the creation unit; a music unit that adds music to the video based on the audio added by the audio unit. A system characterized by:

2. The proposal unit Suggest specific settings based on user input 2. The system of claim 1.

3. The generation unit Uses AI to generate images that match the settings 2. The system of claim 1.

4. The creation unit Convert the generated images into a video as a series of frames 2. The system of claim 1.

5. The audio section Generates lines for characters using a pre-trained voice database 2. The system of claim 1.

6. The music club: Selecting appropriate music based on the content of the scene 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of input based on the estimated user emotions.

2. The system of claim 1.

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

Images