system

Abstract

The system according to this embodiment aims to streamline the manga production process and unleash the creativity of creators. [Solution] The system according to the embodiment comprises a generation unit, a proposal unit, and a management unit. The generation unit generates character designs based on the creator's settings. The proposal unit proposes a story development based on the character designs generated by the generation unit. The generation unit generates dialogue based on the story development proposed by the proposal unit. The management unit manages the dialogue generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that operations such as character design, story development, and dialogue generation in the comic production process require time and effort and are difficult to perform efficiently.

[0005] The system according to the embodiment aims to improve the efficiency of the comic production process and bring out the creativity of the creator.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a generation unit, a proposal unit, and a management unit. The generation unit generates character designs based on the creator's settings. The proposal unit proposes a story development based on the character designs generated by the generation unit. The generation unit generates dialogue based on the story development proposed by the proposal unit. The management unit manages the dialogue generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the manga production process and unleash the creativity of creators. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The MangaAI Creator system according to an embodiment of the present invention is an AI platform that revolutionizes the manga creation process. This MangaAI Creator system provides, as functions for creators, an AI character generator, a story assistant, a dialogue generator, and a production management tool. The AI character generator generates unique character designs based on detailed settings, which can be finely adjusted by the creator. The story assistant proposes an optimal story development from ideas using natural language processing technology. The dialogue generator automatically generates conversations that reflect the personalities of the characters. Next, as functions for readers, it provides personalized recommendations and an AI translation and summarization function. It recommends works that match the reader's preferences using a machine learning algorithm, and provides a manga experience that transcends language barriers with its multilingual translation and summarization functions. For example, the MangaAI Creator system generates unique character designs based on detailed settings such as the appearance, personality, and background settings of the characters set by the creator. The generated character designs can be finely adjusted by the creator. Also, the story assistant proposes an optimal story development from the creator's ideas using natural language processing technology. For example, the story assistant proposes story development considering the progress of the plot, the roles of the characters, the setting of the scenes, etc. Furthermore, the dialogue generator automatically generates conversations that reflect the personalities of the characters. For example, the dialogue generator generates conversations considering the content, tone, style, etc. of the conversations between the characters. As a function for readers, personalized recommendations recommend works that match the reader's preferences using a machine learning algorithm based on the reader's past browsing history, questionnaire results, purchase history, etc. For example, personalized recommendations refer to the trends of works that the reader has highly evaluated in the past and recommend new works. Also, the AI translation and summarization function performs multilingual translation and summarization, providing the reader with a manga experience that transcends language barriers. For example, the AI translation and summarization function performs translation and summarization considering the types of corresponding languages, the accuracy of translation, the method of summarization, etc.This allows the MangaAI Creator system to streamline the manga production process, unleash the creativity of creators, and provide readers with a new manga experience.

[0029] The MangaAI Creator system according to this embodiment comprises a generation unit, a proposal unit, another generation unit, and a management unit. The generation unit generates character designs based on the creator's settings. The generation unit generates unique character designs based on detailed settings such as the character's appearance, personality, and background settings set by the creator. The generation unit uses AI, for example, to take the detailed character settings set by the creator as input and output a unique character design. The proposal unit proposes a story development based on the character designs generated by the generation unit. The proposal unit proposes a story development based on the generated character designs, taking into account the plot progression, character roles, scene settings, etc. The proposal unit uses natural language processing technology, for example, to take the generated character designs as input and output an optimal story development. The generation unit generates dialogue based on the story development proposed by the proposal unit. The generation unit generates dialogue based on the proposed story development, taking into account the content, tone, and writing style of conversations between characters. The generation unit uses AI, for example, to take the proposed story development as input and output dialogues between characters. The management unit manages the dialogues generated by the generation unit. The management department, for example, organizes the generated dialogues and makes corrections or additions as needed. The management department, for example, uses AI as input to manage the dialogues. As a result, the MangaAI Creator system according to this embodiment can generate character designs based on the creator's settings, propose story developments, generate dialogues, and manage them.

[0030] The generation unit generates character designs based on the creator's settings. For example, the generation unit generates unique character designs based on detailed settings such as the character's appearance, personality, and background, as set by the creator. Specifically, it receives information about the character's appearance (e.g., hair color, eye shape, clothing style, etc.) and personality (e.g., cheerful, calm, brave, etc.) provided by the creator as input, and the AI ​​generates character designs based on this information. The AI ​​uses deep learning technology to utilize patterns learned from a vast database of character designs and outputs the design that best suits the creator's settings. Furthermore, information about the background settings (e.g., the character's birthplace, occupation, special skills, etc.) is also taken into consideration, and a design that matches the character's overall atmosphere and story is generated. The generated character design can be reviewed by the creator and modified or adjusted as needed. This allows the generation unit to efficiently produce high-quality character designs that reflect the creator's intentions.

[0031] The proposal unit proposes story developments based on the character designs generated by the generation unit. For example, the proposal unit proposes story developments based on the generated character designs, taking into account the plot progression, character roles, and scene settings. Specifically, it analyzes the characteristics and background settings of the generated character designs and constructs the optimal story plot for them. The AI ​​uses natural language processing technology to automatically generate story progressions based on the characters' personalities and backgrounds. For example, it proposes an adventurous plot for a brave character and a mysterious plot for a calm character. It also considers the relationships and conflicts between characters and proposes developments that enhance the tension and drama of the story. The proposed story developments can be reviewed by the creator and modified or adjusted as needed. This allows the proposal unit to efficiently propose compelling story developments that reflect the creator's intentions.

[0032] The generation unit generates dialogue based on the story development proposed by the proposal unit. For example, based on the proposed story development, the generation unit generates dialogue considering the content, tone, and style of conversations between characters. Specifically, it analyzes the roles and emotions of the characters in each scene of the proposed story and generates natural dialogue accordingly. The AI ​​uses natural language generation technology to select appropriate words and expressions according to the characters' personalities and situations, creating realistic dialogue. For example, it generates short, sharp dialogue in tense scenes and long, emotionally rich dialogue in emotional scenes. It also incorporates unique tones and turns of phrase that reflect the characters' personalities, giving the dialogue depth and realism. The generated dialogue can be reviewed by the creator and modified or adjusted as needed. This allows the generation unit to efficiently generate high-quality dialogue that reflects the creator's intentions.

[0033] The management department manages the dialogue generated by the generation department. For example, the management department organizes the generated dialogue and makes corrections or additions as needed. Specifically, it classifies the generated dialogue by scene and organizes it according to the flow of the story. It also checks whether the content and tone of the dialogue are consistent and makes corrections as needed. AI can analyze the generated dialogue, automatically detect grammatical errors and unnatural expressions, and suggest corrections. Furthermore, it provides an interface that allows creators to easily edit dialogue they want to add or parts they want to change. The management department also manages versions of the generated dialogue, saves past revision history, and allows users to revert to previous versions as needed. In this way, the management department can efficiently manage the generated dialogue and provide an environment in which creators can flexibly edit while maintaining the quality of the story.

[0034] The recommendation department recommends works based on the reader's preferences. For example, the recommendation department recommends works that match the reader's preferences based on the reader's past browsing history, survey results, purchase history, etc. For example, the recommendation department uses machine learning algorithms to identify the reader's preferences and recommend the most suitable works. For example, the recommendation department refers to the trends of works that the reader has previously given high ratings to recommend new works. For example, the recommendation department incorporates the genres and themes of works that the reader has previously viewed into new recommendations. For example, the recommendation department analyzes the reader's past recommendation history to recommend the most suitable works. In this way, the recommendation department can recommend works based on the reader's preferences.

[0035] The Translation Department performs multilingual translation and summarization. The Translation Department considers factors such as the types of languages ​​to be translated, the accuracy of the translation, and the summarization method when performing translation and summarization. For example, the Translation Department uses AI to take the source text as input and output the translated text. For example, the Translation Department uses AI to summarize the source text and output the summarized text. For example, the Translation Department selects the optimal translation method based on the types of languages ​​to be translated. For example, the Translation Department uses translation models specialized for specific language pairs to improve translation accuracy. For example, the Translation Department considers factors such as the length of the text and the importance of the information being summarized when determining the summarization method. This enables the Translation Department to perform multilingual translation and summarization.

[0036] The generation unit can generate unique character designs based on detailed settings. For example, the generation unit generates unique character designs based on detailed settings such as the character's appearance, personality, and background, set by the creator. For example, the generation unit uses AI as input to output unique character designs based on detailed settings set by the creator. For example, the generation unit generates character designs with a unique style and distinctive appearance based on detailed settings such as the character's background, personality, and appearance, set by the creator. For example, the generation unit uses AI as input to output character designs with a unique style and distinctive appearance, set by the creator. This allows the generation unit to generate unique character designs based on detailed settings.

[0037] The proposal unit can use natural language processing technology to suggest the optimal story development from an idea. For example, the proposal unit uses natural language processing technology to suggest the optimal story development from a creator's idea. The proposal unit suggests a story development considering factors such as plot progression, character roles, and scene settings. For example, the proposal unit uses AI to take a creator's idea as input and output the optimal story development. For example, the proposal unit uses natural language processing technologies such as morphological analysis, grammatical analysis, and semantic analysis to analyze a creator's idea and suggest the optimal story development. For example, the proposal unit uses AI to take a creator's idea as input and uses natural language processing technologies such as morphological analysis, grammatical analysis, and semantic analysis to output the optimal story development. As a result, the proposal unit can use natural language processing technology to suggest the optimal story development from an idea.

[0038] The generation unit can automatically generate dialogue that reflects the character's personality. The generation unit, for example, automatically generates dialogue that reflects the character's personality. The generation unit generates dialogue considering, for example, the content, tone, and style of conversations between characters. The generation unit uses, for example, AI to generate dialogue that reflects the character's personality. The generation unit generates dialogue that reflects the character's personality based on, for example, detailed settings such as the character's personality, background, and appearance. The generation unit uses, for example, AI to take detailed settings such as the character's personality, background, and appearance as input and output dialogue that reflects the character's personality. As a result, the generation unit can automatically generate dialogue that reflects the character's personality.

[0039] The management department can manage the generated dialogues. For example, the management department can organize the generated dialogues and make corrections or additions as needed. For example, the management department can use AI to manage the generated dialogues as input. For example, the management department can manage the generated dialogues by considering their content, tone, and style. For example, the management department can use AI to manage the generated dialogues as input. This allows the management department to manage the generated dialogues.

[0040] The recommendation system can use machine learning algorithms to recommend works that match the reader's preferences. For example, the recommendation system recommends works that match the reader's preferences based on their past browsing history, survey results, and purchase history. For example, the recommendation system uses machine learning algorithms to identify the reader's preferences and recommend the most suitable works. For example, the recommendation system refers to trends in works that readers have previously given high ratings to recommend new works. For example, the recommendation system incorporates the genres and themes of works that readers have previously viewed into new recommendations. For example, the recommendation system analyzes the reader's past recommendation history to recommend the most suitable works. In this way, the recommendation system can use machine learning algorithms to recommend works that match the reader's preferences.

[0041] The translation unit can perform multilingual translation and summarization. The translation unit considers factors such as the types of languages ​​to be translated, the accuracy of the translation, and the summarization method when performing translation and summarization. For example, the translation unit can use AI to take the source text as input and output the translated text. For example, the translation unit can use AI to summarize the source text and output the summarized text. For example, the translation unit can select the optimal translation method based on the types of languages ​​to be translated. For example, the translation unit can use translation models specialized for specific language pairs to improve translation accuracy. For example, the translation unit can consider factors such as the length of the text and the importance of the information being summarized when determining the summarization method. This enables the translation unit to perform multilingual translation and summarization.

[0042] The generation unit can maintain design consistency by referencing the user's past design history when generating character designs. For example, the generation unit can reference the color scheme and style of characters the user has created in the past and reflect them in the new design. For example, the generation unit can incorporate specific design elements (e.g., specific accessories or clothing) that the user has used in the past into the new design. For example, the generation unit can reference the poses and expressions of characters the user has created in the past to ensure consistency in the new design. For example, the generation unit can use AI to take the user's past design history as input and reflect it in the new design. In this way, the generation unit can maintain design consistency by referencing the user's past design history.

[0043] The generation unit can determine the design direction based on the user's current project theme when generating character designs. For example, if the user's theme is fantasy, the generation unit will generate designs incorporating magic and fantastical elements. For example, if the user's theme is cyberpunk, the generation unit will generate futuristic and mechanical designs. For example, if the user's theme is horror, the generation unit will generate designs incorporating dark colors and eerie elements. The generation unit can, for example, use AI as input to determine the design direction based on the user's current project theme. This allows the generation unit to determine the design direction based on the user's current project theme.

[0044] The generation unit can incorporate region-specific design elements by considering the user's geographical location when generating character designs. For example, if the user is in Japan, the generation unit will incorporate Japanese-style design elements (e.g., kimono and samurai attire). If the user is in America, the generation unit will incorporate cowboy and Western elements. If the user is in Europe, the generation unit will incorporate medieval knight and castle elements. The generation unit can use AI, for example, to take the user's geographical location as input and incorporate region-specific design elements. This allows the generation unit to incorporate region-specific design elements while considering the user's geographical location.

[0045] The generation unit can analyze the user's social media activity and generate designs that reflect trends when creating character designs. For example, the generation unit can incorporate design elements that have received many "likes" from the user on social media into new designs. For example, the generation unit can generate designs by referencing the styles of popular artists that the user follows. For example, the generation unit can analyze the trends of communities that the user participates in and generate designs based on those trends. For example, the generation unit can use AI to take the user's social media activity as input and generate designs that reflect trends. In this way, the generation unit can analyze the user's social media activity and generate designs that reflect trends.

[0046] The proposal function can maintain consistency when proposing story developments by referring to past story development history. For example, the proposal function can refer to the themes and tones of stories previously created by the user and reflect them in the new story development. For example, the proposal function can incorporate specific plot points or character development used by the user in the past into the new story development. For example, the proposal function can ensure consistency in the new story development with the settings and worldview of stories previously created by the user. For example, the proposal function can use AI to take past story development history as input and reflect it in the new story development. This allows the proposal function to maintain consistency by referring to past story development history.

[0047] The proposal department can adjust its proposed story developments to take into account the growth and changes of the characters. For example, the proposal department can propose story developments that reflect past events and experiences in order to depict the process of a character's growth. For example, the proposal department can propose reactions and actions to new situations that take into account changes in the character's personality and values. For example, the proposal department can propose story developments that reflect changes in the characters' relationships. For example, the proposal department can use AI as input to adjust the proposed content based on the growth and changes of the characters. This allows the proposal department to adjust its proposed content to take into account the growth and changes of the characters.

[0048] The proposal department can incorporate region-specific culture and background information by considering the user's geographical location when proposing story developments. For example, if the user is in Japan, the proposal department will propose a story development that incorporates Japanese culture and scenery. For example, if the user is in the United States, the proposal department will propose a story development that incorporates American culture and scenery. For example, if the user is in Europe, the proposal department will propose a story development that incorporates European culture and scenery. The proposal department can use AI, for example, to take the user's geographical location information as input and incorporate region-specific culture and background information. This allows the proposal department to incorporate region-specific culture and background information by considering the user's geographical location information.

[0049] The proposal team can analyze users' social media activity and propose story developments that reflect current trends when suggesting storylines. For example, the proposal team can incorporate themes and topics that have garnered many "likes" from users on social media into new story developments. For example, the proposal team can suggest story developments based on trends in popular accounts that users follow. For example, the proposal team can analyze trends in communities that users participate in and propose story developments based on those trends. For example, the proposal team can use AI to take users' social media activity as input and propose story developments that reflect current trends. In this way, the proposal team can analyze users' social media activity and propose story developments that reflect current trends.

[0050] The generation unit can maintain consistency by referring to the character's past dialogue history when generating dialogue. For example, the generation unit can incorporate specific phrases or catchphrases that the character has used in the past into new dialogue. For example, the generation unit can generate consistent dialogue based on the character's personality and values. For example, the generation unit can generate dialogue that reflects the character's relationships and past events. For example, the generation unit can use AI to take the character's past dialogue history as input and reflect it in new dialogue. This allows the generation unit to maintain consistency by referring to the character's past dialogue history.

[0051] The generation unit can adjust the content of dialogue according to the story's progress during dialogue generation. For example, the generation unit can generate dialogue that creates tension as the story approaches its climax. For example, in the early stages of the story, the generation unit can generate dialogue that includes character introductions and background explanations. For example, in the later stages of the story, the generation unit can generate dialogue that reflects the characters' growth and changes. For example, the generation unit can use AI to take the story's progress as input and adjust the content of the dialogue. In this way, the generation unit can adjust the content of dialogue according to the story's progress.

[0052] The generation unit can incorporate region-specific expressions and phrases by considering the user's geographical location when generating dialogue. For example, if the user is in Japan, the generation unit will incorporate Japanese expressions and phrases into the dialogue. For example, if the user is in the United States, the generation unit will incorporate American slang and expressions into the dialogue. For example, if the user is in Europe, the generation unit will generate dialogue that reflects European culture and background. The generation unit can, for example, use AI as input to incorporate region-specific expressions and phrases by considering the user's geographical location.

[0053] The generation unit can analyze the user's social media activity and generate dialogues that reflect trends during dialogue generation. For example, the generation unit can incorporate topics or themes that have received many "likes" from the user on social media into new dialogues. For example, the generation unit can generate dialogues by referencing trends from popular accounts that the user follows. For example, the generation unit can analyze trends in communities that the user participates in and generate dialogues based on that. For example, the generation unit can use AI to take the user's social media activity as input and generate dialogues that reflect trends. In this way, the generation unit can analyze the user's social media activity and generate dialogues that reflect trends.

[0054] The management department can maintain consistency during conversational management by referring to past management history. For example, the management department can refer to settings and changes that users have made to conversational management in the past and reflect them in new management. For example, the management department can prioritize displaying specific management options that users have used in the past. For example, the management department can analyze a user's past management history and suggest the optimal management method. For example, the management department can use AI to input past management history and reflect it in new management. This allows the management department to maintain consistency by referring to past management history.

[0055] The management unit can select the optimal management method during interaction management by considering the user's device information. For example, if the user is using a smartphone, the management unit provides a management interface that matches the screen size. For example, if the user is using a tablet, the management unit provides a management interface optimized for a larger screen. For example, if the user is using a desktop, the management unit provides detailed management options. For example, the management unit can use AI to input the user's device information and select the optimal management method. This allows the management unit to select the optimal management method by considering the user's device information.

[0056] The recommendation system can maintain consistency by referring to past recommendation history when making recommendations. For example, the recommendation system can refer to the trends of works that users have previously given high ratings to and reflect them in new recommendations. For example, the recommendation system can incorporate the genres and themes of works that users have previously viewed into new recommendations. For example, the recommendation system can analyze a user's past recommendation history and recommend the most suitable works. For example, the recommendation system can use AI to input past recommendation history and reflect it in new recommendations. This allows the recommendation system to maintain consistency by referring to past recommendation history.

[0057] The recommendation system can prioritize recommending region-specific works by considering the user's geographical location. For example, if the user is in Japan, the recommendation system will recommend works that incorporate Japanese culture and scenery. If the user is in the United States, the recommendation system will recommend works that incorporate American culture and scenery. If the user is in Europe, the recommendation system will recommend works that incorporate European culture and scenery. The recommendation system can use AI, for example, to input the user's geographical location and prioritize recommending region-specific works. This allows the recommendation system to prioritize recommending region-specific works by considering the user's geographical location.

[0058] The translation department can maintain consistency by referring to past translation history during the translation process. For example, the department can refer to the style and tone of translations previously made by the user and reflect them in new translations. For example, the department can incorporate specific terminology or expressions previously used by the user into new translations. For example, the department can analyze the user's past translation history and suggest the optimal translation method. For example, the department can use AI to take past translation history as input and reflect it in new translations. This allows the translation department to maintain consistency by referring to past translation history.

[0059] The translation unit can incorporate region-specific expressions by considering the user's geographical location during translation. For example, if the user is in Japan, the unit will incorporate Japanese expressions and phrases into the translation. For example, if the user is in the United States, the unit will incorporate American slang and expressions into the translation. For example, if the user is in Europe, the unit will incorporate expressions that reflect European culture and background into the translation. The translation unit can, for example, use AI as input to incorporate region-specific expressions based on the user's geographical location. This allows the translation unit to incorporate region-specific expressions while considering the user's geographical location.

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

[0061] The generation unit can maintain design consistency by referencing the user's past design history when generating character designs. For example, the generation unit can reference the color scheme and style of characters the user has created in the past and reflect them in the new design. For example, the generation unit can incorporate specific design elements (e.g., specific accessories or clothing) that the user has used in the past into the new design. For example, the generation unit can reference the poses and expressions of characters the user has created in the past to ensure consistency in the new design. For example, the generation unit can use AI to take the user's past design history as input and reflect it in the new design. In this way, the generation unit can maintain design consistency by referencing the user's past design history.

[0062] The generation unit can determine the design direction based on the user's current project theme when generating character designs. For example, if the user's theme is fantasy, the generation unit will generate designs incorporating magic and fantastical elements. For example, if the user's theme is cyberpunk, the generation unit will generate futuristic and mechanical designs. For example, if the user's theme is horror, the generation unit will generate designs incorporating dark colors and eerie elements. The generation unit can, for example, use AI as input to determine the design direction based on the user's current project theme. This allows the generation unit to determine the design direction based on the user's current project theme.

[0063] The generation unit can incorporate region-specific design elements by considering the user's geographical location when generating character designs. For example, if the user is in Japan, the generation unit will incorporate Japanese-style design elements (e.g., kimono and samurai attire). If the user is in America, the generation unit will incorporate cowboy and Western elements. If the user is in Europe, the generation unit will incorporate medieval knight and castle elements. The generation unit can use AI, for example, to take the user's geographical location as input and incorporate region-specific design elements. This allows the generation unit to incorporate region-specific design elements while considering the user's geographical location.

[0064] The generation unit can analyze the user's social media activity and generate designs that reflect trends when creating character designs. For example, the generation unit can incorporate design elements that have received many "likes" from the user on social media into new designs. For example, the generation unit can generate designs by referencing the styles of popular artists that the user follows. For example, the generation unit can analyze the trends of communities that the user participates in and generate designs based on those trends. For example, the generation unit can use AI to take the user's social media activity as input and generate designs that reflect trends. In this way, the generation unit can analyze the user's social media activity and generate designs that reflect trends.

[0065] The proposal function can maintain consistency when proposing story developments by referring to past story development history. For example, the proposal function can refer to the themes and tones of stories previously created by the user and reflect them in the new story development. For example, the proposal function can incorporate specific plot points or character development used by the user in the past into the new story development. For example, the proposal function can ensure consistency in the new story development with the settings and worldview of stories previously created by the user. For example, the proposal function can use AI to take past story development history as input and reflect it in the new story development. This allows the proposal function to maintain consistency by referring to past story development history.

[0066] The proposal department can adjust its proposed story developments to take into account the growth and changes of the characters. For example, the proposal department can propose story developments that reflect past events and experiences in order to depict the process of a character's growth. For example, the proposal department can propose reactions and actions to new situations that take into account changes in the character's personality and values. For example, the proposal department can propose story developments that reflect changes in the characters' relationships. For example, the proposal department can use AI as input to adjust the proposed content based on the growth and changes of the characters. This allows the proposal department to adjust its proposed content to take into account the growth and changes of the characters.

[0067] The proposal department can incorporate region-specific culture and background information by considering the user's geographical location when proposing story developments. For example, if the user is in Japan, the proposal department will propose a story development that incorporates Japanese culture and scenery. For example, if the user is in the United States, the proposal department will propose a story development that incorporates American culture and scenery. For example, if the user is in Europe, the proposal department will propose a story development that incorporates European culture and scenery. The proposal department can use AI, for example, to take the user's geographical location information as input and incorporate region-specific culture and background information. This allows the proposal department to incorporate region-specific culture and background information by considering the user's geographical location information.

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

[0069] Step 1: The generation unit generates character designs based on the creator's settings. For example, the generation unit generates unique character designs based on detailed settings such as the character's appearance, personality, and background settings set by the creator. The generation unit uses AI to take the detailed character settings set by the creator as input and outputs unique character designs. Step 2: The proposal unit proposes a story development based on the character designs generated by the generation unit. Based on the generated character designs, the proposal unit proposes a story development considering the plot progression, character roles, scene settings, etc. Using natural language processing technology, the proposal unit takes the generated character designs as input and outputs the optimal story development. Step 3: The generation unit generates dialogue based on the story development proposed by the proposal unit. Based on the proposed story development, the generation unit generates dialogue considering the content, tone, and style of conversations between characters. The generation unit uses AI to take the proposed story development as input and outputs dialogue between characters. Step 4: The management unit manages the dialogues generated by the generation unit. The management unit organizes the generated dialogues and makes corrections or additions as needed. The management unit uses AI as input to manage the dialogues.

[0070] (Example of form 2) The MangaAI Creator system according to an embodiment of the present invention is an AI platform that revolutionizes the comic creation process. This MangaAI Creator system provides, as functions for creators, an AI character generator, a story assistant, a dialogue generator, and a production management tool. The AI character generator generates a unique character design based on detailed settings, which can be finely adjusted by the creator. The story assistant proposes an optimal story development from ideas using natural language processing technology. The dialogue generator automatically generates dialogues that reflect the personalities of the characters. Next, as functions for readers, it provides personalized recommendations and an AI translation / summarization function. It recommends works that suit the reader's preferences using machine learning algorithms and provides a comic experience that transcends language barriers with its multilingual translation and summarization functions. For example, the MangaAI Creator system generates a unique character design based on detailed settings such as the appearance, personality, and background of the characters set by the creator. The generated character design can be finely adjusted by the creator. Also, the story assistant proposes an optimal story development from the creator's ideas using natural language processing technology. For example, the story assistant proposes a story development considering the progression of the plot, the roles of the characters, the setting of the scenes, etc. Furthermore, the dialogue generator automatically generates dialogues that reflect the personalities of the characters. For example, the dialogue generator generates dialogues considering the content, tone, style, etc. of the conversations between the characters. As a function for readers, personalized recommendations recommend works that suit the reader's preferences based on the reader's past browsing history, questionnaire results, purchase history, etc. using machine learning algorithms. For example, personalized recommendations refer to the tendencies of works that the reader has highly evaluated in the past and recommend new works. Also, the AI translation / summarization function performs multilingual translation and summarization and provides a comic experience that transcends language barriers for readers. For example, the AI translation / summarization function performs translation and summarization considering the types of corresponding languages, translation accuracy, summarization methods, etc.This allows the MangaAI Creator system to streamline the manga production process, unleash the creativity of creators, and provide readers with a new manga experience.

[0071] The MangaAI Creator system according to this embodiment comprises a generation unit, a proposal unit, another generation unit, and a management unit. The generation unit generates character designs based on the creator's settings. The generation unit generates unique character designs based on detailed settings such as the character's appearance, personality, and background settings set by the creator. The generation unit uses AI, for example, to take the detailed character settings set by the creator as input and output a unique character design. The proposal unit proposes a story development based on the character designs generated by the generation unit. The proposal unit proposes a story development based on the generated character designs, taking into account the plot progression, character roles, scene settings, etc. The proposal unit uses natural language processing technology, for example, to take the generated character designs as input and output an optimal story development. The generation unit generates dialogue based on the story development proposed by the proposal unit. The generation unit generates dialogue based on the proposed story development, taking into account the content, tone, and writing style of conversations between characters. The generation unit uses AI, for example, to take the proposed story development as input and output dialogues between characters. The management unit manages the dialogues generated by the generation unit. The management department, for example, organizes the generated dialogues and makes corrections or additions as needed. The management department, for example, uses AI as input to manage the dialogues. As a result, the MangaAI Creator system according to this embodiment can generate character designs based on the creator's settings, propose story developments, generate dialogues, and manage them.

[0072] The generation unit generates character designs based on the creator's settings. For example, the generation unit generates unique character designs based on detailed settings such as the character's appearance, personality, and background, as set by the creator. Specifically, it receives information about the character's appearance (e.g., hair color, eye shape, clothing style, etc.) and personality (e.g., cheerful, calm, brave, etc.) provided by the creator as input, and the AI ​​generates character designs based on this information. The AI ​​uses deep learning technology to utilize patterns learned from a vast database of character designs and outputs the design that best suits the creator's settings. Furthermore, information about the background settings (e.g., the character's birthplace, occupation, special skills, etc.) is also taken into consideration, and a design that matches the character's overall atmosphere and story is generated. The generated character design can be reviewed by the creator and modified or adjusted as needed. This allows the generation unit to efficiently produce high-quality character designs that reflect the creator's intentions.

[0073] The proposal unit proposes story developments based on the character designs generated by the generation unit. For example, the proposal unit proposes story developments based on the generated character designs, taking into account the plot progression, character roles, and scene settings. Specifically, it analyzes the characteristics and background settings of the generated character designs and constructs the optimal story plot for them. The AI ​​uses natural language processing technology to automatically generate story progressions based on the characters' personalities and backgrounds. For example, it proposes an adventurous plot for a brave character and a mysterious plot for a calm character. It also considers the relationships and conflicts between characters and proposes developments that enhance the tension and drama of the story. The proposed story developments can be reviewed by the creator and modified or adjusted as needed. This allows the proposal unit to efficiently propose compelling story developments that reflect the creator's intentions.

[0074] The generation unit generates dialogue based on the story development proposed by the proposal unit. For example, based on the proposed story development, the generation unit generates dialogue considering the content, tone, and style of conversations between characters. Specifically, it analyzes the roles and emotions of the characters in each scene of the proposed story and generates natural dialogue accordingly. The AI ​​uses natural language generation technology to select appropriate words and expressions according to the characters' personalities and situations, creating realistic dialogue. For example, it generates short, sharp dialogue in tense scenes and long, emotionally rich dialogue in emotional scenes. It also incorporates unique tones and turns of phrase that reflect the characters' personalities, giving the dialogue depth and realism. The generated dialogue can be reviewed by the creator and modified or adjusted as needed. This allows the generation unit to efficiently generate high-quality dialogue that reflects the creator's intentions.

[0075] The management department manages the dialogue generated by the generation department. For example, the management department organizes the generated dialogue and makes corrections or additions as needed. Specifically, it classifies the generated dialogue by scene and organizes it according to the flow of the story. It also checks whether the content and tone of the dialogue are consistent and makes corrections as needed. AI can analyze the generated dialogue, automatically detect grammatical errors and unnatural expressions, and suggest corrections. Furthermore, it provides an interface that allows creators to easily edit dialogue they want to add or parts they want to change. The management department also manages versions of the generated dialogue, saves past revision history, and allows users to revert to previous versions as needed. In this way, the management department can efficiently manage the generated dialogue and provide an environment in which creators can flexibly edit while maintaining the quality of the story.

[0076] The recommendation department recommends works based on the reader's preferences. For example, the recommendation department recommends works that match the reader's preferences based on the reader's past browsing history, survey results, purchase history, etc. For example, the recommendation department uses machine learning algorithms to identify the reader's preferences and recommend the most suitable works. For example, the recommendation department refers to the trends of works that the reader has previously given high ratings to recommend new works. For example, the recommendation department incorporates the genres and themes of works that the reader has previously viewed into new recommendations. For example, the recommendation department analyzes the reader's past recommendation history to recommend the most suitable works. In this way, the recommendation department can recommend works based on the reader's preferences.

[0077] The Translation Department performs multilingual translation and summarization. The Translation Department considers factors such as the types of languages ​​to be translated, the accuracy of the translation, and the summarization method when performing translation and summarization. For example, the Translation Department uses AI to take the source text as input and output the translated text. For example, the Translation Department uses AI to summarize the source text and output the summarized text. For example, the Translation Department selects the optimal translation method based on the types of languages ​​to be translated. For example, the Translation Department uses translation models specialized for specific language pairs to improve translation accuracy. For example, the Translation Department considers factors such as the length of the text and the importance of the information being summarized when determining the summarization method. This enables the Translation Department to perform multilingual translation and summarization.

[0078] The generation unit can generate unique character designs based on detailed settings. For example, the generation unit generates unique character designs based on detailed settings such as the character's appearance, personality, and background, set by the creator. For example, the generation unit uses AI as input to output unique character designs based on detailed settings set by the creator. For example, the generation unit generates character designs with a unique style and distinctive appearance based on detailed settings such as the character's background, personality, and appearance, set by the creator. For example, the generation unit uses AI as input to output character designs with a unique style and distinctive appearance, set by the creator. This allows the generation unit to generate unique character designs based on detailed settings.

[0079] The proposal unit can use natural language processing technology to suggest the optimal story development from an idea. For example, the proposal unit uses natural language processing technology to suggest the optimal story development from a creator's idea. The proposal unit suggests a story development considering factors such as plot progression, character roles, and scene settings. For example, the proposal unit uses AI to take a creator's idea as input and output the optimal story development. For example, the proposal unit uses natural language processing technologies such as morphological analysis, grammatical analysis, and semantic analysis to analyze a creator's idea and suggest the optimal story development. For example, the proposal unit uses AI to take a creator's idea as input and uses natural language processing technologies such as morphological analysis, grammatical analysis, and semantic analysis to output the optimal story development. As a result, the proposal unit can use natural language processing technology to suggest the optimal story development from an idea.

[0080] The generation unit can automatically generate dialogue that reflects the character's personality. The generation unit, for example, automatically generates dialogue that reflects the character's personality. The generation unit generates dialogue considering, for example, the content, tone, and style of conversations between characters. The generation unit uses, for example, AI to generate dialogue that reflects the character's personality. The generation unit generates dialogue that reflects the character's personality based on, for example, detailed settings such as the character's personality, background, and appearance. The generation unit uses, for example, AI to take detailed settings such as the character's personality, background, and appearance as input and output dialogue that reflects the character's personality. As a result, the generation unit can automatically generate dialogue that reflects the character's personality.

[0081] The management department can manage the generated dialogues. For example, the management department can organize the generated dialogues and make corrections or additions as needed. For example, the management department can use AI to manage the generated dialogues as input. For example, the management department can manage the generated dialogues by considering their content, tone, and style. For example, the management department can use AI to manage the generated dialogues as input. This allows the management department to manage the generated dialogues.

[0082] The recommendation system can use machine learning algorithms to recommend works that match the reader's preferences. For example, the recommendation system recommends works that match the reader's preferences based on their past browsing history, survey results, and purchase history. For example, the recommendation system uses machine learning algorithms to identify the reader's preferences and recommend the most suitable works. For example, the recommendation system refers to trends in works that readers have previously given high ratings to recommend new works. For example, the recommendation system incorporates the genres and themes of works that readers have previously viewed into new recommendations. For example, the recommendation system analyzes the reader's past recommendation history to recommend the most suitable works. In this way, the recommendation system can use machine learning algorithms to recommend works that match the reader's preferences.

[0083] The translation unit can perform multilingual translation and summarization. The translation unit considers factors such as the types of languages ​​to be translated, the accuracy of the translation, and the summarization method when performing translation and summarization. For example, the translation unit can use AI to take the source text as input and output the translated text. For example, the translation unit can use AI to summarize the source text and output the summarized text. For example, the translation unit can select the optimal translation method based on the types of languages ​​to be translated. For example, the translation unit can use translation models specialized for specific language pairs to improve translation accuracy. For example, the translation unit can consider factors such as the length of the text and the importance of the information being summarized when determining the summarization method. This enables the translation unit to perform multilingual translation and summarization.

[0084] The generation unit can estimate the user's emotions and adjust the character design style based on those emotions. For example, if the user is relaxed, the generation unit will generate a design with soft colors and many curves. If the user is excited, the generation unit will generate a design with bright colors and many sharp lines. If the user is sad, the generation unit will generate a design with calm colors and a simple design. The generation unit can use AI, for example, to estimate the user's emotions and adjust the character design style based on those emotions. This allows the generation unit to adjust the character design style based on the user's emotions.

[0085] The generation unit can maintain design consistency by referencing the user's past design history when generating character designs. For example, the generation unit can reference the color scheme and style of characters the user has created in the past and reflect them in the new design. For example, the generation unit can incorporate specific design elements (e.g., specific accessories or clothing) that the user has used in the past into the new design. For example, the generation unit can reference the poses and expressions of characters the user has created in the past to ensure consistency in the new design. For example, the generation unit can use AI to take the user's past design history as input and reflect it in the new design. In this way, the generation unit can maintain design consistency by referencing the user's past design history.

[0086] The generation unit can determine the design direction based on the user's current project theme when generating character designs. For example, if the user's theme is fantasy, the generation unit will generate designs incorporating magic and fantastical elements. For example, if the user's theme is cyberpunk, the generation unit will generate futuristic and mechanical designs. For example, if the user's theme is horror, the generation unit will generate designs incorporating dark colors and eerie elements. The generation unit can, for example, use AI as input to determine the design direction based on the user's current project theme. This allows the generation unit to determine the design direction based on the user's current project theme.

[0087] The generation unit can estimate the user's emotions and determine the priority of character designs to generate based on those estimated emotions. For example, if the user is stressed, the generation unit will prioritize generating simple and calming designs. If the user is excited, the generation unit will prioritize generating vibrant and energetic designs. If the user is relaxed, the generation unit will prioritize generating designs that use soft colors and many curves. The generation unit can use AI, for example, to estimate the user's emotions and determine the priority of character designs based on those estimated emotions. This allows the generation unit to determine the priority of character designs to generate based on the user's emotions.

[0088] The generation unit can incorporate region-specific design elements by considering the user's geographical location when generating character designs. For example, if the user is in Japan, the generation unit will incorporate Japanese-style design elements (e.g., kimono and samurai attire). If the user is in America, the generation unit will incorporate cowboy and Western elements. If the user is in Europe, the generation unit will incorporate medieval knight and castle elements. The generation unit can use AI, for example, to take the user's geographical location as input and incorporate region-specific design elements. This allows the generation unit to incorporate region-specific design elements while considering the user's geographical location.

[0089] The generation unit can analyze the user's social media activity and generate designs that reflect trends when creating character designs. For example, the generation unit can incorporate design elements that have received many "likes" from the user on social media into new designs. For example, the generation unit can generate designs by referencing the styles of popular artists that the user follows. For example, the generation unit can analyze the trends of communities that the user participates in and generate designs based on those trends. For example, the generation unit can use AI to take the user's social media activity as input and generate designs that reflect trends. In this way, the generation unit can analyze the user's social media activity and generate designs that reflect trends.

[0090] The suggestion function can estimate the user's emotions and adjust the tone of the story development based on those emotions. For example, if the user is relaxed, the suggestion function will suggest a calm and heartwarming story development. If the user is excited, the suggestion function will suggest a story development that includes elements of action and suspense. If the user is sad, the suggestion function will suggest a moving and tear-jerking story development. The suggestion function can use AI, for example, to estimate the user's emotions and adjust the tone of the story development based on those emotions. This allows the suggestion function to adjust the tone of the story development based on the user's emotions.

[0091] The proposal function can maintain consistency when proposing story developments by referring to past story development history. For example, the proposal function can refer to the themes and tones of stories previously created by the user and reflect them in the new story development. For example, the proposal function can incorporate specific plot points or character development used by the user in the past into the new story development. For example, the proposal function can ensure consistency in the new story development with the settings and worldview of stories previously created by the user. For example, the proposal function can use AI to take past story development history as input and reflect it in the new story development. This allows the proposal function to maintain consistency by referring to past story development history.

[0092] The proposal department can adjust its proposed story developments to take into account the growth and changes of the characters. For example, the proposal department can propose story developments that reflect past events and experiences in order to depict the process of a character's growth. For example, the proposal department can propose reactions and actions to new situations that take into account changes in the character's personality and values. For example, the proposal department can propose story developments that reflect changes in the characters' relationships. For example, the proposal department can use AI as input to adjust the proposed content based on the growth and changes of the characters. This allows the proposal department to adjust its proposed content to take into account the growth and changes of the characters.

[0093] The suggestion unit can estimate the user's emotions and determine the priority of story developments to suggest based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize suggesting relaxing story developments. For example, if the user is excited, the suggestion unit will prioritize suggesting story developments that include elements of action or suspense. For example, if the user is sad, the suggestion unit will prioritize suggesting emotionally moving and tear-jerking story developments. The suggestion unit can use AI, for example, to estimate the user's emotions and determine the priority of story developments based on those emotions. This allows the suggestion unit to determine the priority of story developments to suggest based on the user's emotions.

[0094] The proposal department can incorporate region-specific culture and background information by considering the user's geographical location when proposing story developments. For example, if the user is in Japan, the proposal department will propose a story development that incorporates Japanese culture and scenery. For example, if the user is in the United States, the proposal department will propose a story development that incorporates American culture and scenery. For example, if the user is in Europe, the proposal department will propose a story development that incorporates European culture and scenery. The proposal department can use AI, for example, to take the user's geographical location information as input and incorporate region-specific culture and background information. This allows the proposal department to incorporate region-specific culture and background information by considering the user's geographical location information.

[0095] The proposal team can analyze users' social media activity and propose story developments that reflect current trends when suggesting storylines. For example, the proposal team can incorporate themes and topics that have garnered many "likes" from users on social media into new story developments. For example, the proposal team can suggest story developments based on trends in popular accounts that users follow. For example, the proposal team can analyze trends in communities that users participate in and propose story developments based on those trends. For example, the proposal team can use AI to take users' social media activity as input and propose story developments that reflect current trends. In this way, the proposal team can analyze users' social media activity and propose story developments that reflect current trends.

[0096] The generation unit can estimate the user's emotions and adjust the tone of the dialogue based on those emotions. For example, if the user is relaxed, the generation unit will generate a calm and heartwarming dialogue. For example, if the user is excited, the generation unit will generate an energetic and lively dialogue. For example, if the user is sad, the generation unit will generate a moving and tear-jerking dialogue. The generation unit can use AI, for example, to estimate the user's emotions and adjust the tone of the dialogue based on those emotions. This allows the generation unit to adjust the tone of the dialogue based on the user's emotions.

[0097] The generation unit can maintain consistency by referring to the character's past dialogue history when generating dialogue. For example, the generation unit can incorporate specific phrases or catchphrases that the character has used in the past into new dialogue. For example, the generation unit can generate consistent dialogue based on the character's personality and values. For example, the generation unit can generate dialogue that reflects the character's relationships and past events. For example, the generation unit can use AI to take the character's past dialogue history as input and reflect it in new dialogue. This allows the generation unit to maintain consistency by referring to the character's past dialogue history.

[0098] The generation unit can adjust the content of dialogue according to the story's progress during dialogue generation. For example, the generation unit can generate dialogue that creates tension as the story approaches its climax. For example, in the early stages of the story, the generation unit can generate dialogue that includes character introductions and background explanations. For example, in the later stages of the story, the generation unit can generate dialogue that reflects the characters' growth and changes. For example, the generation unit can use AI to take the story's progress as input and adjust the content of the dialogue. In this way, the generation unit can adjust the content of dialogue according to the story's progress.

[0099] The generation unit can estimate the user's emotions and determine the priority of the dialogues to generate based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize generating relaxing dialogues. For example, if the user is excited, the generation unit will prioritize generating energetic and lively dialogues. For example, if the user is sad, the generation unit will prioritize generating moving and tear-jerking dialogues. The generation unit can use AI, for example, to estimate the user's emotions and determine the priority of the dialogues based on the estimated emotions. This allows the generation unit to determine the priority of the dialogues to generate based on the user's emotions.

[0100] The generation unit can incorporate region-specific expressions and phrases by considering the user's geographical location when generating dialogue. For example, if the user is in Japan, the generation unit will incorporate Japanese expressions and phrases into the dialogue. For example, if the user is in the United States, the generation unit will incorporate American slang and expressions into the dialogue. For example, if the user is in Europe, the generation unit will generate dialogue that reflects European culture and background. The generation unit can, for example, use AI as input to incorporate region-specific expressions and phrases by considering the user's geographical location.

[0101] The generation unit can analyze the user's social media activity and generate dialogues that reflect trends during dialogue generation. For example, the generation unit can incorporate topics or themes that have received many "likes" from the user on social media into new dialogues. For example, the generation unit can generate dialogues by referencing trends from popular accounts that the user follows. For example, the generation unit can analyze trends in communities that the user participates in and generate dialogues based on that. For example, the generation unit can use AI to take the user's social media activity as input and generate dialogues that reflect trends. In this way, the generation unit can analyze the user's social media activity and generate dialogues that reflect trends.

[0102] The management unit can estimate the user's emotions and adjust the interaction management method based on the estimated emotions. For example, if the user is stressed, the management unit provides a simple and intuitive management interface. For example, if the user is relaxed, the management unit provides detailed management options. For example, if the user is in a hurry, the management unit provides shortcuts that allow for quick interaction management. The management unit can use AI, for example, to estimate the user's emotions and adjust the interaction management method based on the estimated emotions. This allows the management unit to adjust the interaction management method based on the user's emotions.

[0103] The management department can maintain consistency during conversational management by referring to past management history. For example, the management department can refer to settings and changes that users have made to conversational management in the past and reflect them in new management. For example, the management department can prioritize displaying specific management options that users have used in the past. For example, the management department can analyze a user's past management history and suggest the optimal management method. For example, the management department can use AI to input past management history and reflect it in new management. This allows the management department to maintain consistency by referring to past management history.

[0104] The management unit can estimate the user's emotions and determine the priority of conversations to manage based on those estimated emotions. For example, if the user is stressed, the management unit will prioritize important conversations. For example, if the user is relaxed, the management unit will provide detailed conversation management options. For example, if the user is in a hurry, the management unit will prioritize displaying conversations that can be managed quickly. The management unit can use AI, for example, to estimate the user's emotions and determine the priority of conversations to manage based on those estimated emotions. This allows the management unit to determine the priority of conversations to manage based on the user's emotions.

[0105] The management unit can select the optimal management method during interaction management by considering the user's device information. For example, if the user is using a smartphone, the management unit provides a management interface that matches the screen size. For example, if the user is using a tablet, the management unit provides a management interface optimized for a larger screen. For example, if the user is using a desktop, the management unit provides detailed management options. For example, the management unit can use AI to input the user's device information and select the optimal management method. This allows the management unit to select the optimal management method by considering the user's device information.

[0106] The recommendation system can estimate the user's emotions and adjust the tone of the recommended works based on those emotions. For example, if the user is relaxed, the recommendation system will recommend calm and heartwarming works. If the user is excited, the recommendation system will recommend works containing elements of action or suspense. If the user is sad, the recommendation system will recommend moving and tear-jerking works. The recommendation system can use AI, for example, to estimate the user's emotions and adjust the tone of the recommended works based on those emotions. This allows the recommendation system to adjust the tone of the recommended works based on the user's emotions.

[0107] The recommendation system can maintain consistency by referring to past recommendation history when making recommendations. For example, the recommendation system can refer to the trends of works that users have previously given high ratings to and reflect them in new recommendations. For example, the recommendation system can incorporate the genres and themes of works that users have previously viewed into new recommendations. For example, the recommendation system can analyze a user's past recommendation history and recommend the most suitable works. For example, the recommendation system can use AI to input past recommendation history and reflect it in new recommendations. This allows the recommendation system to maintain consistency by referring to past recommendation history.

[0108] The recommendation system can estimate the user's emotions and prioritize the works it recommends based on those emotions. For example, if the user is stressed, the recommendation system will prioritize recommending relaxing works. If the user is excited, the recommendation system will prioritize recommending energetic and lively works. If the user is sad, the recommendation system will prioritize recommending moving and tear-jerking works. The recommendation system can use AI, for example, to estimate the user's emotions and prioritize the works it recommends based on those emotions. This allows the recommendation system to prioritize works based on the user's emotions.

[0109] The recommendation system can prioritize recommending region-specific works by considering the user's geographical location. For example, if the user is in Japan, the recommendation system will recommend works that incorporate Japanese culture and scenery. If the user is in the United States, the recommendation system will recommend works that incorporate American culture and scenery. If the user is in Europe, the recommendation system will recommend works that incorporate European culture and scenery. The recommendation system can use AI, for example, to input the user's geographical location and prioritize recommending region-specific works. This allows the recommendation system to prioritize recommending region-specific works by considering the user's geographical location.

[0110] The translation unit can estimate the user's emotions and adjust the tone of the translation based on those emotions. For example, if the user is relaxed, the translation unit will translate in a calm and warm tone. If the user is excited, the translation unit will translate in an energetic and lively tone. If the user is sad, the translation unit will translate in a moving and tear-jerking tone. The translation unit can use AI, for example, to estimate the user's emotions and adjust the tone of the translation based on those emotions. This allows the translation unit to adjust the tone of the translation based on the user's emotions.

[0111] The translation department can maintain consistency by referring to past translation history during the translation process. For example, the department can refer to the style and tone of translations previously made by the user and reflect them in new translations. For example, the department can incorporate specific terminology or expressions previously used by the user into new translations. For example, the department can analyze the user's past translation history and suggest the optimal translation method. For example, the department can use AI to take past translation history as input and reflect it in new translations. This allows the translation department to maintain consistency by referring to past translation history.

[0112] The translation unit can estimate the user's emotions and prioritize the content to translate based on those emotions. For example, if the user is stressed, the translation unit will prioritize translating relaxing content. For example, if the user is excited, the translation unit will prioritize translating energetic and lively content. For example, if the user is sad, the translation unit will prioritize translating touching and tear-jerking content. The translation unit can use AI, for example, to estimate the user's emotions and prioritize the content to translate based on those emotions. This allows the translation unit to prioritize the content to translate based on the user's emotions.

[0113] The translation unit can incorporate region-specific expressions by considering the user's geographical location during translation. For example, if the user is in Japan, the unit will incorporate Japanese expressions and phrases into the translation. For example, if the user is in the United States, the unit will incorporate American slang and expressions into the translation. For example, if the user is in Europe, the unit will incorporate expressions that reflect European culture and background into the translation. The translation unit can, for example, use AI as input to incorporate region-specific expressions based on the user's geographical location. This allows the translation unit to incorporate region-specific expressions while considering the user's geographical location.

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

[0115] The generation unit can maintain design consistency by referencing the user's past design history when generating character designs. For example, the generation unit can reference the color scheme and style of characters the user has created in the past and reflect them in the new design. For example, the generation unit can incorporate specific design elements (e.g., specific accessories or clothing) that the user has used in the past into the new design. For example, the generation unit can reference the poses and expressions of characters the user has created in the past to ensure consistency in the new design. For example, the generation unit can use AI to take the user's past design history as input and reflect it in the new design. In this way, the generation unit can maintain design consistency by referencing the user's past design history.

[0116] The generation unit can determine the design direction based on the user's current project theme when generating character designs. For example, if the user's theme is fantasy, the generation unit will generate designs incorporating magic and fantastical elements. For example, if the user's theme is cyberpunk, the generation unit will generate futuristic and mechanical designs. For example, if the user's theme is horror, the generation unit will generate designs incorporating dark colors and eerie elements. The generation unit can, for example, use AI as input to determine the design direction based on the user's current project theme. This allows the generation unit to determine the design direction based on the user's current project theme.

[0117] The generation unit can estimate the user's emotions and adjust the character design style based on those emotions. For example, if the user is relaxed, the generation unit will generate a design with soft colors and many curves. If the user is excited, the generation unit will generate a design with bright colors and many sharp lines. If the user is sad, the generation unit will generate a design with calm colors and a simple design. The generation unit can use AI, for example, to estimate the user's emotions and adjust the character design style based on those emotions. This allows the generation unit to adjust the character design style based on the user's emotions.

[0118] The generation unit can incorporate region-specific design elements by considering the user's geographical location when generating character designs. For example, if the user is in Japan, the generation unit will incorporate Japanese-style design elements (e.g., kimono and samurai attire). If the user is in America, the generation unit will incorporate cowboy and Western elements. If the user is in Europe, the generation unit will incorporate medieval knight and castle elements. The generation unit can use AI, for example, to take the user's geographical location as input and incorporate region-specific design elements. This allows the generation unit to incorporate region-specific design elements while considering the user's geographical location.

[0119] The generation unit can analyze the user's social media activity and generate designs that reflect trends when creating character designs. For example, the generation unit can incorporate design elements that have received many "likes" from the user on social media into new designs. For example, the generation unit can generate designs by referencing the styles of popular artists that the user follows. For example, the generation unit can analyze the trends of communities that the user participates in and generate designs based on those trends. For example, the generation unit can use AI to take the user's social media activity as input and generate designs that reflect trends. In this way, the generation unit can analyze the user's social media activity and generate designs that reflect trends.

[0120] The suggestion function can estimate the user's emotions and adjust the tone of the story development based on those emotions. For example, if the user is relaxed, the suggestion function will suggest a calm and heartwarming story development. If the user is excited, the suggestion function will suggest a story development that includes elements of action and suspense. If the user is sad, the suggestion function will suggest a moving and tear-jerking story development. The suggestion function can use AI, for example, to estimate the user's emotions and adjust the tone of the story development based on those emotions. This allows the suggestion function to adjust the tone of the story development based on the user's emotions.

[0121] The proposal function can maintain consistency when proposing story developments by referring to past story development history. For example, the proposal function can refer to the themes and tones of stories previously created by the user and reflect them in the new story development. For example, the proposal function can incorporate specific plot points or character development used by the user in the past into the new story development. For example, the proposal function can ensure consistency in the new story development with the settings and worldview of stories previously created by the user. For example, the proposal function can use AI to take past story development history as input and reflect it in the new story development. This allows the proposal function to maintain consistency by referring to past story development history.

[0122] The proposal department can adjust its proposed story developments to take into account the growth and changes of the characters. For example, the proposal department can propose story developments that reflect past events and experiences in order to depict the process of a character's growth. For example, the proposal department can propose reactions and actions to new situations that take into account changes in the character's personality and values. For example, the proposal department can propose story developments that reflect changes in the characters' relationships. For example, the proposal department can use AI as input to adjust the proposed content based on the growth and changes of the characters. This allows the proposal department to adjust its proposed content to take into account the growth and changes of the characters.

[0123] The suggestion unit can estimate the user's emotions and determine the priority of story developments to suggest based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize suggesting relaxing story developments. For example, if the user is excited, the suggestion unit will prioritize suggesting story developments that include elements of action or suspense. For example, if the user is sad, the suggestion unit will prioritize suggesting emotionally moving and tear-jerking story developments. The suggestion unit can use AI, for example, to estimate the user's emotions and determine the priority of story developments based on those emotions. This allows the suggestion unit to determine the priority of story developments to suggest based on the user's emotions.

[0124] The proposal department can incorporate region-specific culture and background information by considering the user's geographical location when proposing story developments. For example, if the user is in Japan, the proposal department will propose a story development that incorporates Japanese culture and scenery. For example, if the user is in the United States, the proposal department will propose a story development that incorporates American culture and scenery. For example, if the user is in Europe, the proposal department will propose a story development that incorporates European culture and scenery. The proposal department can use AI, for example, to take the user's geographical location information as input and incorporate region-specific culture and background information. This allows the proposal department to incorporate region-specific culture and background information by considering the user's geographical location information.

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

[0126] Step 1: The generation unit generates character designs based on the creator's settings. For example, the generation unit generates unique character designs based on detailed settings such as the character's appearance, personality, and background settings set by the creator. The generation unit uses AI to take the detailed character settings set by the creator as input and outputs unique character designs. Step 2: The proposal unit proposes a story development based on the character designs generated by the generation unit. Based on the generated character designs, the proposal unit proposes a story development considering the plot progression, character roles, scene settings, etc. Using natural language processing technology, the proposal unit takes the generated character designs as input and outputs the optimal story development. Step 3: The generation unit generates dialogue based on the story development proposed by the proposal unit. Based on the proposed story development, the generation unit generates dialogue considering the content, tone, and style of conversations between characters. The generation unit uses AI to take the proposed story development as input and outputs dialogue between characters. Step 4: The management unit manages the dialogues generated by the generation unit. The management unit organizes the generated dialogues and makes corrections or additions as needed. The management unit uses AI as input to manage the dialogues.

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

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

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

[0130] Each of the multiple elements described above, including the generation unit, proposal unit, management unit, and recommendation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14 and generates character designs based on detailed character settings set by the creator. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a story development based on the generated character designs. The management unit is implemented by the control unit 46A of the smart device 14 and manages the generated dialogues. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends works based on the reader's preferences. The translation unit is implemented by the control unit 46A of the smart device 14 and performs multilingual translation and summarization. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0146] Each of the multiple elements described above, including the generation unit, proposal unit, management unit, and recommendation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart glasses 214 and generates character designs based on detailed character settings set by the creator. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a story development based on the generated character designs. The management unit is implemented by the control unit 46A of the smart glasses 214 and manages the generated dialogues. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends works based on the reader's preferences. The translation unit is implemented by the control unit 46A of the smart glasses 214 and performs multilingual translation and summarization. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0162] Each of the multiple elements described above, including the generation unit, proposal unit, management unit, and recommendation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314 and generates character designs based on detailed character settings set by the creator. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a story development based on the generated character designs. The management unit is implemented by the control unit 46A of the headset terminal 314 and manages the generated dialogues. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends works based on the reader's preferences. The translation unit is implemented by the control unit 46A of the headset terminal 314 and performs multilingual translation and summarization. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0179] Each of the multiple elements described above, including the generation unit, proposal unit, management unit, and recommendation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the robot 414 and generates a character design based on the detailed character settings set by the creator. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a story development based on the generated character design. The management unit is implemented by the control unit 46A of the robot 414 and manages the generated dialogue. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends works based on the reader's preferences. The translation unit is implemented by the control unit 46A of the robot 414 and performs multilingual translation and summarization. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

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

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

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

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

[0189] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

[0198] (Note 1) A generation unit that generates character designs based on the creator's settings, A proposal unit proposes a story development based on the character designs generated by the generation unit, A generation unit that generates dialogue based on the story development proposed by the aforementioned proposal unit, The system comprises a management unit that manages the dialogue generated by the generation unit. A system characterized by the following features. (Note 2) It has a recommendation section that recommends works based on the reader's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a translation department that handles multilingual translation and summarization. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate unique character designs based on detailed settings. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Using natural language processing technology, we propose the optimal story development from an idea. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Automatically generates dialogue that reflects the character's personality. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned management department, Manage the generated dialogue The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned recommendation department, We use machine learning algorithms to recommend works that match the reader's preferences. The system described in Appendix 2, characterized by the features described herein. (Note 9) The aforementioned translation department, Perform multilingual translation and summarization. The system described in Appendix 3, characterized by the features described herein. (Note 10) The generating unit is It estimates the user's emotions and adjusts the character design style based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating character designs, the system maintains design consistency by referencing the user's past design history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating character designs, the design direction is determined based on the user's current project theme. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and determines the priority of character designs to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating character designs, the user's geographical location information is taken into consideration, and region-specific design elements are incorporated. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating character designs, the system analyzes users' social media activity and generates designs that reflect current trends. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the tone of the story based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When proposing story developments, refer to past story development history to maintain consistency. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When proposing a story development, adjust the content of the proposal to take into account the growth and changes of the characters. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggested story developments based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When proposing story developments, consider the user's geographical location and incorporate regionally specific cultures and backgrounds. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When proposing story developments, we analyze users' social media activity and propose developments that reflect current trends. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the tone of the conversation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating dialogue, refer to the character's past dialogue history to maintain consistency. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating dialogue, adjust the content of the dialogue according to the progress of the story. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is It estimates the user's emotions and determines the priority of the conversations to generate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is When generating dialogue, the system takes the user's geographical location into consideration and incorporates region-specific phrasing and expressions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is During dialogue generation, the system analyzes the user's social media activity and generates dialogue that reflects current trends. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, It estimates the user's emotions and adjusts the interaction management method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, When managing interactions, refer to past management history to maintain consistency. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned management department, It estimates the user's emotions and determines the priority of interactions to manage based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned management department, When managing interactions, the optimal management method is selected by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned recommendation department, It estimates the user's emotions and adjusts the tone of recommended works based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned recommendation department, When making recommendations, refer to past recommendation history to maintain consistency. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned recommendation department, It estimates the user's emotions and determines the priority of recommended works based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned recommendation department, When making recommendations, the system prioritizes recommending region-specific works by taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned translation department, It estimates the user's emotions and adjusts the tone of the translation based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned translation department, During translation, refer to past translation history to maintain consistency. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned translation department, It estimates the user's emotions and determines the priority of the content to translate based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned translation department, During translation, the system takes the user's geographical location into consideration and incorporates region-specific expressions. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. A generation unit that generates character designs based on the creator's settings, A proposal unit proposes a story development based on the character designs generated by the generation unit, A generation unit that generates dialogue based on the story development proposed by the aforementioned proposal unit, The system comprises a management unit that manages the dialogue generated by the generation unit. A system characterized by the following features.

2. It has a recommendation section that recommends works based on the reader's preferences. The system according to feature 1.

3. It has a translation department that handles multilingual translation and summarization. The system according to feature 1.

4. The generating unit is Generate unique character designs based on detailed settings. The system according to feature 1.

5. The aforementioned proposal section is, Using natural language processing technology, we propose the optimal story development from an idea. The system according to feature 1.

6. The generating unit is Automatically generates dialogue that reflects the character's personality. The system according to feature 1.

7. The aforementioned management department, Manage the generated dialogue The system according to feature 1.

8. The aforementioned recommendation department, We use machine learning algorithms to recommend works that match the reader's preferences. The system according to feature 2.

9. The aforementioned translation department, Perform multilingual translation and summarization. The system according to claim 3.

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