system

Abstract

The system according to the embodiment aims to easily create a picture book that reflects the user's intentions. [Solution] A system according to an embodiment includes a QA unit, a character creation unit, a plot setting unit, and a generation unit. The QA unit understands a user's intention. The character creation unit creates a character based on the intention understood by the QA unit. The plot setting unit sets a plot based on the character created by the character creation unit. The generation unit generates a story and images based on the plot set by the plot setting unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult to easily create a picture book that reflects the user's intentions.

[0005] The system according to the embodiment aims to easily create a picture book that reflects the user's intentions. [Means for solving the problem]

[0006] The system according to the embodiment includes a QA unit, a character creation unit, a plot setting unit, and a generation unit. The QA unit understands the user's intention. The character creation unit creates a character based on the intention understood by the QA unit. The plot setting unit sets a plot based on the character created by the character creation unit. The generation unit generates a story and images based on the plot set by the plot setting unit. [Effects of the Invention]

[0007] The system according to the embodiment makes it possible to easily create a picture book that reflects the user's intentions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A picture book creation system according to an embodiment of the present invention utilizes AI technology to enable users to easily create their own picture book. In this system, users input their message, and AI understands the user's intent through a series of questions and answers (Q&A) and creates characters that embody that message. Furthermore, the AI ​​sets the plot and generates the story and images. This allows users to easily create their own original picture book. For example, a user inputs a message such as "I want to convey the importance of friendship." This information is input into the AI. Next, the AI ​​understands the user's intent through a series of Q&A. The AI ​​asks the user specific questions and digs deeper into the user's intent based on the user's answers. For example, it asks questions such as "What kind of characters will appear?" and "What kind of situations will depict friendship?" After understanding the user's intent, the AI ​​creates characters that embody that message. For example, for a picture book themed around friendship, it creates characters that represent friends. These characters are designed based on the user's intent. Next, the AI ​​sets the plot. The plot serves as the framework for the story and is set based on the user's intent. For example, a picture book with the theme of friendship would have a story in which friends overcome difficulties to deepen their friendship. Finally, AI generates the story and images. The story is written in detail based on the plot, and images are generated to match the story. This allows users to easily create their own original picture book. This system allows users to easily create picture books that reflect their own messages. For example, if a parent wants to convey certain values ​​to their child, they can embody that message in a picture book. It can also be used in education, where teachers can create picture books to teach students a specific theme. This makes it easy for the picture book creation system to create original picture books that reflect the user's intentions.

[0029] A picture book creation system according to an embodiment includes a QA unit, a character creation unit, a plot setting unit, and a generation unit. The QA unit understands a user's intention. For example, when a user inputs "what they want to communicate," the QA unit asks specific questions to dig deeper into the user's intention. For example, the QA unit can ask questions such as, "What kind of characters will appear?" or "What kind of situation will the friendship be depicted in?" The character creation unit creates characters based on the intention understood by the QA unit. For example, for a picture book with a friendship theme, the character creation unit creates characters of friends. These characters are designed based on the user's intention. The plot setting unit sets a plot based on the characters created by the character creation unit. For example, for a picture book with a friendship theme, the plot setting unit sets a story in which friends overcome difficulties and deepen their friendship. The generation unit generates a story and images based on the plot set by the plot setting unit. For example, the generation unit describes the story in detail based on the plot and generates images to match the story. As a result, the picture book creation system according to the embodiment can easily create an original picture book that reflects the user's intentions. Some or all of the above-described processes in the QA unit, character creation unit, plot setting unit, and generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the QA unit can use AI to generate questions and analyze answers in order to understand the user's intentions. The character creation unit can use AI to generate character designs. The plot setting unit can use AI to set the plot. The generation unit can use AI to generate stories and images. As a result, the picture book creation system can easily create an original picture book that reflects the user's intentions.

[0030] The picture book creation system includes a questioning unit that asks specific questions to dig deeper into the user's intention. The questioning unit is a department that asks specific questions to dig deeper into the user's intention. For example, when the user inputs "what they want to communicate," the questioning unit asks specific questions to dig deeper into the user's intention. For example, the questioning unit can ask questions such as "What kind of characters appear?" or "What kind of situations depict friendship?" This allows for a deeper understanding of the user's intention. Some or all of the above-described processing in the questioning unit may be performed using, for example, AI, or may be performed without using AI. For example, the questioning unit can generate questions and analyze answers using AI. This allows for a deeper understanding of the user's intention.

[0031] The picture book creation system includes a customization unit that customizes the appearance and personality of a character. The customization unit is a department that customizes the appearance and personality of a character. The customization unit customizes the appearance and personality of a character based on, for example, a user's intention. For example, the customization unit can customize the character's hairstyle, clothing, personality traits, etc. This makes it possible to create a character that matches the user's intention. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can customize the appearance and personality of a character using AI. This makes it possible for the customization unit to create a character that matches the user's intention.

[0032] The picture book creation system includes a scenario designation unit that designates the outline of the plot. The scenario designation unit is a unit that designates the outline of the plot. The scenario designation unit designates the outline of the plot based on, for example, the user's intention. For example, the scenario designation unit can designate the main storyline and important events. This allows the plot to be set based on the user's intention. Some or all of the above-described processing in the scenario designation unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario designation unit can designate the outline of the plot using AI. This allows the scenario designation unit to set the plot based on the user's intention.

[0033] The picture book creation system includes a quality control unit that manages the quality of the generated story and images. The quality control unit is a department that manages the quality of the generated story and images. The quality control unit, for example, manages the quality of the generated story and images. For example, the quality control unit can manage the accuracy of the text and the resolution of the images. This can improve the quality of the generated picture book. Some or all of the above-mentioned processing in the quality control unit may be performed using AI, for example, or may be performed without using AI. For example, the quality control unit can manage the quality of the story and images using AI. This can improve the quality of the generated picture book.

[0034] The QA unit can analyze past user response data and generate appropriate question patterns. For example, the QA unit can analyze past user response data and generate appropriate question patterns. For example, the QA unit can generate effective question patterns based on response data from users with similar intentions in the past. The QA unit can also analyze user response tendencies and determine the most appropriate question order. The QA unit can also select question formats that are easy for users to answer from past data. This allows effective questions to be asked by utilizing past data. Some or all of the above-described processing in the QA unit may be performed using, for example, AI, or may be performed without using AI. For example, the QA unit can analyze past user response data using AI and generate appropriate question patterns. This allows the QA unit to ask effective questions by utilizing past data.

[0035] The QA unit can analyze the user's response speed and reaction in real time when asking a question and adjust the difficulty of the question. For example, when asking a question, the QA unit can analyze the user's response speed and reaction in real time and adjust the difficulty of the question. For example, if the user is taking a long time to answer, the QA unit can lower the difficulty of the question. Furthermore, if the user is answering quickly, the QA unit can also increase the difficulty of the question. Furthermore, if the user's reaction is slow, the QA unit can change the format of the question and ask it again. In this way, by adjusting the difficulty of the question according to the user's reaction, more appropriate questions can be asked. Some or all of the above-mentioned processing in the QA unit may be performed using, for example, AI, or may be performed without using AI. For example, the QA unit can analyze the user's response speed and reaction in real time using AI and adjust the difficulty of the question. In this way, by adjusting the difficulty of the question according to the user's reaction, the QA unit can ask more appropriate questions.

[0036] The QA unit can customize the content of questions by taking into account the user's geographical and cultural backgrounds when asking questions. For example, the QA unit can customize the content of questions by taking into account the user's geographical and cultural backgrounds when asking questions. For example, the QA unit can ask questions using examples specific to a region based on the user's geographical background. The QA unit can also ask questions using appropriate expressions by taking into account the user's cultural background. The QA unit can also ask questions tailored to the user's language and habits. This allows for a more appropriate understanding of the user's intentions by asking questions tailored to the user's background. Some or all of the above-described processing in the QA unit can be performed using, for example, AI, or without AI. For example, the QA unit can customize the content of questions by taking into account the user's geographical and cultural backgrounds using AI. This allows for a more appropriate understanding of the user's intentions by asking questions tailored to the user's background.

[0037] When asking a question, the QA unit refers to the user's past picture book creation history and prioritizes asking related questions. For example, when asking a question, the QA unit refers to the user's past picture book creation history and prioritizes asking related questions. For example, the QA unit asks related questions based on the theme of picture books the user has created in the past. The QA unit can also ask questions about characters based on the user's past character preferences. The QA unit can also ask questions about plots based on the user's past plot settings. This allows related questions to be prioritized by referring to the past history. Some or all of the above-described processing in the QA unit may be performed using, for example, AI, or may be performed without using AI. For example, the QA unit uses AI to refer to the user's past picture book creation history and prioritizes asking related questions. This allows the QA unit to refer to the past history and prioritize asking related questions.

[0038] The character creation unit can suggest an appropriate character by referring to the user's past character preference data when creating a character. For example, the character creation unit can suggest an appropriate character by referring to the user's past character preference data when creating a character. For example, the character creation unit can suggest an optimal character based on the characteristics of characters previously selected by the user. The character creation unit can also suggest a similar character from the user's past preference data. The character creation unit can also analyze the user's past preference data and suggest a new character. In this way, the optimal character can be suggested by referring to the past preference data. Some or all of the above-described processing in the character creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the character creation unit can suggest an appropriate character by referring to the user's past character preference data using AI. In this way, the character creation unit can suggest an optimal character by referring to the past preference data.

[0039] The character creation unit can automatically generate a character's background story based on user input when creating a character. The character creation unit automatically generates a character's background story based on user input, for example, when creating a character. For example, the character creation unit generates a character's background story based on a theme input by the user. The character creation unit can also generate a character's background story based on a situation input by the user. The character creation unit can also generate a background story based on character characteristics input by the user. By generating a background story based on the user's input, a more consistent character can be provided. Some or all of the above-described processing in the character creation unit may be performed using AI, for example, or may be performed without using AI. For example, the character creation unit can automatically generate a character's background story based on the user's input using AI. By generating a background story based on the user's input, the character creation unit can provide a more consistent character.

[0040] The character creation unit can customize the character's appearance by taking into account the user's geographical and cultural background when creating a character. For example, the character creation unit customizes the character's appearance by taking into account the user's geographical and cultural background when creating a character. For example, the character creation unit creates a character wearing clothing unique to the region based on the user's geographical background. The character creation unit can also create a character with an appropriate appearance by taking into account the user's cultural background. The character creation unit can also create a character that matches the user's language and customs. This makes it possible to provide a more appropriate character by creating a character with an appearance that matches the user's background. Some or all of the above-described processing in the character creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the character creation unit can customize the character's appearance by taking into account the user's geographical and cultural background using AI. This makes it possible to provide a more appropriate character by creating a character with an appearance that matches the user's background.

[0041] The plot setting unit can generate an appropriate plot by referring to plot data that has been previously successful when setting a plot. For example, the plot setting unit generates an appropriate plot by referring to plot data that has been previously successful when setting a plot. For example, the plot setting unit generates an optimal plot based on plot data that has been previously successful. The plot setting unit can also generate a plot by referring to plot data that has been successfully executed in accordance with a user's intention. The plot setting unit can also analyze the past successful data and generate a plot that is optimal for the user's intention. In this way, an optimal plot can be generated by referring to the past successful data. Some or all of the above-described processing in the plot setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the plot setting unit can generate an appropriate plot by referring to plot data that has been previously successful using AI. In this way, the plot setting unit can generate an optimal plot by referring to the past successful data.

[0042] The plot setting unit can automatically generate a detailed scenario of the plot based on user input during plot setting. For example, the plot setting unit automatically generates a detailed scenario of the plot based on user input during plot setting. For example, the plot setting unit generates a detailed scenario based on a theme input by the user. The plot setting unit can also generate a detailed scenario based on a situation input by the user. The plot setting unit can also generate a detailed scenario based on character characteristics input by the user. By generating a detailed scenario based on user input, a more consistent story can be provided. Some or all of the above-described processing in the plot setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the plot setting unit can automatically generate a detailed scenario of the plot based on user input using AI. By generating a detailed scenario based on user input, a more consistent story can be provided.

[0043] The plot setting unit can customize the content of the plot by taking into account the geographical and cultural background of the user when setting the plot. For example, the plot setting unit customizes the content of the plot by taking into account the geographical and cultural background of the user when setting the plot. For example, the plot setting unit sets a plot that includes a situation specific to a region based on the geographical background of the user. The plot setting unit can also set a plot with appropriate content by taking into account the cultural background of the user. The plot setting unit can also set a plot that matches the language and habits of the user. This allows for a more appropriate story to be provided by setting a plot that matches the user's background. Some or all of the above-described processing in the plot setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the plot setting unit can customize the content of the plot by taking into account the geographical and cultural background of the user using AI. This allows for a more appropriate story to be provided by setting a plot that matches the user's background.

[0044] The plot setting unit can analyze the user's social media activity and suggest related plots when setting a plot. For example, the plot setting unit can analyze the user's social media activity and suggest related plots when setting a plot. For example, the plot setting unit can suggest an optimal plot based on a theme frequently mentioned by the user on social media. The plot setting unit can also suggest similar plots based on the user's social media activity. The plot setting unit can also analyze the user's social media activity and suggest a new plot. In this way, related plots can be suggested by analyzing the user's social media activity. Some or all of the above-described processing in the plot setting unit can be performed using AI, for example, or without AI. For example, the plot setting unit can analyze the user's social media activity using AI and suggest related plots. In this way, the plot setting unit can suggest related plots by analyzing the user's social media activity.

[0045] The generation unit can generate appropriate stories and images by referring to past generation data at the time of generation. For example, the generation unit generates appropriate stories and images by referring to past generation data at the time of generation. For example, the generation unit generates optimal stories and images based on data of stories and images that were successful in the past. The generation unit can also generate by referring to data of successful stories and images that match the user's intention. The generation unit can also analyze past generation data and generate stories and images that are optimal for the user's intention. In this way, optimal stories and images can be generated by referring to past data. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can use AI to refer to past generation data and generate appropriate stories and images. In this way, the generation unit can generate optimal stories and images by referring to past data.

[0046] The generation unit can automatically generate a detailed story scenario and image layout based on user input during generation. The generation unit, for example, automatically generates a detailed story scenario and image layout based on user input during generation. For example, the generation unit generates a detailed story scenario and image layout based on a theme input by the user. The generation unit can also generate a detailed story scenario and image layout based on a situation input by the user. The generation unit can also generate a detailed story scenario and image layout based on character characteristics input by the user. This allows for a more consistent picture book by generating a detailed scenario and image layout based on user input. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can automatically generate a detailed story scenario and image layout based on user input using AI. This allows for a more consistent picture book by generating a detailed story scenario and image layout based on user input.

[0047] The generation unit can customize the content of the story and images during generation, taking into account the user's geographical and cultural background. For example, the generation unit customizes the content of the story and images during generation, taking into account the user's geographical and cultural background. For example, the generation unit generates stories and images including regional situations based on the user's geographical background. The generation unit can also generate stories and images with appropriate content taking into account the user's cultural background. The generation unit can also generate stories and images tailored to the user's language and habits. This allows for the generation of stories and images tailored to the user's background, thereby providing a more appropriate picture book. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can customize the content of the story and images using AI, taking into account the user's geographical and cultural background. This allows for the generation unit to generate stories and images tailored to the user's background, thereby providing a more appropriate picture book.

[0048] The generation unit can analyze the user's social media activity and suggest related stories and images at the time of generation. For example, the generation unit can analyze the user's social media activity and suggest related stories and images at the time of generation. For example, the generation unit can suggest optimal stories and images based on themes frequently mentioned by the user on social media. The generation unit can also suggest similar stories and images from the user's social media activity. The generation unit can also analyze the user's social media activity and suggest new stories and images. In this way, related stories and images can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can analyze the user's social media activity using AI and suggest related stories and images. In this way, the generation unit can suggest related stories and images by analyzing the user's social media activity.

[0049] The questioning unit can analyze past user response data and generate an appropriate question pattern when asking a question. For example, the questioning unit can analyze past user response data and generate an appropriate question pattern when asking a question. For example, the questioning unit generates an effective question pattern based on response data of users with similar intentions in the past. The questioning unit can also analyze users' response tendencies and determine the most appropriate question order. The questioning unit can also select a question format that is easy for the user to answer from past data. This makes it possible to ask effective questions by utilizing past data. Some or all of the above-described processing in the questioning unit may be performed using, for example, AI, or may be performed without using AI. For example, the questioning unit can analyze past user response data using AI and generate an appropriate question pattern. This makes it possible to ask effective questions by utilizing past data.

[0050] The questioning unit can customize the content of the question by taking into account the geographical and cultural background of the user when asking a question. For example, the questioning unit customizes the content of the question by taking into account the geographical and cultural background of the user when asking a question. For example, the questioning unit asks questions using examples specific to a region based on the geographical background of the user. The questioning unit can also ask questions using appropriate expressions by taking into account the cultural background of the user. The questioning unit can also ask questions that are tailored to the language and habits of the user. This makes it possible to understand the user's intention more appropriately by asking questions that are tailored to the user's background. Some or all of the above-described processing in the questioning unit may be performed using, for example, AI, or may be performed without using AI. For example, the questioning unit can customize the content of the question by taking into account the geographical and cultural background of the user using AI. This makes it possible to understand the user's intention more appropriately by asking questions that are tailored to the user's background.

[0051] The customization unit can suggest appropriate customization by referring to the user's past character preference data during customization. For example, the customization unit can suggest appropriate customization by referring to the user's past character preference data during customization. For example, the customization unit can suggest optimal customization based on the characteristics of characters previously selected by the user. The customization unit can also suggest similar characters from the user's past preference data. The customization unit can also analyze the user's past preference data and suggest new characters. In this way, optimal customization can be suggested by referring to the past preference data. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can suggest appropriate customization by referring to the user's past character preference data using AI. In this way, the customization unit can suggest optimal customization by referring to the past preference data.

[0052] The customization unit can customize the character's appearance by taking into account the user's geographical background and cultural background during customization. For example, the customization unit customizes the character's appearance by taking into account the user's geographical background and cultural background during customization. For example, the customization unit creates a character wearing clothing unique to the region based on the user's geographical background. The customization unit can also create a character with an appropriate appearance by taking into account the user's cultural background. The customization unit can also create a character that matches the user's language and customs. This makes it possible to provide a more appropriate character by creating a character with an appearance that matches the user's background. Some or all of the above-described processing by the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can customize the character's appearance by taking into account the user's geographical background and cultural background using AI. This makes it possible to provide a more appropriate character by creating a character with an appearance that matches the user's background.

[0053] The scenario designation unit can generate an appropriate scenario by referencing past successful scenario data when designating a scenario. For example, the scenario designation unit generates an appropriate scenario by referencing past successful scenario data when designating a scenario. For example, the scenario designation unit generates an optimal scenario based on past successful scenario data. The scenario designation unit can also generate a scenario by referencing successful scenario data that matches the user's intention. The scenario designation unit can also analyze past success data and generate a scenario that best matches the user's intention. In this way, an optimal scenario can be generated by referencing past success data. Some or all of the above-described processing in the scenario designation unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario designation unit can use AI to refer to past successful scenario data to generate an appropriate scenario. In this way, the scenario designation unit can generate an optimal scenario by referencing past success data.

[0054] The scenario designation unit can customize the content of the scenario by taking into account the user's geographical and cultural background when designating a scenario. For example, the scenario designation unit customizes the content of the scenario by taking into account the user's geographical and cultural background when designating a scenario. For example, the scenario designation unit sets a scenario including a situation specific to a region based on the user's geographical background. The scenario designation unit can also set a scenario with appropriate content by taking into account the user's cultural background. The scenario designation unit can also set a scenario that matches the user's language and habits. This makes it possible to provide a more appropriate story by setting a scenario that matches the user's background. Some or all of the above-described processing in the scenario designation unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario designation unit can customize the content of the scenario by taking into account the user's geographical and cultural background using AI. This makes it possible to provide a more appropriate story by setting a scenario that matches the user's background.

[0055] The quality control unit can apply an appropriate quality control method by referring to past quality data during quality control. For example, the quality control unit can apply an appropriate quality control method by referring to past quality data during quality control. For example, the quality control unit can apply an optimal quality control method based on quality control data that was successful in the past. The quality control unit can also perform quality control by referring to successful quality control data that matches the user's intention. The quality control unit can also analyze past quality data and apply a quality control method that is optimal for the user's intention. In this way, the optimal quality control method can be applied by referring to past data. Some or all of the above-mentioned processing in the quality control unit may be performed using, for example, AI, or may be performed without using AI. For example, the quality control unit can use AI to refer to past quality data and apply an appropriate quality control method. In this way, the quality control unit can apply an optimal quality control method by referring to past data.

[0056] The quality control unit can customize the content of quality control during quality control, taking into account the geographical and cultural background of the user. For example, the quality control unit customizes the content of quality control during quality control, taking into account the geographical and cultural background of the user. For example, the quality control unit performs quality control using region-specific standards based on the geographical background of the user. The quality control unit can also perform quality control using appropriate standards taking into account the cultural background of the user. The quality control unit can also perform quality control using standards tailored to the language and customs of the user. This allows for more appropriate picture books to be provided by performing quality control using standards tailored to the user's background. Some or all of the above-described processing in the quality control unit may be performed using, for example, AI, or may be performed without using AI. For example, the quality control unit can customize the content of quality control using AI, taking into account the geographical and cultural background of the user. This allows for more appropriate picture books to be provided by performing quality control using standards tailored to the user's background.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The picture book creation system not only reflects the user's intentions, but also refers to the user's past picture book creation history to ask more personalized questions. For example, it can prioritize questions related to the themes and character preferences of picture books created by the user in the past. It can also suggest the most suitable characters based on the characteristics of characters selected by the user in the past. It can also ask questions related to the plot based on past plot settings. This makes it possible to ask more effective questions by utilizing the user's past history.

[0059] The picture book creation system can customize the content of questions taking into account the geographical and cultural background of the user. For example, questions can be posed using examples specific to the region based on the user's geographical background. Questions can also be posed using appropriate expressions taking into account the user's cultural background. Furthermore, questions can be posed that are tailored to the user's language and customs. This allows for a more appropriate understanding of the user's intentions by asking questions that are tailored to the user's background.

[0060] When setting a plot, the picture book creation system can automatically generate a detailed scenario for the plot based on the user's input. For example, a detailed scenario can be generated based on a theme input by the user. A detailed scenario can also be generated based on a situation input by the user. A detailed scenario can also be generated based on the characteristics of characters input by the user. In this way, a more consistent story can be provided by generating a detailed scenario based on the user's input.

[0061] The picture book creation system includes a quality control unit that manages the quality of the generated story and images. The quality control unit manages, for example, the quality of the generated story and images. For example, the quality control unit can manage the accuracy of the text and the resolution of the images. This can improve the quality of the generated picture book. Some or all of the above-described processing in the quality control unit may be performed using, for example, AI, or may be performed without using AI. For example, the quality control unit can manage the quality of the story and images using AI. This can improve the quality of the generated picture book.

[0062] The picture book creation system can customize the content of stories and images by taking into account the user's geographical and cultural backgrounds when generating them. For example, it can generate stories and images that include situations specific to a region based on the user's geographical background. It can also generate stories and images with appropriate content by taking into account the user's cultural background. It can also generate stories and images that are tailored to the user's language and customs. This makes it possible to provide a more appropriate picture book by generating stories and images that are tailored to the user's background.

[0063] The picture book creation system can generate appropriate stories and images by referencing past generation data. For example, the generation unit can generate optimal stories and images based on data on stories and images that have been successful in the past. It can also generate by referencing data on successful stories and images that match the user's intentions. Furthermore, it can analyze past generation data and generate stories and images that are optimal for the user's intentions. In this way, it is possible to generate optimal stories and images by referencing past data.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The QA department is responsible for understanding the user's intent. When the user enters what they want to communicate, the QA department asks specific questions to dig deeper into the user's intent. For example, they can ask questions such as, "What kind of characters will appear?" or "What kind of situations will depict friendship?" Step 2: The character creation department creates characters based on the intent understood by the QA department. For example, if it is a picture book with a friendship theme, characters of friends will be created. These characters are designed based on the user's intent. Step 3: The plot setting department sets the plot based on the characters created by the character creation department. For example, if it is a picture book with the theme of friendship, the story will be set up in which friends overcome difficulties and deepen their friendship. Step 4: The generation section generates a story and images based on the plot set by the plot setting section. The generation section draws a detailed story based on the plot and generates images to match the story.

[0066] (Example 2) A picture book creation system according to an embodiment of the present invention utilizes AI technology to enable users to easily create their own picture book. In this system, users input their message, and AI understands the user's intent through a series of questions and answers (Q&A) and creates characters that embody that message. Furthermore, the AI ​​sets the plot and generates the story and images. This allows users to easily create their own original picture book. For example, a user inputs a message such as "I want to convey the importance of friendship." This information is input into the AI. Next, the AI ​​understands the user's intent through a series of Q&A. The AI ​​asks the user specific questions and digs deeper into the user's intent based on the user's answers. For example, it asks questions such as "What kind of characters will appear?" and "What kind of situations will depict friendship?" After understanding the user's intent, the AI ​​creates characters that embody that message. For example, for a picture book themed around friendship, it creates characters that represent friends. These characters are designed based on the user's intent. Next, the AI ​​sets the plot. The plot serves as the framework for the story and is set based on the user's intent. For example, a picture book with the theme of friendship would have a story in which friends overcome difficulties to deepen their friendship. Finally, AI generates the story and images. The story is written in detail based on the plot, and images are generated to match the story. This allows users to easily create their own original picture book. This system allows users to easily create picture books that reflect their own messages. For example, if a parent wants to convey certain values ​​to their child, they can embody that message in a picture book. It can also be used in education, where teachers can create picture books to teach students a specific theme. This makes it easy for the picture book creation system to create original picture books that reflect the user's intentions.

[0067] A picture book creation system according to an embodiment includes a QA unit, a character creation unit, a plot setting unit, and a generation unit. The QA unit understands a user's intention. For example, when a user inputs "what they want to communicate," the QA unit asks specific questions to dig deeper into the user's intention. For example, the QA unit can ask questions such as, "What kind of characters will appear?" or "What kind of situation will the friendship be depicted in?" The character creation unit creates characters based on the intention understood by the QA unit. For example, for a picture book with a friendship theme, the character creation unit creates characters of friends. These characters are designed based on the user's intention. The plot setting unit sets a plot based on the characters created by the character creation unit. For example, for a picture book with a friendship theme, the plot setting unit sets a story in which friends overcome difficulties and deepen their friendship. The generation unit generates a story and images based on the plot set by the plot setting unit. For example, the generation unit describes the story in detail based on the plot and generates images to match the story. As a result, the picture book creation system according to the embodiment can easily create an original picture book that reflects the user's intentions. Some or all of the above-described processes in the QA unit, character creation unit, plot setting unit, and generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the QA unit can use AI to generate questions and analyze answers in order to understand the user's intentions. The character creation unit can use AI to generate character designs. The plot setting unit can use AI to set the plot. The generation unit can use AI to generate stories and images. As a result, the picture book creation system can easily create an original picture book that reflects the user's intentions.

[0068] The picture book creation system includes a questioning unit that asks specific questions to dig deeper into the user's intention. The questioning unit is a department that asks specific questions to dig deeper into the user's intention. For example, when the user inputs "what they want to communicate," the questioning unit asks specific questions to dig deeper into the user's intention. For example, the questioning unit can ask questions such as "What kind of characters appear?" or "What kind of situations depict friendship?" This allows for a deeper understanding of the user's intention. Some or all of the above-described processing in the questioning unit may be performed using, for example, AI, or may be performed without using AI. For example, the questioning unit can generate questions and analyze answers using AI. This allows for a deeper understanding of the user's intention.

[0069] The picture book creation system includes a customization unit that customizes the appearance and personality of a character. The customization unit is a department that customizes the appearance and personality of a character. The customization unit customizes the appearance and personality of a character based on, for example, a user's intention. For example, the customization unit can customize the character's hairstyle, clothing, personality traits, etc. This makes it possible to create a character that matches the user's intention. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can customize the appearance and personality of a character using AI. This makes it possible for the customization unit to create a character that matches the user's intention.

[0070] The picture book creation system includes a scenario designation unit that designates the outline of the plot. The scenario designation unit is a unit that designates the outline of the plot. The scenario designation unit designates the outline of the plot based on, for example, the user's intention. For example, the scenario designation unit can designate the main storyline and important events. This allows the plot to be set based on the user's intention. Some or all of the above-described processing in the scenario designation unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario designation unit can designate the outline of the plot using AI. This allows the scenario designation unit to set the plot based on the user's intention.

[0071] The picture book creation system includes a quality control unit that manages the quality of the generated story and images. The quality control unit is a department that manages the quality of the generated story and images. The quality control unit, for example, manages the quality of the generated story and images. For example, the quality control unit can manage the accuracy of the text and the resolution of the images. This can improve the quality of the generated picture book. Some or all of the above-mentioned processing in the quality control unit may be performed using AI, for example, or may be performed without using AI. For example, the quality control unit can manage the quality of the story and images using AI. This can improve the quality of the generated picture book.

[0072] The QA unit can estimate the user's emotions and adjust the content and order of questions based on the estimated user emotions. For example, the QA unit can estimate the user's emotions and adjust the content and order of questions based on the estimated user emotions. For example, if the user is nervous, the QA unit can start with simple questions to relax the user. Also, if the user is excited, the QA unit can ask specific questions early on to quickly understand the user's intentions. Also, if the user is tired, the QA unit can reduce the number of questions and focus on important questions. This allows for more appropriate understanding of the user's intentions by asking questions that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the QA unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the QA unit can estimate the user's emotions using an AI and adjust the content and order of questions. This allows the QA department to ask questions that reflect the user's emotions and more accurately understand their intentions.

[0073] The QA unit can analyze past user response data and generate appropriate question patterns. For example, the QA unit can analyze past user response data and generate appropriate question patterns. For example, the QA unit can generate effective question patterns based on response data from users with similar intentions in the past. The QA unit can also analyze user response tendencies and determine the most appropriate question order. The QA unit can also select question formats that are easy for users to answer from past data. This allows effective questions to be asked by utilizing past data. Some or all of the above-described processing in the QA unit may be performed using, for example, AI, or may be performed without using AI. For example, the QA unit can analyze past user response data using AI and generate appropriate question patterns. This allows the QA unit to ask effective questions by utilizing past data.

[0074] The QA unit can analyze the user's response speed and reaction in real time when asking a question and adjust the difficulty of the question. For example, when asking a question, the QA unit can analyze the user's response speed and reaction in real time and adjust the difficulty of the question. For example, if the user is taking a long time to respond, the QA unit can lower the difficulty of the question. Furthermore, if the user is responding quickly, the QA unit can increase the difficulty of the question. Furthermore, if the user's reaction is slow, the QA unit can change the format of the question and ask it again. In this way, by adjusting the difficulty of the question according to the user's reaction, more appropriate questions can be asked. Some or all of the above-mentioned processing in the QA unit may be performed using, for example, AI, or may be performed without using AI. For example, the QA unit can analyze the user's response speed and reaction in real time using AI and adjust the difficulty of the question. In this way, by adjusting the difficulty of the question according to the user's reaction, more appropriate questions can be asked.

[0075] The QA unit can estimate the user's emotions and change the tone and wording of questions based on the estimated user emotions. For example, the QA unit can estimate the user's emotions and change the tone and wording of questions based on the estimated user emotions. For example, if the user is relaxed, the QA unit can ask questions in a friendly tone. If the user is nervous, the QA unit can ask questions in a calm tone. If the user is excited, the QA unit can ask questions in an energetic tone. This allows questions to be asked in a tone and wording that matches the user's emotions, making it possible to more appropriately understand the user's intentions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the QA unit may be performed using AI, or may be performed without AI. For example, the QA unit can estimate the user's emotions using AI and change the tone and wording of questions. This allows the QA department to ask questions in a tone and with expressions that reflect the user's emotions, allowing them to better understand their intentions.

[0076] The QA unit can customize the content of questions by taking into account the user's geographical and cultural backgrounds when asking questions. For example, the QA unit can customize the content of questions by taking into account the user's geographical and cultural backgrounds when asking questions. For example, the QA unit can ask questions using examples specific to a region based on the user's geographical background. The QA unit can also ask questions using appropriate expressions by taking into account the user's cultural background. The QA unit can also ask questions tailored to the user's language and habits. This allows for a more appropriate understanding of the user's intentions by asking questions tailored to the user's background. Some or all of the above-described processing in the QA unit can be performed using, for example, AI, or without AI. For example, the QA unit can customize the content of questions by taking into account the user's geographical and cultural backgrounds using AI. This allows for a more appropriate understanding of the user's intentions by asking questions tailored to the user's background.

[0077] When asking a question, the QA unit refers to the user's past picture book creation history and prioritizes asking related questions. For example, when asking a question, the QA unit refers to the user's past picture book creation history and prioritizes asking related questions. For example, the QA unit asks related questions based on the theme of picture books the user has created in the past. The QA unit can also ask questions about characters based on the user's past character preferences. The QA unit can also ask questions about plots based on the user's past plot settings. This allows related questions to be prioritized by referring to the past history. Some or all of the above-described processing in the QA unit may be performed using, for example, AI, or may be performed without using AI. For example, the QA unit uses AI to refer to the user's past picture book creation history and prioritizes asking related questions. This allows the QA unit to refer to the past history and prioritize asking related questions.

[0078] The character creation unit can estimate the user's emotions and adjust the character's facial expressions and movements based on the estimated user emotions. The character creation unit, for example, estimates the user's emotions and adjusts the character's facial expressions and movements based on the estimated user emotions. For example, if the user is relaxed, the character creation unit can create a character with a calm expression. Also, if the user is excited, the character creation unit can create a character with active movements. Also, if the user is sad, the character creation unit can create a character with a comforting expression. This allows for creating a character that matches the user's emotions, thereby providing a more appropriate character. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the character creation unit may be performed using, for example, AI, or without AI. For example, the character creation unit can estimate the user's emotions using AI and adjust the character's facial expressions and movements. This allows the character creation unit to create a character that corresponds to the user's emotions, thereby providing a more appropriate character.

[0079] The character creation unit can suggest an appropriate character by referring to the user's past character preference data when creating a character. For example, the character creation unit can suggest an appropriate character by referring to the user's past character preference data when creating a character. For example, the character creation unit can suggest an optimal character based on the characteristics of characters previously selected by the user. The character creation unit can also suggest a similar character from the user's past preference data. The character creation unit can also analyze the user's past preference data and suggest a new character. In this way, the optimal character can be suggested by referring to the past preference data. Some or all of the above-described processing in the character creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the character creation unit can suggest an appropriate character by referring to the user's past character preference data using AI. In this way, the character creation unit can suggest an optimal character by referring to the past preference data.

[0080] The character creation unit can automatically generate a character's background story based on user input when creating a character. The character creation unit automatically generates a character's background story based on user input, for example, when creating a character. For example, the character creation unit generates a character's background story based on a theme input by the user. The character creation unit can also generate a character's background story based on a situation input by the user. The character creation unit can also generate a background story based on character characteristics input by the user. By generating a background story based on the user's input, a more consistent character can be provided. Some or all of the above-described processing in the character creation unit may be performed using AI, for example, or may be performed without using AI. For example, the character creation unit can automatically generate a character's background story based on the user's input using AI. By generating a background story based on the user's input, the character creation unit can provide a more consistent character.

[0081] The character creation unit can estimate the user's emotions and change the color and design of the character based on the estimated user's emotions. The character creation unit, for example, estimates the user's emotions and changes the color and design of the character based on the estimated user's emotions. For example, if the user is relaxed, the character creation unit can create a character with calm colors. If the user is excited, the character creation unit can also create a character with vivid colors. If the user is sad, the character creation unit can also create a character with subdued colors. This allows for creating a character with colors and designs that correspond to the user's emotions, thereby providing a more appropriate character. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the character creation unit may be performed using, for example, AI, or without AI. For example, the character creation unit can estimate the user's emotions using AI and change the color and design of the character. This allows the character creation unit to create a character with colors and designs that match the user's emotions, thereby providing a more appropriate character.

[0082] The character creation unit can customize the character's appearance by taking into account the user's geographical and cultural background when creating a character. For example, the character creation unit customizes the character's appearance by taking into account the user's geographical and cultural background when creating a character. For example, the character creation unit creates a character wearing clothing unique to the region based on the user's geographical background. The character creation unit can also create a character with an appropriate appearance by taking into account the user's cultural background. The character creation unit can also create a character that matches the user's language and customs. This allows for a more appropriate character to be provided by creating a character with an appearance that matches the user's background. Some or all of the above-described processing in the character creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the character creation unit can customize the character's appearance by taking into account the user's geographical and cultural background using AI. This allows for a more appropriate character to be provided by creating a character with an appearance that matches the user's background.

[0083] The plot setting unit can estimate the user's emotions and adjust the plot development based on the estimated user's emotions. For example, the plot setting unit can estimate the user's emotions and adjust the plot development based on the estimated user's emotions. For example, if the user is relaxed, the plot setting unit can set a plot with a calm development. Furthermore, if the user is excited, the plot setting unit can set a plot with a thrilling development. Furthermore, if the user is sad, the plot setting unit can set a plot with an emotional development. This allows for a more appropriate story to be provided by setting a plot according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the plot setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the plot setting unit can estimate the user's emotions using AI and adjust the plot development. This allows for a more appropriate story to be provided by setting a plot according to the user's emotions.

[0084] The plot setting unit can generate an appropriate plot by referring to plot data that has been previously successful when setting a plot. For example, the plot setting unit generates an appropriate plot by referring to plot data that has been previously successful when setting a plot. For example, the plot setting unit generates an optimal plot based on plot data that has been previously successful. The plot setting unit can also generate a plot by referring to plot data that has been successfully executed in accordance with a user's intention. The plot setting unit can also analyze the past successful data and generate a plot that is optimal for the user's intention. In this way, an optimal plot can be generated by referring to the past successful data. Some or all of the above-described processing in the plot setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the plot setting unit can generate an appropriate plot by referring to plot data that has been previously successful using AI. In this way, the plot setting unit can generate an optimal plot by referring to the past successful data.

[0085] The plot setting unit can automatically generate a detailed scenario of the plot based on user input during plot setting. For example, the plot setting unit automatically generates a detailed scenario of the plot based on user input during plot setting. For example, the plot setting unit generates a detailed scenario based on a theme input by the user. The plot setting unit can also generate a detailed scenario based on a situation input by the user. The plot setting unit can also generate a detailed scenario based on character characteristics input by the user. By generating a detailed scenario based on user input, a more consistent story can be provided. Some or all of the above-described processing in the plot setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the plot setting unit can automatically generate a detailed scenario of the plot based on user input using AI. By generating a detailed scenario based on user input, a more consistent story can be provided.

[0086] The plot setting unit can estimate the user's emotions and adjust the tempo and rhythm of the plot based on the estimated user's emotions. For example, the plot setting unit can estimate the user's emotions and adjust the tempo and rhythm of the plot based on the estimated user's emotions. For example, the plot setting unit can set a plot with a slow tempo if the user is relaxed. Furthermore, the plot setting unit can set a plot with a fast tempo if the user is excited. Furthermore, the plot setting unit can set a plot with an emotional rhythm if the user is sad. This allows for a more appropriate story to be provided by setting a tempo and rhythm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the plot setting unit can be performed using, for example, AI, or without AI. For example, the plot setting unit can estimate the user's emotions using AI and adjust the tempo and rhythm of the plot. This allows the plot setting unit to provide a more appropriate story by setting the plot with a tempo and rhythm that matches the user's emotions.

[0087] The plot setting unit can customize the content of the plot by taking into account the geographical and cultural background of the user when setting the plot. For example, the plot setting unit customizes the content of the plot by taking into account the geographical and cultural background of the user when setting the plot. For example, the plot setting unit sets a plot including a situation specific to a region based on the geographical background of the user. The plot setting unit can also set a plot with appropriate content by taking into account the cultural background of the user. The plot setting unit can also set a plot that matches the language and habits of the user. This allows for a more appropriate story to be provided by setting a plot that matches the user's background. Some or all of the above-described processing in the plot setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the plot setting unit can customize the content of the plot by taking into account the geographical and cultural background of the user using AI. This allows for a more appropriate story to be provided by setting a plot that matches the user's background.

[0088] The plot setting unit can analyze the user's social media activity and suggest related plots when setting a plot. For example, the plot setting unit can analyze the user's social media activity and suggest related plots when setting a plot. For example, the plot setting unit can suggest an optimal plot based on a theme frequently mentioned by the user on social media. The plot setting unit can also suggest similar plots based on the user's social media activity. The plot setting unit can also analyze the user's social media activity and suggest a new plot. In this way, related plots can be suggested by analyzing the user's social media activity. Some or all of the above-described processing in the plot setting unit can be performed using AI, for example, or without AI. For example, the plot setting unit can analyze the user's social media activity using AI and suggest related plots. In this way, the plot setting unit can suggest related plots by analyzing the user's social media activity.

[0089] The generation unit can estimate the user's emotions and adjust the story development and image presentation based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the story development and image presentation based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a story with a calm development and images with soft colors. If the user is excited, the generation unit can generate a story with a thrilling development and images with vivid colors. If the user is sad, the generation unit can generate a story with an emotional development and images with calm colors. This allows for the generation of stories and images that correspond to the user's emotions, thereby providing a more appropriate picture book. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without AI. For example, the generation unit can estimate the user's emotions using AI and adjust the story development and image presentation. This allows the generator to generate a story and images that match the user's emotions, thereby providing a more appropriate picture book.

[0090] The generation unit can generate appropriate stories and images by referring to past generation data at the time of generation. For example, the generation unit generates appropriate stories and images by referring to past generation data at the time of generation. For example, the generation unit generates optimal stories and images based on data of stories and images that were successful in the past. The generation unit can also generate by referring to data of successful stories and images that match the user's intention. The generation unit can also analyze past generation data and generate stories and images that are optimal for the user's intention. In this way, optimal stories and images can be generated by referring to past data. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can use AI to refer to past generation data and generate appropriate stories and images. In this way, the generation unit can generate optimal stories and images by referring to past data.

[0091] The generation unit can automatically generate a detailed story scenario and image layout based on user input during generation. The generation unit, for example, automatically generates a detailed story scenario and image layout based on user input during generation. For example, the generation unit generates a detailed story scenario and image layout based on a theme input by the user. The generation unit can also generate a detailed story scenario and image layout based on a situation input by the user. The generation unit can also generate a detailed story scenario and image layout based on character characteristics input by the user. This allows for a more consistent picture book by generating a detailed scenario and image layout based on user input. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can automatically generate a detailed story scenario and image layout based on user input using AI. This allows for a more consistent picture book by generating a detailed story scenario and image layout based on user input.

[0092] The generation unit can estimate the user's emotions and adjust the length of the story and the number of images based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the length of the story and the number of images based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point story with fewer images. Alternatively, if the user is relaxed, the generation unit can generate a longer story with detailed explanations and more images. Alternatively, if the user is excited, the generation unit can generate a story and images with visually stimulating effects. This allows for adjusting the length of the story and the number of images according to the user's emotions, thereby providing a more appropriate picture book. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without AI. For example, the generation unit can estimate the user's emotions using AI and adjust the length of the story and the number of images. This allows the generator to provide a more appropriate picture book by adjusting the length of the story and the number of images according to the user's emotions.

[0093] The generation unit can customize the content of the story and images during generation, taking into account the user's geographical and cultural background. For example, the generation unit customizes the content of the story and images during generation, taking into account the user's geographical and cultural background. For example, the generation unit generates stories and images including regional situations based on the user's geographical background. The generation unit can also generate stories and images with appropriate content taking into account the user's cultural background. The generation unit can also generate stories and images tailored to the user's language and habits. This allows for the generation of stories and images tailored to the user's background, thereby providing a more appropriate picture book. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can customize the content of the story and images using AI, taking into account the user's geographical and cultural background. This allows for the generation unit to generate stories and images tailored to the user's background, thereby providing a more appropriate picture book.

[0094] The generation unit can analyze the user's social media activity and suggest related stories and images at the time of generation. For example, the generation unit can analyze the user's social media activity and suggest related stories and images at the time of generation. For example, the generation unit can suggest optimal stories and images based on themes frequently mentioned by the user on social media. The generation unit can also suggest similar stories and images from the user's social media activity. The generation unit can also analyze the user's social media activity and suggest new stories and images. In this way, related stories and images can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can analyze the user's social media activity using AI and suggest related stories and images. In this way, the generation unit can suggest related stories and images by analyzing the user's social media activity.

[0095] The questioning unit can estimate the user's emotions and adjust the content and order of questions based on the estimated user's emotions. For example, the questioning unit can estimate the user's emotions and adjust the content and order of questions based on the estimated user's emotions. For example, if the user is nervous, the questioning unit can start with simple questions to relax the user. Also, if the user is excited, the questioning unit can ask specific questions early on to quickly understand the user's intentions. Also, if the user is tired, the questioning unit can reduce the number of questions and focus on important questions. This makes it possible to ask questions that are appropriate for the user's emotions and more appropriately understand the user's intentions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the questioning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the questioning unit can estimate the user's emotions using an AI and adjust the content and order of questions. This makes it possible to ask questions that are appropriate for the user's emotions and more appropriately understand the user's intentions.

[0096] The questioning unit can analyze past user response data and generate an appropriate question pattern when asking a question. For example, the questioning unit can analyze past user response data and generate an appropriate question pattern when asking a question. For example, the questioning unit generates an effective question pattern based on response data of users with similar intentions in the past. The questioning unit can also analyze users' response tendencies and determine the most appropriate question order. The questioning unit can also select a question format that is easy for the user to answer from past data. This makes it possible to ask effective questions by utilizing past data. Some or all of the above-described processing in the questioning unit may be performed using, for example, AI, or may be performed without using AI. For example, the questioning unit can analyze past user response data using AI and generate an appropriate question pattern. This makes it possible to ask effective questions by utilizing past data.

[0097] The questioning unit can estimate the user's emotions and change the tone and wording of the questions based on the estimated user emotions. For example, the questioning unit can estimate the user's emotions and change the tone and wording of the questions based on the estimated user emotions. For example, if the user is relaxed, the questioning unit can ask the questions in a friendly tone. If the user is nervous, the questioning unit can ask the questions in a calm tone. If the user is excited, the questioning unit can ask the questions in an energetic tone. This allows the user's intention to be more appropriately understood by asking questions in a tone and wording that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the questioning unit may be performed using an AI, or may be performed without using an AI. For example, the questioning unit can estimate the user's emotions using an AI and change the tone and wording of the questions. This allows the questioning unit to ask questions in a tone and expression that matches the user's emotions, thereby enabling a more appropriate understanding of the user's intentions.

[0098] The questioning unit can customize the content of the question by taking into account the geographical and cultural background of the user when asking a question. For example, the questioning unit customizes the content of the question by taking into account the geographical and cultural background of the user when asking a question. For example, the questioning unit asks questions using examples specific to a region based on the geographical background of the user. The questioning unit can also ask questions using appropriate expressions by taking into account the cultural background of the user. The questioning unit can also ask questions that are tailored to the language and habits of the user. This makes it possible to understand the user's intention more appropriately by asking questions that are tailored to the user's background. Some or all of the above-described processing in the questioning unit may be performed using, for example, AI, or may be performed without using AI. For example, the questioning unit can customize the content of the question by taking into account the geographical and cultural background of the user using AI. This makes it possible to understand the user's intention more appropriately by asking questions that are tailored to the user's background.

[0099] The customization unit can estimate the user's emotions and adjust the character's appearance and personality based on the estimated user's emotions. The customization unit, for example, estimates the user's emotions and adjusts the character's appearance and personality based on the estimated user's emotions. For example, the customization unit can create a character with a calm personality when the user is relaxed. The customization unit can also create a character with a lively personality when the user is excited. The customization unit can also create a character with a comforting personality when the user is sad. This allows a more appropriate character to be provided by creating a character with an appearance and personality that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or without AI. For example, the customization unit can estimate the user's emotions using AI and adjust the character's appearance and personality. This allows the customization unit to create a character with an appearance and personality that matches the user's emotions, thereby providing a more appropriate character.

[0100] The customization unit can suggest appropriate customization by referring to the user's past character preference data during customization. For example, the customization unit can suggest appropriate customization by referring to the user's past character preference data during customization. For example, the customization unit can suggest optimal customization based on the characteristics of characters previously selected by the user. The customization unit can also suggest similar characters from the user's past preference data. The customization unit can also analyze the user's past preference data and suggest new characters. In this way, optimal customization can be suggested by referring to the past preference data. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can suggest appropriate customization by referring to the user's past character preference data using AI. In this way, the customization unit can suggest optimal customization by referring to the past preference data.

[0101] The customization unit can estimate the user's emotions and change the character's color and design based on the estimated user's emotions. The customization unit, for example, estimates the user's emotions and changes the character's color and design based on the estimated user's emotions. For example, the customization unit can create a character with calm colors when the user is relaxed. The customization unit can also create a character with vibrant colors when the user is excited. The customization unit can also create a character with subdued colors when the user is sad. This allows for a more appropriate character to be provided by creating a character with colors and designs that correspond to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or without AI. For example, the customization unit can estimate the user's emotions using AI and change the character's color and design. This allows the customization unit to create a character with colors and designs that match the user's emotions, thereby providing a more appropriate character.

[0102] The customization unit can customize the character's appearance by taking into account the user's geographical background and cultural background during customization. For example, the customization unit customizes the character's appearance by taking into account the user's geographical background and cultural background during customization. For example, the customization unit creates a character wearing clothing unique to the region based on the user's geographical background. The customization unit can also create a character with an appropriate appearance by taking into account the user's cultural background. The customization unit can also create a character that matches the user's language and customs. This makes it possible to provide a more appropriate character by creating a character with an appearance that matches the user's background. Some or all of the above-described processing by the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can customize the character's appearance by taking into account the user's geographical background and cultural background using AI. This makes it possible to provide a more appropriate character by creating a character with an appearance that matches the user's background.

[0103] The scenario designation unit can estimate the user's emotions and adjust the overall scenario based on the estimated user emotions. The scenario designation unit, for example, estimates the user's emotions and adjusts the overall scenario based on the estimated user emotions. For example, if the user is relaxed, the scenario designation unit can set a calm overall scenario. Furthermore, if the user is excited, the scenario designation unit can set a thrilling overall scenario. Furthermore, if the user is sad, the scenario designation unit can set a moving overall scenario. This allows for setting an overall scenario based on the user's emotions, thereby providing a more appropriate story. The emotion designation is realized using an emotion designation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the scenario designation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the scenario designation unit can estimate the user's emotions using an AI and adjust the overall scenario. This allows the scenario designation unit to provide a more appropriate story by setting the outline of the scenario according to the user's emotions.

[0104] The scenario designation unit can generate an appropriate scenario by referencing past successful scenario data when designating a scenario. For example, the scenario designation unit generates an appropriate scenario by referencing past successful scenario data when designating a scenario. For example, the scenario designation unit generates an optimal scenario based on past successful scenario data. The scenario designation unit can also generate a scenario by referencing successful scenario data that matches the user's intention. The scenario designation unit can also analyze past success data and generate a scenario that best matches the user's intention. In this way, an optimal scenario can be generated by referencing past success data. Some or all of the above-described processing in the scenario designation unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario designation unit can use AI to refer to past successful scenario data to generate an appropriate scenario. In this way, the scenario designation unit can generate an optimal scenario by referencing past success data.

[0105] The scenario designation unit can estimate the user's emotions and adjust the tempo and rhythm of the scenario based on the estimated user emotions. The scenario designation unit, for example, estimates the user's emotions and adjusts the tempo and rhythm of the scenario based on the estimated user emotions. For example, the scenario designation unit can set a scenario with a slow tempo when the user is relaxed. The scenario designation unit can also set a scenario with a fast tempo when the user is excited. The scenario designation unit can also set a scenario with an emotional rhythm when the user is sad. This allows a more appropriate story to be provided by setting a scenario with a tempo and rhythm that corresponds to the user's emotions. The emotion designation is realized using an emotion designation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the scenario designation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the scenario designation unit can estimate the user's emotions using an AI and adjust the tempo and rhythm of the scenario. This allows the scenario designation unit to provide a more appropriate story by setting a scenario with a tempo and rhythm that matches the user's emotions.

[0106] The scenario designation unit can customize the content of the scenario by taking into account the user's geographical and cultural background when designating a scenario. For example, the scenario designation unit customizes the content of the scenario by taking into account the user's geographical and cultural background when designating a scenario. For example, the scenario designation unit sets a scenario including a situation specific to a region based on the user's geographical background. The scenario designation unit can also set a scenario with appropriate content by taking into account the user's cultural background. The scenario designation unit can also set a scenario that matches the user's language and habits. This makes it possible to provide a more appropriate story by setting a scenario that matches the user's background. Some or all of the above-described processing in the scenario designation unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario designation unit can customize the content of the scenario by taking into account the user's geographical and cultural background using AI. This makes it possible to provide a more appropriate story by setting a scenario that matches the user's background.

[0107] The quality control unit can estimate the user's emotions and adjust the quality of the generated story and images based on the estimated user emotions. For example, the quality control unit estimates the user's emotions and adjusts the quality of the generated story and images based on the estimated user emotions. For example, if the user is relaxed, the quality control unit can generate stories and images with calm colors and soft expressions. If the user is excited, the quality control unit can generate stories and images with vivid colors and dynamic expressions. If the user is sad, the quality control unit can generate stories and images with calm colors and moving expressions. This allows for the provision of a more appropriate picture book by generating stories and images with quality appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the quality control unit can be performed using, for example, AI, or without AI. For example, the quality control department can use AI to estimate a user's emotions and adjust the quality of the generated story and images. This allows the quality control department to provide more appropriate picture books by generating stories and images with quality that corresponds to the user's emotions.

[0108] The quality control unit can apply an appropriate quality control method by referring to past quality data during quality control. For example, the quality control unit can apply an appropriate quality control method by referring to past quality data during quality control. For example, the quality control unit can apply an optimal quality control method based on quality control data that was successful in the past. The quality control unit can also perform quality control by referring to successful quality control data that matches the user's intention. The quality control unit can also analyze past quality data and apply a quality control method that is optimal for the user's intention. In this way, the optimal quality control method can be applied by referring to past data. Some or all of the above-mentioned processing in the quality control unit may be performed using, for example, AI, or may be performed without using AI. For example, the quality control unit can use AI to refer to past quality data and apply an appropriate quality control method. In this way, the quality control unit can apply an optimal quality control method by referring to past data.

[0109] The quality control unit can estimate the user's emotions and adjust the quality control standards based on the estimated user emotions. For example, the quality control unit can estimate the user's emotions and adjust the quality control standards based on the estimated user emotions. For example, if the user is relaxed, the quality control unit can perform quality control using gentle standards. Also, if the user is excited, the quality control unit can perform quality control using strict standards. Also, if the user is sad, the quality control unit can perform quality control using moving standards. This allows for quality control based on standards appropriate to the user's emotions, thereby providing a more appropriate picture book. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the quality control unit can be performed using AI, or without AI. For example, the quality control unit can estimate the user's emotions using AI and adjust the quality control standards. This allows the quality control department to perform quality control based on standards that correspond to the user's feelings, thereby providing more appropriate picture books.

[0110] The quality control unit can customize the content of quality control during quality control, taking into account the geographical and cultural background of the user. For example, the quality control unit customizes the content of quality control during quality control, taking into account the geographical and cultural background of the user. For example, the quality control unit performs quality control using region-specific standards based on the geographical background of the user. The quality control unit can also perform quality control using appropriate standards taking into account the cultural background of the user. The quality control unit can also perform quality control using standards tailored to the language and customs of the user. This allows for more appropriate picture books to be provided by performing quality control using standards tailored to the user's background. Some or all of the above-described processing in the quality control unit may be performed using, for example, AI, or may be performed without using AI. For example, the quality control unit can customize the content of quality control using AI, taking into account the geographical and cultural background of the user. This allows for more appropriate picture books to be provided by performing quality control using standards tailored to the user's background. === Hard Collateral 1-1 === Each of the multiple elements, including the QA unit, character creation unit, plot setting unit, generation unit, question unit, customization unit, scenario designation unit, and quality control unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the QA unit generates questions to understand the user's intentions via the control unit 46A of the smart device 14, and analyzes the answers via the specific processing unit 290 of the data processing device 12. For example, the character creation unit designs a character via the control unit 46A of the smart device 14, and sets details of the character via the specific processing unit 290 of the data processing device 12. For example, the plot setting unit sets a plot via the specific processing unit 290 of the data processing device 12, and the generation unit generates a story and images via the control unit 46A of the smart device 14. For example, the question unit asks specific questions via the control unit 46A of the smart device 14, and the customization unit customizes the character's appearance and personality via the control unit 46A of the smart device 14. The scenario specification unit specifies the outline of the plot, for example, by the specific processing unit 290 of the data processing device 12, and the quality control unit manages the quality of the story and images generated by the specific processing unit 290 of the data processing device 12, for example. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned QA unit, character creation unit, plot setting unit, generation unit, question unit, customization unit, scenario designation unit, and quality control unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the QA unit generates questions to understand the user's intention via the control unit 46A of the smart glasses 214, and analyzes the answers via the specific processing unit 290 of the data processing device 12. For example, the character creation unit designs a character via the control unit 46A of the smart glasses 214, and sets details of the character via the specific processing unit 290 of the data processing device 12. For example, the plot setting unit sets a plot via the specific processing unit 290 of the data processing device 12, and the generation unit generates a story and images via the control unit 46A of the smart glasses 214. For example, the question unit asks specific questions via the control unit 46A of the smart glasses 214, and the customization unit customizes the character's appearance and personality via the control unit 46A of the smart glasses 214. The scenario specification unit specifies the outline of the plot, for example, by the specific processing unit 290 of the data processing device 12, and the quality control unit manages the quality of the story and images generated by the specific processing unit 290 of the data processing device 12, for example. === Hard Collateral 1-3 === Each of the multiple elements including the QA unit, character creation unit, plot setting unit, generation unit, question unit, customization unit, scenario designation unit, and quality control unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the QA unit generates questions to understand the user's intentions via the control unit 46A of the headset type terminal 314, and analyzes the answers via the specific processing unit 290 of the data processing device 12. For example, the character creation unit designs a character via the control unit 46A of the headset type terminal 314, and sets details of the character via the specific processing unit 290 of the data processing device 12. For example, the plot setting unit sets a plot via the specific processing unit 290 of the data processing device 12, and the generation unit generates a story and images via the control unit 46A of the headset type terminal 314. For example, the question unit asks specific questions via the control unit 46A of the headset type terminal 314, and the customization unit customizes the character's appearance and personality via the control unit 46A of the headset type terminal 314. The scenario specification unit specifies the outline of the plot, for example, by the specific processing unit 290 of the data processing device 12, and the quality control unit manages the quality of the story and images generated by the specific processing unit 290 of the data processing device 12, for example. === Hard Collateral 1-4 === Each of the multiple elements including the QA unit, character creation unit, plot setting unit, generation unit, questioning unit, customization unit, scenario designation unit, and quality control unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the QA unit generates questions to understand the user's intentions using the control unit 46A of the robot 414, and analyzes the answers using the specific processing unit 290 of the data processing device 12. For example, the character creation unit designs a character using the control unit 46A of the robot 414, and sets details of the character using the specific processing unit 290 of the data processing device 12. For example, the plot setting unit sets a plot using the specific processing unit 290 of the data processing device 12, and the generation unit generates a story and images using the control unit 46A of the robot 414. For example, the questioning unit asks specific questions using the control unit 46A of the robot 414, and the customization unit customizes the character's appearance and personality using the control unit 46A of the robot 414. The scenario specification unit specifies the outline of the plot, for example, by the specific processing unit 290 of the data processing device 12, and the quality control unit manages the quality of the story and images generated by the specific processing unit 290 of the data processing device 12, for example.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The picture book creation system not only reflects the user's intentions, but also refers to the user's past picture book creation history to ask more personalized questions. For example, it can prioritize questions related to the themes and character preferences of picture books created by the user in the past. It can also suggest the most suitable characters based on the characteristics of characters selected by the user in the past. It can also ask questions related to the plot based on past plot settings. This makes it possible to ask more effective questions by utilizing the user's past history.

[0113] The picture book creation system can customize the content of questions taking into account the geographical and cultural background of the user. For example, questions can be posed using examples specific to the region based on the user's geographical background. Questions can also be posed using appropriate expressions taking into account the user's cultural background. Furthermore, questions can be posed that are tailored to the user's language and customs. This allows for a more appropriate understanding of the user's intentions by asking questions that are tailored to the user's background.

[0114] The picture book creation system can estimate the user's emotions and adjust the character's appearance and personality based on the estimated user's emotions. For example, if the user is relaxed, a character with a calm personality can be created. If the user is excited, a character with a lively personality can be created. Furthermore, if the user is sad, a character with a comforting personality can be created. In this way, a more appropriate character can be provided by creating a character with an appearance and personality that corresponds to the user's emotions.

[0115] When setting a plot, the picture book creation system can automatically generate a detailed scenario for the plot based on the user's input. For example, a detailed scenario can be generated based on a theme input by the user. A detailed scenario can also be generated based on a situation input by the user. A detailed scenario can also be generated based on the characteristics of characters input by the user. In this way, a more consistent story can be provided by generating a detailed scenario based on the user's input.

[0116] The picture book creation system includes a quality control unit that manages the quality of the generated story and images. The quality control unit manages, for example, the quality of the generated story and images. For example, the quality control unit can manage the accuracy of the text and the resolution of the images. This can improve the quality of the generated picture book. Some or all of the above-described processing in the quality control unit may be performed using, for example, AI, or may be performed without using AI. For example, the quality control unit can manage the quality of the story and images using AI. This can improve the quality of the generated picture book.

[0117] The picture book creation system can estimate the user's emotions and adjust the plot development based on the estimated user's emotions. For example, if the user is relaxed, a calm plot development can be set. If the user is excited, a thrilling plot development can be set. Furthermore, if the user is sad, a moving plot development can be set. In this way, a more appropriate story can be provided by setting a plot according to the user's emotions.

[0118] The picture book creation system can customize the content of stories and images by taking into account the user's geographical and cultural backgrounds when generating them. For example, it can generate stories and images that include situations specific to a region based on the user's geographical background. It can also generate stories and images with appropriate content by taking into account the user's cultural background. It can also generate stories and images that are tailored to the user's language and customs. This makes it possible to provide a more appropriate picture book by generating stories and images that are tailored to the user's background.

[0119] The picture book creation system can estimate the user's emotions and adjust the story development and image expression based on the estimated user's emotions. For example, if the user is relaxed, it can generate a story with a calm development and images with soft colors. If the user is excited, it can generate a story with a thrilling development and images with vivid colors. Furthermore, if the user is sad, it can generate a story with a moving development and images with calm colors. In this way, it is possible to provide a more appropriate picture book by generating stories and images that correspond to the user's emotions.

[0120] The picture book creation system can generate appropriate stories and images by referencing past generation data. For example, the generation unit can generate optimal stories and images based on data on stories and images that have been successful in the past. It can also generate by referencing data on successful stories and images that match the user's intentions. Furthermore, it can analyze past generation data and generate stories and images that are optimal for the user's intentions. In this way, it is possible to generate optimal stories and images by referencing past data.

[0121] The picture book creation system can estimate the user's emotions and adjust the length of the story and the number of images based on the estimated user emotions. For example, if the user is in a hurry, a short, to-the-point story with fewer images can be generated. If the user is relaxed, a longer story with detailed explanations and more images can be generated. Furthermore, if the user is excited, a story and images with visually stimulating effects can be generated. In this way, by adjusting the length of the story and the number of images according to the user's emotions, a more appropriate picture book can be provided.

[0122] The processing flow of the second embodiment will be briefly explained below.

[0123] Step 1: The QA department is responsible for understanding the user's intent. When the user enters what they want to communicate, the QA department asks specific questions to dig deeper into the user's intent. For example, they can ask questions such as, "What kind of characters will appear?" or "What kind of situations will depict friendship?" Step 2: The character creation department creates characters based on the intent understood by the QA department. For example, if it is a picture book with a friendship theme, characters of friends will be created. These characters are designed based on the user's intent. Step 3: The plot setting department sets the plot based on the characters created by the character creation department. For example, if it is a picture book with the theme of friendship, the story will be set up in which friends overcome difficulties and deepen their friendship. Step 4: The generation section generates a story and images based on the plot set by the plot setting section. The generation section draws a detailed story based on the plot and generates images to match the story.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0126] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0151] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0164] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0166] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0167] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0168] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0169] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0170] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0171] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0172] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0174] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0179] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0180] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0181] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0185] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0186] 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.

[0187] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0188] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0189] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0190] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0192] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0193] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0194] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0195] [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A QA department to understand user intent, a character creation unit that creates a character based on the intention understood by the QA unit; a plot setting unit that sets a plot based on the character created by the character creation unit; a generation unit that generates a story and images based on the plot set by the plot setting unit; Equipped with A system characterized by:

2. Equipped with a questioning section that asks specific questions to dig deeper into the user's intentions 2. The system of claim 1.

3. Includes a customization section for customizing the character's appearance and personality 2. The system of claim 1.

4. It has a scenario specification section that specifies the outline of the plot 2. The system of claim 1.

5. A quality control department is in place to manage the quality of the stories and images produced.

2. The system of claim 1.

6. The QA department: Inferring user emotions and adjusting the content and order of questions based on the estimated user emotions 2. The system of claim 1.

7. The QA department: Analyze past user response data to generate appropriate question patterns 2. The system of claim 1.

8. The QA department: When asking a question, analyze the user's response speed and reaction in real time and adjust the difficulty of the question.

2. The system of claim 1.

9. The QA department: Inferring user emotions and changing the tone and wording of questions based on the inferred user emotions 2. The system of claim 1.

Images