System

Abstract

An object of the system according to the embodiment is to convert a novel for adults into a novel for children and adjust the amount of information according to the language proficiency level.SOLUTION: A system includes an extraction unit, a generation unit, and a determination unit. The extraction unit analyzes the original novel and extracts information and main points of the story. The generation unit generates a picture book for children based on the information extracted by the extraction unit. The determination section adjusts the amount of information in the picture book generated by the generation section according to the language proficiency level.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to convert novels aimed at adults into ones suitable for children, and of not being able to adjust the amount of information according to language proficiency levels.

[0005] The system according to the embodiment aims to convert novels for adults into novels for children and adjust the amount of information according to language proficiency. [Means for solving the problem]

[0006] The system according to the embodiment includes an extraction unit, a generation unit, and a determination unit. The extraction unit analyzes the original novel and extracts information and key points of the story. The generation unit generates a picture book for children based on the information extracted by the extraction unit. The determination unit adjusts the amount of information in the picture book generated by the generation unit according to the language learning proficiency level. [Effects of the Invention]

[0007] The system according to the embodiment can convert novels for adults into novels for children and adjust the amount of information according to language proficiency. [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 system according to an embodiment of the present invention converts novels for adults into picture books that can be understood by children with low language proficiency. This system analyzes the original novel, extracts important information and story key points, generates a picture book for children, and adjusts the amount of information according to the child's language proficiency. For example, the system analyzes the original novel and extracts key characters, important events, and the flow of the story. Next, the system generates a picture book for children based on the extracted information. The generation AI expresses the extracted information using simple words and illustrations. For example, it replaces complex sentences with simple words and illustrates important scenes. Furthermore, the amount of information increases as the child's language proficiency improves. For example, as the child grows, the system provides a picture book with more information and detailed descriptions. This allows the system to enjoy the contrast of the book depending on the child's age and language proficiency. This allows the system to generate a picture book that can be understood by children with low language proficiency and adjusts the amount of information as the child grows, thereby providing a reading experience appropriate for the child's age and language proficiency. For example, when a child reads a picture book as an adult, they may gain access to information that they could not obtain from the picture book. This allows children to enjoy the book on a deeper level as they grow, while still retaining the charm of a picture book that can be enjoyed even with the amount of information pared down.

[0029] A conversion system according to an embodiment includes an extraction unit, a generation unit, and a determination unit. The extraction unit analyzes the original novel and extracts information and key story points. For example, the extraction unit uses text analysis technology to extract key characters, important events, and the flow of the story. The extraction unit can also perform grammatical and semantic analysis to grasp the essence of the story. Furthermore, the extraction unit can use AI to select important information taking into account the theme and message of the story. The generation unit generates a picture book for children based on the information extracted by the extraction unit. For example, the generation unit expresses the extracted information using simple words and illustrations. The generation AI replaces complex sentences with simple words and expresses important scenes with illustrations. The generation unit can also adjust the page layout and design to match the progression of the story. Furthermore, the generation unit can estimate the user's emotions and adjust the way the picture book is presented based on the estimated user's emotions. The determination unit adjusts the amount of information in the picture book generated by the generation unit according to the user's language proficiency. For example, the determination unit increases or decreases the amount of information according to the user's language proficiency. The determination unit can also customize the amount of information depending on the user's age or grade level. Furthermore, the determination unit can estimate the user's emotions and determine a method for adjusting the amount of information based on the estimated user's emotions. As a result, the conversion system according to the embodiment can generate picture books that can be understood even by children with low language proficiency, and can adjust the amount of information as the children grow, thereby providing a reading experience appropriate for the child's age and language proficiency.

[0030] The generation unit can express the extracted information using simple words and illustrations. For example, the generation unit replaces complex sentences with simple words. For example, the generation unit shortens long sentences and converts difficult vocabulary into simple words. The generation unit can also express important scenes with illustrations. For example, the generation unit creates illustrations to visually emphasize the climax of the story. The generation unit can also adjust the balance between text and illustrations as the story progresses. For example, the generation unit may include more text in the early part of the story and more illustrations in the middle part. This makes it possible to generate a picture book that is easy to understand even for children with low language proficiency. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the extracted information into a generation AI and have the generation AI generate simple words and illustrations.

[0031] The determination unit can adjust the amount of information according to the user's language proficiency. The determination unit, for example, increases or decreases the amount of information according to the user's language proficiency. For example, the determination unit decreases the amount of information when the user's language proficiency is low and increases the amount of information when the user's language proficiency is high. The determination unit can also customize the amount of information according to the user's age or grade. For example, the determination unit may provide a picture book with simple language and less information for young children and a picture book with more detailed information for elementary school students. The determination unit can also estimate the user's emotions and determine how to adjust the amount of information based on the estimated user's emotions. For example, the determination unit may provide a picture book with detailed information when the user is relaxed and a concise picture book with the main points when the user is in a hurry. This enables the amount of information to be adjusted according to the user's language proficiency. Some or all of the above-described processing by the determination unit may be performed using AI, or may be performed without AI. For example, the determination unit may input the user's language proficiency data into AI and have the AI ​​adjust the amount of information.

[0032] The generation unit can generate a picture book with an increasing amount of information as the child's language proficiency level improves. For example, the generation unit increases the amount of information as the child's language proficiency level improves. For example, the generation unit provides a picture book with more information and detailed descriptions as the child grows. The generation unit can also adjust the page layout and design as the story progresses. For example, the generation unit uses a simple layout in the beginning of the story and devise a layout to show the character's growth and change in the middle. The generation unit can also estimate the user's emotions and adjust the way the picture book is presented based on the estimated user's emotions. For example, the generation unit generates a picture book using calm colors and soft-touch illustrations when the user is relaxed, and generates a picture book using vivid colors and dynamic illustrations when the user is excited. This makes it possible to provide a picture book with an increasing amount of information as the user grows. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input language proficiency data into the generation AI and cause the generation AI to increase the amount of information.

[0033] The extraction unit can extract key characters, important events, and the flow of the story. The extraction unit can extract key characters, important events, and the flow of the story using, for example, text analysis technology. For example, the extraction unit can extract characters who frequently appear in the story and events that are turning points in the story. The extraction unit can also perform grammatical and semantic analysis to grasp the essence of the story. For example, the extraction unit can analyze the structure and chronological progression of the story to extract the flow of the story. Furthermore, the extraction unit can use AI to select important information taking into account the theme and message of the story. For example, if the theme of the story is friendship, the extraction unit can prioritize extracting scenes and dialogue that symbolize friendship. This allows the essence of the original work to be preserved by extracting important elements of the story. Some or all of the above-mentioned processing in the extraction unit can be performed using AI, or can be performed without AI. For example, the extraction unit can input the original novel data into AI and have it extract key characters and important events.

[0034] The generation unit can replace complex sentences with simpler words. For example, the generation unit replaces complex sentences with simpler words. For example, the generation unit shortens long sentences and converts difficult vocabulary into simpler words. The generation unit can also represent important scenes with illustrations. For example, the generation unit creates illustrations to visually emphasize the climax of the story. The generation unit can also adjust the balance between text and illustrations as the story progresses. For example, the generation unit may include more text in the early part of the story and more illustrations in the middle part. This allows for the generation of a picture book that is easy for children to understand by replacing complex sentences with simpler words. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input complex sentences into a generation AI and have the generation AI convert the sentences into simpler words.

[0035] The generation unit can represent important scenes with illustrations. For example, the generation unit creates illustrations to visually emphasize the climax scene of a story. For example, the generation unit visually emphasizes the scene by using creative colors and composition. The generation unit can also emphasize moving scenes in a story by depicting characters' facial expressions and backgrounds in detail. Furthermore, the generation unit can emphasize action scenes in a story by using dynamic poses and effects. This can help children understand the story by visually representing important scenes. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input information about important scenes into the generation AI and have the generation AI generate illustrations.

[0036] The extraction unit can select important information taking into consideration the theme and message of the story. For example, the extraction unit selects important information taking into consideration the theme and message of the story. For example, if the theme of the story is friendship, the extraction unit can prioritize extracting scenes and dialogues that symbolize friendship. Also, if the message of the story is courage, the extraction unit can prioritize extracting actions and events that show courage. Furthermore, if the theme of the story is growth, the extraction unit can prioritize extracting scenes that show the character's growth process. This can promote deeper understanding by taking into consideration the theme and message of the story. Some or all of the above-mentioned processing in the extraction unit may be performed using AI, or may be performed without using AI. For example, the extraction unit can input the theme and message of the story into AI and have the AI ​​select important information.

[0037] The extraction unit can extract key points while preserving the style and vocabulary characteristics of the original work. For example, the extraction unit extracts key points while preserving the style and vocabulary characteristics of the original work. For example, if the style of the original work is poetic, the extraction unit extracts key points while preserving the poetic expressions. Furthermore, if the vocabulary of the original work is technical, the extraction unit can extract key points while replacing technical terms with simpler words. Furthermore, if the style of the original work is humorous, the extraction unit can extract key points without losing the humor. In this way, by preserving the style and vocabulary characteristics of the original work, the atmosphere of the original work can be preserved. Some or all of the above-mentioned processing in the extraction unit may be performed using AI, or may be performed without using AI. For example, the extraction unit can input the style and vocabulary characteristics of the original work into AI and have the AI ​​extract key points.

[0038] The extraction unit can select information taking into consideration the character's growth and changes as the story progresses. For example, the extraction unit selects information taking into consideration the character's growth and changes as the story progresses. For example, the extraction unit prioritizes extracting scenes that show the character's growth process. The extraction unit can also extract important scenes in which the character's personality or behavior changes. Furthermore, the extraction unit can extract scenes in which the relationships between characters change. This allows the story to be deepened by taking into consideration the character's growth and changes. Some or all of the above-described processing in the extraction unit may be performed using AI, or may be performed without using AI. For example, the extraction unit can input data on the character's growth and changes into AI and have the AI ​​select the information.

[0039] The extraction unit can select information taking into account the setting and background information of the story. For example, the extraction unit selects information taking into account the setting and background information of the story. For example, if the setting of the story is in another world, the extraction unit prioritizes extracting scenes that show that worldview. Also, if the background of the story is historical, the extraction unit can extract scenes that show historical events and settings. Furthermore, if the setting of the story is modern, the extraction unit can extract scenes that show modern life and culture. This allows for a deeper understanding of the story by taking into account the setting and background information of the story. Some or all of the above-mentioned processing in the extraction unit may be performed using AI, or may be performed without using AI. For example, the extraction unit can input the setting and background information of the story into AI and have the AI ​​select the information.

[0040] The extraction unit can apply different extraction algorithms depending on the genre of the story. For example, the extraction unit can apply different extraction algorithms depending on the genre of the story. For example, in the case of fantasy, the extraction unit can apply an algorithm that prioritizes extracting scenes of magic or adventure. In addition, in the case of mystery, the extraction unit can apply an algorithm that prioritizes extracting scenes of mystery solving or suspense. Furthermore, in the case of romance, the extraction unit can apply an algorithm that prioritizes extracting scenes of love or emotion. In this way, by applying an extraction algorithm depending on the genre of the story, more appropriate information can be provided. Some or all of the above-mentioned processing in the extraction unit may be performed using AI, or may be performed without using AI. For example, the extraction unit can input story genre data into AI and have the AI ​​apply the extraction algorithm.

[0041] The extraction unit can select information that reflects the intention and style of the original author. For example, the extraction unit selects information that reflects the intention and style of the original author. For example, if the author's intention is to depict the growth of a character, the extraction unit can prioritize extracting scenes that show that growth. Also, if the author's style is humorous, the extraction unit can prioritize extracting scenes that include humor. Furthermore, if the author's intention is to raise social issues, the extraction unit can prioritize extracting scenes that show those issues. This allows the original author's intention and style to be reflected, thereby preventing the appeal of the original work from being diminished. Some or all of the above-described processing in the extraction unit can be performed using AI, or can be performed without AI. For example, the extraction unit can input the author's intention and style data into AI and have the AI ​​select the information.

[0042] The generation unit can draw out the appeal of a story by depicting the character's facial expressions and actions in detail. The generation unit, for example, draws out the appeal of a story by depicting the character's facial expressions and actions in detail. For example, in a scene where a character is smiling, the generation unit depicts the smiling expression in detail. The generation unit can also depict the dynamic pose and background in detail in a scene where a character is running. Furthermore, the generation unit can also depict the tears and changes in facial expression in detail in a scene where a character is crying. In this way, the appeal of a story can be drawn out by depicting the character's facial expressions and actions in detail. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the character's facial expressions and actions into the generation AI and have the generation AI perform detailed depictions.

[0043] The generation unit can create illustrations to visually emphasize important scenes in a story. For example, the generation unit can create illustrations to visually emphasize the climax scene of a story. For example, the generation unit visually emphasizes the scene by using creative colors and composition. The generation unit can also emphasize moving scenes in a story by depicting characters' expressions and backgrounds in detail. Furthermore, the generation unit can emphasize action scenes in a story by using dynamic poses and effects. This can help readers understand the story by visually emphasizing important scenes in the story. Some or all of the above-mentioned processing in the generation unit can be performed using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input information about important scenes into the generation AI and have the generation AI generate illustrations.

[0044] The generation unit can adjust the page layout and design in accordance with the progress of the story. The generation unit, for example, adjusts the page layout and design in accordance with the progress of the story. For example, the generation unit uses a simple layout in the early part of the story to make the introduction easy to understand. The generation unit can also devise a layout in the middle of the story to show the growth and change of the characters. Furthermore, the generation unit can adjust the layout in the late part of the story to emphasize the climax or conclusion. This makes it possible to adjust the page layout and design in accordance with the progress of the story. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input story progress data into the generation AI and have the generation AI adjust the page layout and design.

[0045] The generation unit can apply different illustration styles depending on the genre of the story. For example, the generation unit applies different illustration styles depending on the genre of the story. For example, in the case of fantasy, the generation unit generates a picture book using a fantastical illustration style. In addition, in the case of mystery, the generation unit can generate a picture book using an illustration style with darker colors and emphasized shadows. Furthermore, in the case of romance, the generation unit can generate a picture book using an illustration style with soft colors and warmth. This makes it possible to apply an illustration style depending on the genre of the story. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input story genre data into the generation AI and cause the generation AI to apply the illustration style.

[0046] The generation unit can create background illustrations based on the setting of the story. For example, the generation unit creates background illustrations based on the setting of the story. For example, if the setting of the story is another world, the generation unit creates background illustrations that show that worldview. Furthermore, if the setting of the story is modern, the generation unit can create background illustrations that show modern landscapes and buildings. Furthermore, if the setting of the story is historical, the generation unit can create background illustrations that show historical landscapes and buildings. This makes it possible to create background illustrations based on the setting of the story. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input story setting data into the generation AI and cause the generation AI to create background illustrations.

[0047] The generation unit can adjust the balance between text and illustrations as the story progresses. The generation unit, for example, adjusts the balance between text and illustrations as the story progresses. For example, the generation unit may use more text in the early part of the story to explain the introduction in detail. The generation unit may also use more illustrations in the middle part of the story to visually show the character's actions and emotions. Furthermore, the generation unit may adjust the balance between text and illustrations at the end of the story to emphasize the climax. This makes it possible to adjust the balance between text and illustrations as the story progresses. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input story progression data into the generation AI and have the generation AI adjust the balance between text and illustrations.

[0048] The determination unit can set the optimal amount of information by referring to the user's past reading history. The determination unit, for example, sets the optimal amount of information by referring to the user's past reading history. For example, the determination unit sets the optimal amount of information based on the amount of information in picture books the user has read in the past. The determination unit can also predict and set the user's preferred amount of information from the user's past reading history. Furthermore, the determination unit can set the amount of information by referring to the genres and themes of picture books the user has read in the past. In this way, the optimal amount of information can be set by referring to the user's past reading history. Some or all of the above-described processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can input the user's reading history data into AI and have the AI ​​set the amount of information.

[0049] The determination unit can customize the amount of information according to the user's age and grade. The determination unit customizes the amount of information according to the user's age and grade, for example. For example, the determination unit provides picture books with simple language and less information for young children. The determination unit can also provide picture books with slightly more detailed information for elementary school students. The determination unit can also provide picture books with more information and detailed descriptions for junior high school students and above. This makes it possible to customize the amount of information according to the user's age and grade. Some or all of the above-mentioned processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can input the user's age and grade data into AI and have the AI ​​customize the amount of information.

[0050] The determination unit can adjust the amount of information taking into account the user's reading speed and level of comprehension. The determination unit adjusts the amount of information taking into account, for example, the user's reading speed and level of comprehension. For example, if the user's reading speed is fast, the determination unit can increase the amount of information to provide a detailed picture book. In addition, if the user's level of comprehension is high, the determination unit can also provide a picture book containing complex information. Furthermore, if the user's reading speed is slow, the determination unit can reduce the amount of information to provide a concise picture book. This makes it possible to adjust the amount of information according to the user's reading speed and level of comprehension. Some or all of the above-mentioned processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can input the user's reading speed and level of comprehension data into AI and have the AI ​​adjust the amount of information.

[0051] The determination unit can gradually increase or decrease the amount of information based on the user's learning progress. The determination unit, for example, gradually increases or decreases the amount of information based on the user's learning progress. For example, if the user's learning progress is fast, the determination unit can gradually increase the amount of information to provide a detailed picture book. Furthermore, if the user's learning progress is slow, the determination unit can gradually decrease the amount of information to provide a concise picture book. Furthermore, the determination unit can appropriately adjust the amount of information according to the user's learning progress. This makes it possible to gradually increase or decrease the amount of information according to the user's learning progress. Some or all of the above-mentioned processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can input the user's learning progress data into AI and have the AI ​​increase or decrease the amount of information.

[0052] The determination unit can customize the amount of information based on the user's interests. The determination unit customizes the amount of information based on the user's interests. For example, the determination unit may provide more information about a topic in which the user is interested. The determination unit may also provide more information about characters in which the user is highly interested. Furthermore, the determination unit may appropriately adjust the amount of information based on the user's interests. This makes it possible to customize the amount of information according to the user's interests. Some or all of the above-described processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit may input the user's interests into AI and have the AI ​​customize the amount of information.

[0053] The determination unit can improve the method for adjusting the amount of information by reflecting user feedback. The determination unit, for example, improves the method for adjusting the amount of information by reflecting user feedback. For example, the determination unit improves the method for adjusting the amount of information based on user feedback. The determination unit can also appropriately adjust the amount of information by reflecting user opinions. Furthermore, the determination unit can analyze user feedback and optimize the method for adjusting the amount of information. In this way, the method for adjusting the amount of information can be improved by reflecting user feedback. Some or all of the above-described processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can input user feedback data into AI and cause the AI ​​to improve the method for adjusting the amount of information.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The generation unit can add interactive elements that match the progression of the story based on the user's learning progress. For example, the generation unit can provide a quiz or a mini-game when the user reaches a specific scene. The generation unit can also lead the user to a different ending by allowing them to choose options as the story progresses. Furthermore, the generation unit can add new characters or side stories to the story based on the user's learning progress. This allows the user to enjoy the story more deeply.

[0056] The generation unit may provide a function that allows a user to create their own character and participate in the story as the story progresses. For example, the generation unit may allow a user to customize the appearance and personality of their character. The generation unit may also generate scenes in which the user's character interacts with other characters in the story or participates in adventures. Furthermore, the generation unit may allow the user's character to grow and acquire new skills and items as the story progresses. This allows the user to become more immersed in the story.

[0057] The generation unit can provide a function that allows the user to select options within the story as the story progresses, leading to different endings. For example, the generation unit can change the development of the story by the user selecting an option in a specific scene. The generation unit can also add different characters or scenes depending on the user's selection. Furthermore, the generation unit can provide multiple endings with different story outcomes based on the user's selection. This allows the user to enjoy the story multiple times.

[0058] The generation unit may provide a function that allows a user to create their own character and participate in the story as the story progresses. For example, the generation unit may allow a user to customize the appearance and personality of their character. The generation unit may also generate scenes in which the user's character interacts with other characters in the story or participates in adventures. Furthermore, the generation unit may allow the user's character to grow and acquire new skills and items as the story progresses. This allows the user to become more immersed in the story.

[0059] The generation unit can provide a function that allows the user to select options within the story as the story progresses, leading to different endings. For example, the generation unit can change the development of the story by the user selecting an option in a specific scene. The generation unit can also add different characters or scenes depending on the user's selection. Furthermore, the generation unit can provide multiple endings with different story outcomes based on the user's selection. This allows the user to enjoy the story multiple times.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The extraction unit analyzes the original novel and extracts information and key story points. For example, the extraction unit uses text analysis technology to extract key characters, important events, and the flow of the story. The extraction unit can also perform grammatical and semantic analysis to grasp the essence of the story. Furthermore, the extraction unit can use AI to select important information taking into account the story's theme and message. Step 2: The generator generates a picture book for children based on the information extracted by the extractor. For example, the generator expresses the extracted information using simple words and illustrations. The generation AI replaces complex sentences with simple words and illustrates important scenes. The generator can also adjust the page layout and design as the story progresses. Furthermore, the generator can estimate the user's emotions and adjust the way the picture book is presented based on the estimated user emotions. Step 3: The determination unit adjusts the amount of information in the picture book generated by the generation unit according to the language proficiency level. For example, the determination unit increases or decreases the amount of information according to the language proficiency level. The determination unit can also customize the amount of information according to the user's age or grade level. Furthermore, the determination unit can estimate the user's emotions and determine a method for adjusting the amount of information based on the estimated user's emotions.

[0062] (Example 2) A system according to an embodiment of the present invention converts novels for adults into picture books that can be understood by children with low language proficiency. This system analyzes the original novel, extracts important information and story key points, generates a picture book for children, and adjusts the amount of information according to the child's language proficiency. For example, the system analyzes the original novel and extracts key characters, important events, and the flow of the story. Next, the system generates a picture book for children based on the extracted information. The generation AI expresses the extracted information using simple words and illustrations. For example, it replaces complex sentences with simple words and illustrates important scenes. Furthermore, the amount of information increases as the child's language proficiency improves. For example, as the child grows, the system provides a picture book with more information and detailed descriptions. This allows the system to enjoy the contrast of the book depending on the child's age and language proficiency. This allows the system to generate a picture book that can be understood by children with low language proficiency and adjusts the amount of information as the child grows, thereby providing a reading experience appropriate for the child's age and language proficiency. For example, when a child reads a picture book as an adult, they may gain access to information that they could not obtain from the picture book. This allows children to enjoy the book on a deeper level as they grow, while still retaining the charm of a picture book that can be enjoyed even with the amount of information pared down.

[0063] A conversion system according to an embodiment includes an extraction unit, a generation unit, and a determination unit. The extraction unit analyzes the original novel and extracts information and key story points. For example, the extraction unit uses text analysis technology to extract key characters, important events, and the flow of the story. The extraction unit can also perform grammatical and semantic analysis to grasp the essence of the story. Furthermore, the extraction unit can use AI to select important information taking into account the theme and message of the story. The generation unit generates a picture book for children based on the information extracted by the extraction unit. For example, the generation unit expresses the extracted information using simple words and illustrations. The generation AI replaces complex sentences with simple words and expresses important scenes with illustrations. The generation unit can also adjust the page layout and design to match the progression of the story. Furthermore, the generation unit can estimate the user's emotions and adjust the way the picture book is presented based on the estimated user's emotions. The determination unit adjusts the amount of information in the picture book generated by the generation unit according to the user's language proficiency. For example, the determination unit increases or decreases the amount of information according to the user's language proficiency. The determination unit can also customize the amount of information depending on the user's age or grade level. Furthermore, the determination unit can estimate the user's emotions and determine a method for adjusting the amount of information based on the estimated user's emotions. As a result, the conversion system according to the embodiment can generate picture books that can be understood even by children with low language proficiency, and can adjust the amount of information as the children grow, thereby providing a reading experience appropriate for the child's age and language proficiency.

[0064] The generation unit can express the extracted information using simple words and illustrations. For example, the generation unit replaces complex sentences with simple words. For example, the generation unit shortens long sentences and converts difficult vocabulary into simple words. The generation unit can also express important scenes with illustrations. For example, the generation unit creates illustrations to visually emphasize the climax of the story. The generation unit can also adjust the balance between text and illustrations as the story progresses. For example, the generation unit may include more text in the early part of the story and more illustrations in the middle part. This makes it possible to generate a picture book that is easy to understand even for children with low language proficiency. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the extracted information into a generation AI and have the generation AI generate simple words and illustrations.

[0065] The determination unit can adjust the amount of information according to the user's language proficiency. The determination unit, for example, increases or decreases the amount of information according to the user's language proficiency. For example, the determination unit decreases the amount of information when the user's language proficiency is low and increases the amount of information when the user's language proficiency is high. The determination unit can also customize the amount of information according to the user's age or grade. For example, the determination unit may provide a picture book with simple language and less information for young children and a picture book with more detailed information for elementary school students. The determination unit can also estimate the user's emotions and determine how to adjust the amount of information based on the estimated user's emotions. For example, the determination unit may provide a picture book with detailed information when the user is relaxed and a concise picture book with the main points when the user is in a hurry. This enables the amount of information to be adjusted according to the user's language proficiency. Some or all of the above-described processing by the determination unit may be performed using AI, or may be performed without AI. For example, the determination unit may input the user's language proficiency data into AI and have the AI ​​adjust the amount of information.

[0066] The generation unit can generate a picture book with an increasing amount of information as the child's language proficiency level improves. For example, the generation unit increases the amount of information as the child's language proficiency level improves. For example, the generation unit provides a picture book with more information and detailed descriptions as the child grows. The generation unit can also adjust the page layout and design as the story progresses. For example, the generation unit uses a simple layout in the beginning of the story and devise a layout to show the character's growth and change in the middle. The generation unit can also estimate the user's emotions and adjust the way the picture book is presented based on the estimated user's emotions. For example, the generation unit generates a picture book using calm colors and soft-touch illustrations when the user is relaxed, and generates a picture book using vivid colors and dynamic illustrations when the user is excited. This makes it possible to provide a picture book with an increasing amount of information as the user grows. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input language proficiency data into the generation AI and cause the generation AI to increase the amount of information.

[0067] The extraction unit can extract key characters, important events, and the flow of the story. The extraction unit can extract key characters, important events, and the flow of the story using, for example, text analysis technology. For example, the extraction unit can extract characters who frequently appear in the story and events that are turning points in the story. The extraction unit can also perform grammatical and semantic analysis to grasp the essence of the story. For example, the extraction unit can analyze the structure and chronological progression of the story to extract the flow of the story. Furthermore, the extraction unit can use AI to select important information taking into account the theme and message of the story. For example, if the theme of the story is friendship, the extraction unit can prioritize extracting scenes and dialogue that symbolize friendship. This allows the essence of the original work to be preserved by extracting important elements of the story. Some or all of the above-mentioned processing in the extraction unit can be performed using AI, or can be performed without AI. For example, the extraction unit can input the original novel data into AI and have it extract key characters and important events.

[0068] The generation unit can replace complex sentences with simpler words. For example, the generation unit replaces complex sentences with simpler words. For example, the generation unit shortens long sentences and converts difficult vocabulary into simpler words. The generation unit can also represent important scenes with illustrations. For example, the generation unit creates illustrations to visually emphasize the climax of the story. The generation unit can also adjust the balance between text and illustrations as the story progresses. For example, the generation unit may include more text in the early part of the story and more illustrations in the middle part. This allows for the generation of a picture book that is easy for children to understand by replacing complex sentences with simpler words. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input complex sentences into a generation AI and have the generation AI convert the sentences into simpler words.

[0069] The generation unit can represent important scenes with illustrations. For example, the generation unit creates illustrations to visually emphasize the climax scene of a story. For example, the generation unit visually emphasizes the scene by using creative colors and composition. The generation unit can also emphasize moving scenes in a story by depicting characters' facial expressions and backgrounds in detail. Furthermore, the generation unit can emphasize action scenes in a story by using dynamic poses and effects. This can help children understand the story by visually representing important scenes. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input information about important scenes into the generation AI and have the generation AI generate illustrations.

[0070] The extraction unit can estimate the user's emotions and determine the priority of information to be extracted based on the estimated user emotions. The extraction unit, for example, estimates the user's emotions and determines the priority of information to be extracted based on the estimated user emotions. For example, if the user is excited, the extraction unit can prioritize extracting action scenes and tense scenes from the story. Also, if the user is relaxed, the extraction unit can prioritize extracting calm and moving scenes from the story. Furthermore, if the user is sad, the extraction unit can prioritize extracting encouraging and hopeful scenes from the story. This allows for more appropriate information to be provided by determining the priority of information 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, 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 extraction unit may be performed using AI, or without AI. For example, the extraction unit can input the user's emotion data into AI and have the AI ​​determine the priority of information.

[0071] The extraction unit can select important information taking into consideration the theme and message of the story. For example, the extraction unit selects important information taking into consideration the theme and message of the story. For example, if the theme of the story is friendship, the extraction unit can prioritize extracting scenes and dialogues that symbolize friendship. Also, if the message of the story is courage, the extraction unit can prioritize extracting actions and events that show courage. Furthermore, if the theme of the story is growth, the extraction unit can prioritize extracting scenes that show the character's growth process. This can promote deeper understanding by taking into consideration the theme and message of the story. Some or all of the above-mentioned processing in the extraction unit may be performed using AI, or may be performed without using AI. For example, the extraction unit can input the theme and message of the story into AI and have the AI ​​select important information.

[0072] The extraction unit can extract key points while preserving the style and vocabulary characteristics of the original work. For example, the extraction unit extracts key points while preserving the style and vocabulary characteristics of the original work. For example, if the style of the original work is poetic, the extraction unit extracts key points while preserving the poetic expressions. Furthermore, if the vocabulary of the original work is technical, the extraction unit can extract key points while replacing technical terms with simpler words. Furthermore, if the style of the original work is humorous, the extraction unit can extract key points without losing the humor. In this way, by preserving the style and vocabulary characteristics of the original work, the atmosphere of the original work can be preserved. Some or all of the above-mentioned processing in the extraction unit may be performed using AI, or may be performed without using AI. For example, the extraction unit can input the style and vocabulary characteristics of the original work into AI and have the AI ​​extract key points.

[0073] The extraction unit can select information taking into consideration the character's growth and changes as the story progresses. For example, the extraction unit selects information taking into consideration the character's growth and changes as the story progresses. For example, the extraction unit prioritizes extracting scenes that show the character's growth process. The extraction unit can also extract important scenes in which the character's personality or behavior changes. Furthermore, the extraction unit can extract scenes in which the relationships between characters change. This allows the story to be deepened by taking into consideration the character's growth and changes. Some or all of the above-described processing in the extraction unit may be performed using AI, or may be performed without using AI. For example, the extraction unit can input data on the character's growth and changes into AI and have the AI ​​select the information.

[0074] The extraction unit can estimate the user's emotion and adjust the presentation method of the extracted information based on the estimated user emotion. For example, the extraction unit can estimate the user's emotion and adjust the presentation method of the extracted information based on the estimated user emotion. For example, if the user is excited, the extraction unit can represent an action scene with a dynamic illustration. If the user is relaxed, the extraction unit can represent a calm scene with soft colors. If the user is sad, the extraction unit can represent an emotional scene with a warm illustration. This allows for more appropriate information to be provided by adjusting the presentation method according to the user's emotion. The 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 extraction unit may be performed using AI, or may be performed without AI. For example, the extraction unit can input the user's emotion data into AI and have the AI ​​adjust the presentation method of the information.

[0075] The extraction unit can select information taking into account the setting and background information of the story. For example, the extraction unit selects information taking into account the setting and background information of the story. For example, if the setting of the story is in another world, the extraction unit prioritizes extracting scenes that show that worldview. Also, if the background of the story is historical, the extraction unit can extract scenes that show historical events and settings. Furthermore, if the setting of the story is modern, the extraction unit can extract scenes that show modern life and culture. This allows for a deeper understanding of the story by taking into account the setting and background information of the story. Some or all of the above-mentioned processing in the extraction unit may be performed using AI, or may be performed without using AI. For example, the extraction unit can input the setting and background information of the story into AI and have the AI ​​select the information.

[0076] The extraction unit can apply different extraction algorithms depending on the genre of the story. For example, the extraction unit can apply different extraction algorithms depending on the genre of the story. For example, in the case of fantasy, the extraction unit can apply an algorithm that prioritizes extracting scenes of magic or adventure. In addition, in the case of mystery, the extraction unit can apply an algorithm that prioritizes extracting scenes of mystery solving or suspense. Furthermore, in the case of romance, the extraction unit can apply an algorithm that prioritizes extracting scenes of love or emotion. In this way, by applying an extraction algorithm depending on the genre of the story, more appropriate information can be provided. Some or all of the above-mentioned processing in the extraction unit may be performed using AI, or may be performed without using AI. For example, the extraction unit can input story genre data into AI and have the AI ​​apply the extraction algorithm.

[0077] The extraction unit can select information that reflects the intention and style of the original author. For example, the extraction unit selects information that reflects the intention and style of the original author. For example, if the author's intention is to depict the growth of a character, the extraction unit can prioritize extracting scenes that show that growth. Also, if the author's style is humorous, the extraction unit can prioritize extracting scenes that include humor. Furthermore, if the author's intention is to raise social issues, the extraction unit can prioritize extracting scenes that show those issues. This allows the original author's intention and style to be reflected, thereby preventing the appeal of the original work from being diminished. Some or all of the above-described processing in the extraction unit can be performed using AI, or can be performed without AI. For example, the extraction unit can input the author's intention and style data into AI and have the AI ​​select the information.

[0078] The generation unit can estimate the user's emotions and adjust the way the picture book is presented based on the estimated user's emotions. For example, the generation unit can estimate the user's emotions and adjust the way the picture book is presented based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a picture book using calm colors and soft-touch illustrations. If the user is excited, the generation unit can also generate a picture book using vivid colors and dynamic illustrations. Furthermore, if the user is sad, the generation unit can generate a picture book using warm colors and emphasizing moving scenes. This allows for adjusting the way the picture book is presented based on 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 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 generation unit can be performed using AI, or without AI. For example, the generation unit can input the user's emotion data into AI and have the AI ​​adjust the way the picture book is presented.

[0079] The generation unit can draw out the appeal of a story by depicting the character's facial expressions and actions in detail. The generation unit, for example, draws out the appeal of a story by depicting the character's facial expressions and actions in detail. For example, in a scene where a character is smiling, the generation unit depicts the smiling expression in detail. The generation unit can also depict the dynamic pose and background in detail in a scene where a character is running. Furthermore, the generation unit can also depict the tears and changes in facial expression in detail in a scene where a character is crying. In this way, the appeal of a story can be drawn out by depicting the character's facial expressions and actions in detail. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the character's facial expressions and actions into the generation AI and have the generation AI perform detailed depictions.

[0080] The generation unit can create illustrations to visually emphasize important scenes in a story. For example, the generation unit can create illustrations to visually emphasize the climax scene of a story. For example, the generation unit visually emphasizes the scene by using creative colors and composition. The generation unit can also emphasize moving scenes in a story by depicting characters' expressions and backgrounds in detail. Furthermore, the generation unit can emphasize action scenes in a story by using dynamic poses and effects. This can help readers understand the story by visually emphasizing important scenes in the story. Some or all of the above-mentioned processing in the generation unit can be performed using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input information about important scenes into the generation AI and have the generation AI generate illustrations.

[0081] The generation unit can adjust the page layout and design in accordance with the progress of the story. The generation unit, for example, adjusts the page layout and design in accordance with the progress of the story. For example, the generation unit uses a simple layout in the early part of the story to make the introduction easy to understand. The generation unit can also devise a layout in the middle of the story to show the growth and change of the characters. Furthermore, the generation unit can adjust the layout in the late part of the story to emphasize the climax or conclusion. This makes it possible to adjust the page layout and design in accordance with the progress of the story. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input story progress data into the generation AI and have the generation AI adjust the page layout and design.

[0082] The generation unit can estimate the user's emotions and adjust the number of pages in the picture book based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the number of pages in the picture book based on the estimated user emotions. For example, if the user is in a hurry, the generation unit reduces the number of pages to generate a picture book that can be read in a short time. The generation unit can also increase the number of pages to generate a picture book with detailed content when the user is relaxed. Furthermore, if the user is excited, the generation unit can adjust the number of pages to generate a picture book with a good tempo. This makes it possible to adjust the number of pages 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 may 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 input the user's emotion data into AI and have the AI ​​adjust the number of pages.

[0083] The generation unit can apply different illustration styles depending on the genre of the story. For example, the generation unit applies different illustration styles depending on the genre of the story. For example, in the case of fantasy, the generation unit generates a picture book using a fantastical illustration style. In addition, in the case of mystery, the generation unit can generate a picture book using an illustration style with darker colors and emphasized shadows. Furthermore, in the case of romance, the generation unit can generate a picture book using an illustration style with soft colors and warmth. This makes it possible to apply an illustration style depending on the genre of the story. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input story genre data into the generation AI and cause the generation AI to apply the illustration style.

[0084] The generation unit can create background illustrations based on the setting of the story. For example, the generation unit creates background illustrations based on the setting of the story. For example, if the setting of the story is another world, the generation unit creates background illustrations that show that worldview. Furthermore, if the setting of the story is modern, the generation unit can create background illustrations that show modern landscapes and buildings. Furthermore, if the setting of the story is historical, the generation unit can create background illustrations that show historical landscapes and buildings. This makes it possible to create background illustrations based on the setting of the story. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input story setting data into the generation AI and cause the generation AI to create background illustrations.

[0085] The generation unit can adjust the balance between text and illustrations as the story progresses. The generation unit, for example, adjusts the balance between text and illustrations as the story progresses. For example, the generation unit may use more text in the early part of the story to explain the introduction in detail. The generation unit may also use more illustrations in the middle part of the story to visually show the character's actions and emotions. Furthermore, the generation unit may adjust the balance between text and illustrations at the end of the story to emphasize the climax. This makes it possible to adjust the balance between text and illustrations as the story progresses. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input story progression data into the generation AI and have the generation AI adjust the balance between text and illustrations.

[0086] The determination unit can estimate the user's emotion and determine a method for adjusting the amount of information based on the estimated user's emotion. For example, the determination unit can estimate the user's emotion and determine a method for adjusting the amount of information based on the estimated user's emotion. For example, the determination unit can provide a picture book containing detailed information when the user is relaxed. The determination unit can also provide a concise picture book that focuses on the main points when the user is in a hurry. Furthermore, the determination unit can provide a picture book containing visually stimulating information when the user is excited. This allows for determining a method for adjusting the amount of information according to the user's emotion, thereby providing more appropriate information. The emotion estimation is realized 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 determination unit can be performed using AI, or without AI. For example, the determination unit can input the user's emotion data into AI and have the AI ​​determine a method for adjusting the amount of information.

[0087] The determination unit can set the optimal amount of information by referring to the user's past reading history. The determination unit, for example, sets the optimal amount of information by referring to the user's past reading history. For example, the determination unit sets the optimal amount of information based on the amount of information in picture books the user has read in the past. The determination unit can also predict and set the user's preferred amount of information from the user's past reading history. Furthermore, the determination unit can set the amount of information by referring to the genres and themes of picture books the user has read in the past. In this way, the optimal amount of information can be set by referring to the user's past reading history. Some or all of the above-described processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can input the user's reading history data into AI and have the AI ​​set the amount of information.

[0088] The determination unit can customize the amount of information according to the user's age and grade. The determination unit customizes the amount of information according to the user's age and grade, for example. For example, the determination unit provides picture books with simple language and less information for young children. The determination unit can also provide picture books with slightly more detailed information for elementary school students. The determination unit can also provide picture books with more information and detailed descriptions for junior high school students and above. This makes it possible to customize the amount of information according to the user's age and grade. Some or all of the above-mentioned processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can input the user's age and grade data into AI and have the AI ​​customize the amount of information.

[0089] The determination unit can adjust the amount of information taking into account the user's reading speed and level of comprehension. The determination unit adjusts the amount of information taking into account, for example, the user's reading speed and level of comprehension. For example, if the user's reading speed is fast, the determination unit can increase the amount of information to provide a detailed picture book. In addition, if the user's level of comprehension is high, the determination unit can also provide a picture book containing complex information. Furthermore, if the user's reading speed is slow, the determination unit can reduce the amount of information to provide a concise picture book. This makes it possible to adjust the amount of information according to the user's reading speed and level of comprehension. Some or all of the above-mentioned processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can input the user's reading speed and level of comprehension data into AI and have the AI ​​adjust the amount of information.

[0090] The determination unit can estimate the user's emotion and adjust the display method of the amount of information based on the estimated user's emotion. For example, the determination unit can estimate the user's emotion and adjust the display method of the amount of information based on the estimated user's emotion. For example, the determination unit can provide a simple, highly visible display method when the user is nervous. The determination unit can also provide a display method that includes detailed information when the user is relaxed. Furthermore, the determination unit can also provide a display method that focuses on the main points when the user is in a hurry. This makes it possible to adjust the display method of the amount of information according to the user's emotion. 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 determination unit may be performed using AI, or may be performed without AI. For example, the determination unit can input the user's emotion data into AI and have the AI ​​adjust the display method.

[0091] The determination unit can gradually increase or decrease the amount of information based on the user's learning progress. The determination unit, for example, gradually increases or decreases the amount of information based on the user's learning progress. For example, if the user's learning progress is fast, the determination unit can gradually increase the amount of information to provide a detailed picture book. Furthermore, if the user's learning progress is slow, the determination unit can gradually decrease the amount of information to provide a concise picture book. Furthermore, the determination unit can appropriately adjust the amount of information according to the user's learning progress. This makes it possible to gradually increase or decrease the amount of information according to the user's learning progress. Some or all of the above-mentioned processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can input the user's learning progress data into AI and have the AI ​​increase or decrease the amount of information.

[0092] The determination unit can customize the amount of information based on the user's interests. The determination unit customizes the amount of information based on the user's interests. For example, the determination unit may provide more information about a topic in which the user is interested. The determination unit may also provide more information about characters in which the user is highly interested. Furthermore, the determination unit may appropriately adjust the amount of information based on the user's interests. This makes it possible to customize the amount of information according to the user's interests. Some or all of the above-described processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit may input the user's interests into AI and have the AI ​​customize the amount of information.

[0093] The determination unit can improve the method for adjusting the amount of information by reflecting user feedback. The determination unit, for example, improves the method for adjusting the amount of information by reflecting user feedback. For example, the determination unit improves the method for adjusting the amount of information based on user feedback. The determination unit can also appropriately adjust the amount of information by reflecting user opinions. Furthermore, the determination unit can analyze user feedback and optimize the method for adjusting the amount of information. In this way, the method for adjusting the amount of information can be improved by reflecting user feedback. Some or all of the above-described processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can input user feedback data into AI and cause the AI ​​to improve the method for adjusting the amount of information. === Hard Collateral 1-1 === Each of the multiple elements, including the extraction unit, generation unit, and determination unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the extraction unit is realized by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing device 12, and analyzes the original novel and extracts information and key points of the story. The generation unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and generates a picture book for children based on the extracted information. The determination unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and adjusts the amount of information in the generated picture book according to language proficiency. === Hard Collateral 1-2 === Each of the multiple elements, including the extraction unit, generation unit, and determination unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the extraction unit is realized by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and analyzes the original novel and extracts information and key points of the story. The generation unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and generates a picture book for children based on the extracted information. The determination unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and adjusts the amount of information in the generated picture book according to language learning proficiency. === Hard Collateral 1-3 === Each of the multiple elements including the extraction unit, generation unit, and determination 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 extraction unit is realized by the processor 46 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and analyzes the original novel and extracts information and key points of the story. The generation unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and generates a picture book for children based on the extracted information. The determination unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and adjusts the amount of information in the generated picture book according to language learning proficiency. === Hard Collateral 1-4 === Each of the multiple elements including the extraction unit, generation unit, and determination unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the extraction unit is realized by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing device 12, and analyzes the original novel and extracts information and key points of the story. The generation unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and generates a picture book for children based on the extracted information. The determination unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and adjusts the amount of information in the generated picture book according to language proficiency.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The generation unit can add interactive elements that match the progression of the story based on the user's learning progress. For example, the generation unit can provide a quiz or a mini-game when the user reaches a specific scene. The generation unit can also lead the user to a different ending by allowing them to choose options as the story progresses. Furthermore, the generation unit can add new characters or side stories to the story based on the user's learning progress. This allows the user to enjoy the story more deeply.

[0096] The determination unit can estimate the user's emotions and dynamically change the progress of the story based on the estimated emotions. For example, if the user is excited, the number of action scenes can be increased. If the user is relaxed, the number of calm scenes can be increased. Furthermore, if the user is sad, moving scenes can be added. This makes it possible to progress the story according to the user's emotions.

[0097] The generation unit may provide a function that allows a user to create their own character and participate in the story as the story progresses. For example, the generation unit may allow a user to customize the appearance and personality of their character. The generation unit may also generate scenes in which the user's character interacts with other characters in the story or participates in adventures. Furthermore, the generation unit may allow the user's character to grow and acquire new skills and items as the story progresses. This allows the user to become more immersed in the story.

[0098] The extraction unit can estimate the user's emotions and emphasize the theme and message of the story based on the estimated emotions. For example, if the user is moved, scenes themed around friendship and love can be emphasized. If the user is excited, scenes themed around adventure and challenge can be emphasized. Furthermore, if the user is relaxed, scenes themed around healing and comfort can be emphasized. In this way, the theme and message of the story can be emphasized according to the user's emotions.

[0099] The generation unit can provide a function that allows the user to select options within the story as the story progresses, leading to different endings. For example, the generation unit can change the development of the story by the user selecting an option in a specific scene. The generation unit can also add different characters or scenes depending on the user's selection. Furthermore, the generation unit can provide multiple endings with different story outcomes based on the user's selection. This allows the user to enjoy the story multiple times.

[0100] The determination unit can estimate the user's emotions and dynamically change the progress of the story based on the estimated emotions. For example, if the user is excited, the number of action scenes can be increased. If the user is relaxed, the number of calm scenes can be increased. Furthermore, if the user is sad, moving scenes can be added. This makes it possible to progress the story according to the user's emotions.

[0101] The generation unit may provide a function that allows a user to create their own character and participate in the story as the story progresses. For example, the generation unit may allow a user to customize the appearance and personality of their character. The generation unit may also generate scenes in which the user's character interacts with other characters in the story or participates in adventures. Furthermore, the generation unit may allow the user's character to grow and acquire new skills and items as the story progresses. This allows the user to become more immersed in the story.

[0102] The extraction unit can estimate the user's emotions and emphasize the theme and message of the story based on the estimated emotions. For example, if the user is moved, scenes themed around friendship and love can be emphasized. If the user is excited, scenes themed around adventure and challenge can be emphasized. Furthermore, if the user is relaxed, scenes themed around healing and comfort can be emphasized. In this way, the theme and message of the story can be emphasized according to the user's emotions.

[0103] The generation unit can provide a function that allows the user to select options within the story as the story progresses, leading to different endings. For example, the generation unit can change the development of the story by the user selecting an option in a specific scene. The generation unit can also add different characters or scenes depending on the user's selection. Furthermore, the generation unit can provide multiple endings with different story outcomes based on the user's selection. This allows the user to enjoy the story multiple times.

[0104] The determination unit can estimate the user's emotions and dynamically change the progress of the story based on the estimated emotions. For example, if the user is excited, the number of action scenes can be increased. If the user is relaxed, the number of calm scenes can be increased. Furthermore, if the user is sad, moving scenes can be added. This makes it possible to progress the story according to the user's emotions.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The extraction unit analyzes the original novel and extracts information and key story points. For example, the extraction unit uses text analysis technology to extract key characters, important events, and the flow of the story. The extraction unit can also perform grammatical and semantic analysis to grasp the essence of the story. Furthermore, the extraction unit can use AI to select important information taking into account the story's theme and message. Step 2: The generator generates a picture book for children based on the information extracted by the extractor. For example, the generator expresses the extracted information using simple words and illustrations. The generation AI replaces complex sentences with simple words and illustrates important scenes. The generator can also adjust the page layout and design as the story progresses. Furthermore, the generator can estimate the user's emotions and adjust the way the picture book is presented based on the estimated user emotions. Step 3: The determination unit adjusts the amount of information in the picture book generated by the generation unit according to the language proficiency level. For example, the determination unit increases or decreases the amount of information according to the language proficiency level. The determination unit can also customize the amount of information according to the user's age or grade level. Furthermore, the determination unit can estimate the user's emotions and determine a method for adjusting the amount of information based on the estimated user's emotions.

[0107] 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.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[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 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.

[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 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.

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

[0158] 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.

[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] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0177] 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.

[0178] [Explanation of symbols]

[0179] 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. An extraction section analyzes the original novel and extracts key information and story points. a generation unit that generates a picture book for children based on the information extracted by the extraction unit; a determination unit that adjusts the amount of information in the picture book generated by the generation unit according to the language learning proficiency level. A system characterized by:

2. The generation unit Express the extracted information using simple words and illustrations 2. The system of claim 1.

3. The determination unit Adjust the amount of information according to language proficiency 2. The system of claim 1.

4. The generation unit Generate picture books that increase in information content as language proficiency increases 2. The system of claim 1.

5. The extraction unit Extract the main characters, important events, and story flow 2. The system of claim 1.

6. The generation unit Replace complex sentences with simpler words 2. The system of claim 1.

7. The generation unit Illustrate important scenes 2. The system of claim 1.

8. The extraction unit Estimate the user's emotions and prioritize the information to be extracted based on the estimated user emotions.

2. The system of claim 1.

Images