system

Abstract

The system according to the embodiment aims to provide optimal stories according to a child's age, interests, reading level, and educational purpose. [Solution] A system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives information on a child's age, interests, reading level, and educational objectives. The generation unit analyzes the information received by the reception unit and generates an appropriate story. The provision unit provides the story generated by the generation unit as an e-book.

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 made it difficult to select the most appropriate picture book based on a child's age, interests, reading level, and educational objectives.

[0005] The system according to the embodiment aims to provide optimal stories according to a child's age, interests, reading level, and educational purpose. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives information on the child's age, interests, reading level, and educational objectives. The generation unit analyzes the information received by the reception unit and generates an appropriate story. The provision unit provides the story generated by the generation unit as an e-book. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal stories according to a child's age, interests, reading level, and educational purpose. [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 pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example 1) A picture book generation system according to an embodiment of the present invention solves the problem of children's educators having difficulty selecting the most appropriate picture book for a child's age, interests, reading level, and educational objectives. In this picture book generation system, educators input information such as a child's age, interests, reading level, and educational objectives. A generation AI analyzes the information to generate an optimal story and provides it as an e-book. For example, an educator inputs information about a 5-year-old child who is interested in dinosaurs, has a beginner's reading level, and whose educational objective is to improve scientific knowledge. This information is input into the generation AI, which analyzes it and generates an optimal story. The generated story is customized to the child's age and interests and contains content appropriate for the educational objectives. The generated story is provided as an e-book, and educators can download it and read it to children. This allows children to learn according to their educational objectives while engaging their interest. For example, nursery school and kindergarten teachers can generate and read stories tailored to each child in their class. This improves the quality of education and deepens children's learning. The picture book generation system generates an optimal story appropriate for a child's age, interests, reading level, and educational objectives and provides it as an e-book.

[0029] A picture book creation system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives information on a child's age, interests, reading level, and educational objectives. For example, an educator can input information about a 5-year-old child who is interested in dinosaurs, has a beginner's reading level, and whose educational objective is to improve scientific knowledge. The generation unit uses a generation AI to analyze the information received by the reception unit and generate an optimal story. The generation AI generates a story tailored to the child's age and interests based on a large amount of data. For example, a story containing scientific knowledge about dinosaurs is generated. This story is written in simple language tailored to the child's reading level. The provision unit provides the story generated by the generation unit as an e-book. Educators can download the generated story as an e-book and read it to children. This allows for learning that meets educational objectives while attracting children's interest. For example, nursery school or kindergarten teachers can generate and read stories tailored to the children in their class. This improves the quality of education and deepens children's learning. As a result, the picture book creation system according to the embodiment can create an optimal story according to the child's age, interests, reading level, and educational purpose, and provide it as an electronic book.

[0030] The generation unit can use a generation AI to generate a story based on the child's age and interests. The generation unit, for example, uses a generation AI to generate a story tailored to the child's age and interests. The generation AI generates a story tailored to the child's age and interests based on a large amount of data. For example, a story including scientific knowledge about dinosaurs is generated. This story is written in simple language tailored to the child's reading level. In this way, the generation AI can be used to generate a story tailored to the child's age and interests. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a story using an AI model for generating stories based on the child's age and interests.

[0031] The generation unit can generate a story including content that meets the educational purpose. For example, the generation unit generates a story including content that meets the educational purpose. The generation AI generates a story including content that meets the educational purpose based on a large amount of data. For example, a story aimed at improving scientific knowledge is generated. This story is written in simple language that meets a child's reading level. This makes it possible to generate a story including content that meets the educational purpose. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can generate a story using an AI model for generating stories that include content that meets the educational purpose.

[0032] The providing unit can provide the generated story as an e-book. The providing unit, for example, provides the generated story as an e-book. The generation AI generates a story tailored to the age and interests of a child based on a large amount of data. The generated story is provided as an e-book, and educators can download it and read it to children. This makes it possible to provide learning according to educational objectives while attracting children's interest. For example, a teacher at a nursery school or kindergarten can generate a story tailored to the children in their class and read it to them. This improves the quality of education and deepens children's learning. This makes it possible to provide the generated story as an e-book. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can provide the story using an AI model for providing the generated story as an e-book.

[0033] The reception unit can input information input by an educator into the generation AI. The reception unit, for example, inputs information input by an educator into the generation AI. The educator inputs information such as the child's age, interests, reading level, and educational objectives. For example, an educator can input information about a 5-year-old child who is interested in dinosaurs, has an elementary reading level, and whose educational objective is to improve scientific knowledge. This information is input into the generation AI, which analyzes it and generates an optimal story. In this way, the information input by the educator can be input into the generation AI. Some or all of the above-described processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input information using an AI model for inputting the information input by the educator into the generation AI.

[0034] The generation unit can generate a story based on multiple data. The generation unit, for example, generates a story based on multiple data. The generation AI generates a story tailored to a child's age and interests based on a large amount of data. For example, a story including scientific knowledge about dinosaurs is generated. This story is written in simple language tailored to a child's reading level. This allows a story to be generated based on multiple data. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can generate a story using an AI model for generating a story based on multiple data.

[0035] The reception unit can analyze past input data and suggest an appropriate input method. The reception unit, for example, analyzes past input data and suggests an appropriate input method. For example, it can automatically display information that the educator has frequently input in the past as a candidate. It can also preferentially suggest input methods (voice, text, etc.) that the educator has used in the past. It can also predict and suggest information that will be used in a specific time period based on the educator's past input history. This makes it possible to analyze past input data and suggest an optimal input method. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze past input data and suggest an input method using an AI model for suggesting an appropriate input method.

[0036] The reception unit may add a function to automatically complete input information taking into account the child's current learning progress. The reception unit may add a function to automatically complete input information taking into account the child's current learning progress, for example. For example, the reception unit may automatically complete an appropriate educational objective based on the child's learning progress. The reception unit may also automatically complete an appropriate reading level based on the child's reading level. The reception unit may also automatically complete related interests based on the child's interests. This allows the input information to be automatically completed taking into account the child's learning progress. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit may complete the information using an AI model for automatically completing input information taking into account the child's current learning progress.

[0037] The reception unit can prioritize acquiring highly relevant information in consideration of the educator's geographical location information at the time of input. For example, the reception unit prioritizes acquiring highly relevant information in consideration of the educator's geographical location information at the time of input. For example, if the educator is in a specific area, information related to that area can be prioritized. Also, if the educator is in a specific facility, information related to that facility can be prioritized. Also, if the educator is participating in a specific event, information related to that event can be prioritized. In this way, highly relevant information can be prioritized in consideration of the educator's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can acquire information using an AI model for preferentially acquiring highly relevant information in consideration of the educator's geographical location information.

[0038] The reception unit can analyze the educator's social media activity at the time of input and automatically complete the relevant information. The reception unit, for example, analyzes the educator's social media activity at the time of input and automatically completes the relevant information. For example, the reception unit can automatically complete the relevant information based on information shared by the educator on social media. The reception unit can also automatically complete the relevant information based on information about accounts the educator follows on social media. The reception unit can also automatically complete the relevant information based on information about groups the educator participates in on social media. In this way, the educator's social media activity can be analyzed and the relevant information can be automatically completed. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the educator's social media activity and complete the information using an AI model for automatically completing the relevant information.

[0039] The generation unit can adjust the level of detail of the content based on the importance of the story during generation. For example, the generation unit adjusts the level of detail of the content based on the importance of the story during generation. For example, in the case of a story with an important educational purpose, content including detailed explanations can be generated. Also, in the case of a theme that is of great interest to children, content including detailed descriptions can be generated. Also, in the case of a story that can be read in a short time, concise content can be generated. In this way, the level of detail of the content can be adjusted based on the importance of the story. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the content using an AI model for adjusting the level of detail of the content based on the importance of the story.

[0040] The generation unit can apply different generation algorithms depending on the category of the story during generation. For example, the generation unit can apply different generation algorithms depending on the category of the story during generation. For example, in the case of a story including scientific knowledge, a generation algorithm based on scientific data can be applied. In addition, in the case of a fantasy story, a creative storytelling algorithm can be applied. In addition, in the case of a story with a strong educational purpose, a generation algorithm that emphasizes educational content can be applied. In this way, different generation algorithms can be applied depending on the category of the story. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a story using an AI model for applying different generation algorithms depending on the category of the story.

[0041] The generation unit can determine the priority of content based on the time of generation of the story at the time of generation. The generation unit, for example, determines the priority of content based on the time of generation of the story at the time of generation. For example, in the case of a story with a strong educational purpose, important content can be generated preferentially. Also, in the case of a theme that is of great interest to children, related content can be generated preferentially. Also, in the case of a story related to the seasons, content appropriate to the season can be generated preferentially. In this way, the priority of content can be determined based on the time of generation of the story. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can determine the content using an AI model for determining the priority of content based on the time of generation of the story.

[0042] The generation unit can adjust the order of the content based on the relevance of the story during generation. The generation unit, for example, adjusts the order of the content based on the relevance of the story during generation. For example, content related to the theme of the story can be prioritized. Content related to educational purposes can also be prioritized. Content related to children's interests can also be prioritized. In this way, the order of the content can be adjusted based on the relevance of the story. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the content using an AI model for adjusting the order of the content based on the relevance of the story.

[0043] The providing unit can select an appropriate delivery method by referring to the user's past usage history at the time of provision. For example, the providing unit can select an appropriate delivery method by referring to the user's past usage history at the time of provision. For example, the providing unit can select an optimal delivery method based on delivery methods used by the user in the past. The most efficient delivery method can also be selected from the user's past usage history. The user's past usage history can also be analyzed to select the delivery method that provides the highest satisfaction. This makes it possible to select an optimal delivery method by referring to the user's past usage history. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can select a delivery method by using an AI model for selecting an appropriate delivery method by referring to the user's past usage history.

[0044] The providing unit can provide the content in an appropriate format, taking into account the user's device information, when providing the content. For example, the providing unit can provide the content in an appropriate format, taking into account the user's device information, when providing the content. For example, if the user is using a smartphone, the content can be provided in a format optimized for the smartphone. Furthermore, if the user is using a tablet, the content can be provided in a format optimized for the tablet. Furthermore, if the user is using a personal computer, the content can be provided in a format optimized for the personal computer. This makes it possible to provide the content in an optimal format according to the user's device information. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can select a format using an AI model for providing the content in an appropriate format, taking into account the user's device information.

[0045] The providing unit can select an appropriate delivery method by taking into account the user's geographical location information when providing information. For example, the providing unit selects an appropriate delivery method by taking into account the user's geographical location information when providing information. For example, if the user is in a specific area, information related to that area can be provided preferentially. Also, if the user is in a specific facility, information related to that facility can be provided preferentially. Also, if the user is participating in a specific event, information related to that event can be provided preferentially. This makes it possible to select an optimal delivery method according to the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can select a delivery method by using an AI model for selecting an appropriate delivery method by taking into account the user's geographical location information.

[0046] The providing unit can analyze the user's social media activity and suggest related content at the time of providing. For example, the providing unit can analyze the user's social media activity and suggest related content at the time of providing. For example, the providing unit can suggest related content based on information shared by the user on social media. Also, the providing unit can suggest related content based on information about accounts the user follows on social media. Also, the providing unit can suggest related content based on information about groups the user participates in on social media. In this way, the user's social media activity can be analyzed and related content can be suggested. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the providing unit can analyze the user's social media activity and suggest content using an AI model for suggesting related content.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The reception unit can analyze the information entered by the educator in real time and provide immediate feedback based on the input. For example, when the educator enters the child's age and interests, it can instantly present relevant educational content and reference materials. It can also display appropriate questions or prompts for additional information based on the information entered by the educator. Furthermore, if the information entered by the educator is incomplete, it can make suggestions for completion. This allows educators to enter information efficiently and receive support for generating the optimal story.

[0049] During the story generation process, the generator can incorporate the latest research and data related to a specific theme or topic selected by the educator. For example, the latest discoveries and scientific knowledge about dinosaurs can be reflected in the story. The generator can also incorporate expert opinions and commentary related to the theme selected by the educator. Furthermore, the generator can evaluate whether the content of the story is aligned with the educational objectives and revise the content as necessary. This improves the quality of the story and maximizes the educational effect desired by the educator.

[0050] The generator can customize the story generation process to suit a child's learning style and preferences. For example, for a child who prefers visual learning, it can generate a story that makes extensive use of illustrations and diagrams. For a child who prefers auditory learning, it can also generate a story that incorporates audio and music. Furthermore, for a child who prefers tactile learning, it can generate a story that includes interactive elements. This makes it possible to provide stories that suit a child's learning style and improve learning effectiveness.

[0051] The providing unit can provide the generated story in multiple formats. For example, it can provide not only e-book format, but also audiobook format and interactive app format. It can also provide the story in printable PDF format depending on the content of the story. Furthermore, the providing unit can automatically select the optimal delivery method depending on the format selected by the educator. This allows educators to use the story in the format that is best suited to the child's learning style and environment.

[0052] The reception unit can support the generation of optimal stories by referring to similar cases in the past based on the information entered by the educator. For example, it can suggest optimal stories by utilizing data on stories previously generated for children of the same age group or interests. It can also improve the content and structure of stories based on past success stories and feedback. Furthermore, the reception unit can suggest approaches to achieve optimal educational objectives from past data based on the information entered by the educator. This allows educators to utilize past knowledge to generate more effective stories.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The reception unit accepts information about the child's age, interests, reading level, and educational purpose. For example, an educator can enter information about a 5-year-old child who is interested in dinosaurs, has an elementary reading level, and whose educational purpose is to improve scientific knowledge. Step 2: The generator uses AI to analyze the information received by the reception unit and generate an optimal story. Based on a large amount of data, the AI ​​generates a story tailored to the child's age and interests. For example, a story containing scientific knowledge about dinosaurs is generated. The story is written in simple language to suit the child's reading level. Step 3: The provider provides the story generated by the generator as an e-book. Educators can download the generated story as an e-book and read it to children. This makes it possible to provide learning that meets educational objectives while attracting children's interest.

[0055] (Example 2) A picture book generation system according to an embodiment of the present invention solves the problem of children's educators having difficulty selecting the most appropriate picture book for a child's age, interests, reading level, and educational objectives. In this picture book generation system, educators input information such as a child's age, interests, reading level, and educational objectives. A generation AI analyzes the information to generate an optimal story and provides it as an e-book. For example, an educator inputs information about a 5-year-old child who is interested in dinosaurs, has a beginner's reading level, and whose educational objective is to improve scientific knowledge. This information is input into the generation AI, which analyzes it and generates an optimal story. The generated story is customized to the child's age and interests and contains content appropriate for the educational objectives. The generated story is provided as an e-book, and educators can download it and read it to children. This allows children to learn according to their educational objectives while engaging their interest. For example, nursery school and kindergarten teachers can generate and read stories tailored to each child in their class. This improves the quality of education and deepens children's learning. The picture book generation system generates an optimal story appropriate for a child's age, interests, reading level, and educational objectives and provides it as an e-book.

[0056] A picture book creation system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives information on a child's age, interests, reading level, and educational objectives. For example, an educator can input information about a 5-year-old child who is interested in dinosaurs, has a beginner's reading level, and whose educational objective is to improve scientific knowledge. The generation unit uses a generation AI to analyze the information received by the reception unit and generate an optimal story. The generation AI generates a story tailored to the child's age and interests based on a large amount of data. For example, a story containing scientific knowledge about dinosaurs is generated. This story is written in simple language tailored to the child's reading level. The provision unit provides the story generated by the generation unit as an e-book. Educators can download the generated story as an e-book and read it to children. This allows for learning that meets educational objectives while attracting children's interest. For example, nursery school or kindergarten teachers can generate and read stories tailored to the children in their class. This improves the quality of education and deepens children's learning. As a result, the picture book creation system according to the embodiment can create an optimal story according to the child's age, interests, reading level, and educational purpose, and provide it as an electronic book.

[0057] The generation unit can use a generation AI to generate a story based on the child's age and interests. The generation unit, for example, uses a generation AI to generate a story tailored to the child's age and interests. The generation AI generates a story tailored to the child's age and interests based on a large amount of data. For example, a story including scientific knowledge about dinosaurs is generated. This story is written in simple language tailored to the child's reading level. In this way, the generation AI can be used to generate a story tailored to the child's age and interests. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a story using an AI model for generating stories based on the child's age and interests.

[0058] The generation unit can generate a story including content that meets the educational purpose. For example, the generation unit generates a story including content that meets the educational purpose. The generation AI generates a story including content that meets the educational purpose based on a large amount of data. For example, a story aimed at improving scientific knowledge is generated. This story is written in simple language that meets a child's reading level. This makes it possible to generate a story including content that meets the educational purpose. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can generate a story using an AI model for generating stories that include content that meets the educational purpose.

[0059] The providing unit can provide the generated story as an e-book. The providing unit, for example, provides the generated story as an e-book. The generation AI generates a story tailored to the age and interests of a child based on a large amount of data. The generated story is provided as an e-book, and educators can download it and read it to children. This makes it possible to provide learning according to educational objectives while attracting children's interest. For example, a teacher at a nursery school or kindergarten can generate a story tailored to the children in their class and read it to them. This improves the quality of education and deepens children's learning. This makes it possible to provide the generated story as an e-book. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can provide the story using an AI model for providing the generated story as an e-book.

[0060] The reception unit can input information input by an educator into the generation AI. The reception unit, for example, inputs information input by an educator into the generation AI. The educator inputs information such as the child's age, interests, reading level, and educational objectives. For example, an educator can input information about a 5-year-old child who is interested in dinosaurs, has an elementary reading level, and whose educational objective is to improve scientific knowledge. This information is input into the generation AI, which analyzes it and generates an optimal story. In this way, the information input by the educator can be input into the generation AI. Some or all of the above-described processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input information using an AI model for inputting the information input by the educator into the generation AI.

[0061] The generation unit can generate a story based on multiple data. The generation unit, for example, generates a story based on multiple data. The generation AI generates a story tailored to a child's age and interests based on a large amount of data. For example, a story including scientific knowledge about dinosaurs is generated. This story is written in simple language tailored to a child's reading level. This allows a story to be generated based on multiple data. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can generate a story using an AI model for generating a story based on multiple data.

[0062] The reception unit can estimate the educator's emotions and adjust the information input interface based on the estimated educator's emotions. For example, the reception unit can estimate the educator's emotions and adjust the information input interface based on the estimated educator's emotions. For example, if the educator is stressed, a simple interface can be provided to minimize input steps. Alternatively, if the educator is relaxed, detailed input options can be provided and a customizable input method can be suggested. Alternatively, if the educator is in a hurry, voice input can be prioritized to enable quick information input. This allows the information input interface to be adjusted according to the educator's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be 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 reception unit can be performed using, for example, a generative AI, or can be performed without using a generative AI. For example, the reception unit can estimate the educator's emotions and adjust the interface using an AI model for adjusting the information input interface based on the estimated educator's emotions.

[0063] The reception unit can analyze past input data and suggest an appropriate input method. The reception unit, for example, analyzes past input data and suggests an appropriate input method. For example, it can automatically display information that the educator has frequently input in the past as a candidate. It can also preferentially suggest input methods (voice, text, etc.) that the educator has used in the past. It can also predict and suggest information that will be used in a specific time period based on the educator's past input history. This makes it possible to analyze past input data and suggest an optimal input method. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze past input data and suggest an input method using an AI model for suggesting an appropriate input method.

[0064] The reception unit may add a function to automatically complete input information taking into account the child's current learning progress. The reception unit may add a function to automatically complete input information taking into account the child's current learning progress, for example. For example, the reception unit may automatically complete an appropriate educational objective based on the child's learning progress. The reception unit may also automatically complete an appropriate reading level based on the child's reading level. The reception unit may also automatically complete related interests based on the child's interests. This allows the input information to be automatically completed taking into account the child's learning progress. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit may complete the information using an AI model for automatically completing input information taking into account the child's current learning progress.

[0065] The reception unit can estimate the educator's emotions and prioritize the input information based on the estimated educator's emotions. For example, the reception unit can estimate the educator's emotions and prioritize the input information based on the estimated educator's emotions. For example, if the educator is feeling stressed, the reception unit can prompt the educator to prioritize inputting important information. Also, if the educator is relaxed, the reception unit can prompt the educator to prioritize inputting detailed information. Also, if the educator is in a hurry, the reception unit can prompt the educator to prioritize inputting minimal information. This allows the priority of the input information to be determined according to the educator's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can estimate the educator's emotions and determine the priority of the input information based on the estimated educator's emotions using an AI model for prioritizing the input information.

[0066] The reception unit can prioritize acquiring highly relevant information in consideration of the educator's geographical location information at the time of input. For example, the reception unit prioritizes acquiring highly relevant information in consideration of the educator's geographical location information at the time of input. For example, if the educator is in a specific area, information related to that area can be prioritized. Also, if the educator is in a specific facility, information related to that facility can be prioritized. Also, if the educator is participating in a specific event, information related to that event can be prioritized. In this way, highly relevant information can be prioritized in consideration of the educator's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can acquire information using an AI model for preferentially acquiring highly relevant information in consideration of the educator's geographical location information.

[0067] The reception unit can analyze the educator's social media activity at the time of input and automatically complete the relevant information. The reception unit, for example, analyzes the educator's social media activity at the time of input and automatically completes the relevant information. For example, the reception unit can automatically complete the relevant information based on information shared by the educator on social media. The reception unit can also automatically complete the relevant information based on information about accounts the educator follows on social media. The reception unit can also automatically complete the relevant information based on information about groups the educator participates in on social media. In this way, the educator's social media activity can be analyzed and the relevant information can be automatically completed. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the educator's social media activity and complete the information using an AI model for automatically completing the relevant information.

[0068] The generation unit can estimate the child's emotions and adjust the content and expression of the story based on the estimated child's emotions. For example, the generation unit can estimate the child's emotions and adjust the content and expression of the story based on the estimated child's emotions. For example, if the child is excited, a story with visually stimulating effects can be generated. If the child is relaxed, a story with a calm tone can be generated. If the child is sad, a story including an encouraging message can be generated. This makes it possible to adjust the content and expression of the story according to the child's emotions. 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 these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can estimate the child's emotions and adjust the content and expression of the story based on the estimated child's emotions using an AI model.

[0069] The generation unit can adjust the level of detail of the content based on the importance of the story during generation. For example, the generation unit adjusts the level of detail of the content based on the importance of the story during generation. For example, in the case of a story with an important educational purpose, content including detailed explanations can be generated. Also, in the case of a theme that is of great interest to children, content including detailed descriptions can be generated. Also, in the case of a story that can be read in a short time, concise content can be generated. In this way, the level of detail of the content can be adjusted based on the importance of the story. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the content using an AI model for adjusting the level of detail of the content based on the importance of the story.

[0070] The generation unit can apply different generation algorithms depending on the category of the story during generation. For example, the generation unit can apply different generation algorithms depending on the category of the story during generation. For example, in the case of a story including scientific knowledge, a generation algorithm based on scientific data can be applied. In addition, in the case of a fantasy story, a creative storytelling algorithm can be applied. In addition, in the case of a story with a strong educational purpose, a generation algorithm that emphasizes educational content can be applied. In this way, different generation algorithms can be applied depending on the category of the story. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a story using an AI model for applying different generation algorithms depending on the category of the story.

[0071] The generation unit can estimate the child's emotions and adjust the length of the story based on the estimated child's emotions. For example, the generation unit can estimate the child's emotions and adjust the length of the story based on the estimated child's emotions. For example, if the child is excited, a short and to-the-point story can be generated. If the child is relaxed, a longer story with detailed explanations can be generated. If the child is sad, a short story with an encouraging message can be generated. This allows the length of the story to be adjusted according to the child's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can estimate the child's emotions and adjust the length of the story based on the estimated child's emotions using an AI model.

[0072] The generation unit can determine the priority of content based on the time of generation of the story at the time of generation. The generation unit, for example, determines the priority of content based on the time of generation of the story at the time of generation. For example, in the case of a story with a strong educational purpose, important content can be generated preferentially. Also, in the case of a theme that is of great interest to children, related content can be generated preferentially. Also, in the case of a story related to the seasons, content appropriate to the season can be generated preferentially. In this way, the priority of content can be determined based on the time of generation of the story. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can determine the content using an AI model for determining the priority of content based on the time of generation of the story.

[0073] The generation unit can adjust the order of the content based on the relevance of the story during generation. The generation unit, for example, adjusts the order of the content based on the relevance of the story during generation. For example, content related to the theme of the story can be prioritized. Content related to educational purposes can also be prioritized. Content related to children's interests can also be prioritized. In this way, the order of the content can be adjusted based on the relevance of the story. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the content using an AI model for adjusting the order of the content based on the relevance of the story.

[0074] The providing unit can estimate the educator's emotions and adjust the e-book provision method based on the estimated educator's emotions. For example, the providing unit can estimate the educator's emotions and adjust the e-book provision method based on the estimated educator's emotions. For example, if the educator is stressed, a simple provision method can be provided. If the educator is relaxed, a detailed provision method can be provided. If the educator is in a hurry, a quick provision method can be provided. This makes it possible to adjust the e-book provision method according to the educator's emotions. 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-mentioned processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can estimate the educator's emotions and adjust the e-book provision method based on the estimated educator's emotions using an AI model for adjusting the e-book provision method.

[0075] The providing unit can select an appropriate delivery method by referring to the user's past usage history at the time of provision. For example, the providing unit can select an appropriate delivery method by referring to the user's past usage history at the time of provision. For example, the providing unit can select an optimal delivery method based on delivery methods used by the user in the past. The most efficient delivery method can also be selected from the user's past usage history. The user's past usage history can also be analyzed to select the delivery method that provides the highest satisfaction. This makes it possible to select an optimal delivery method by referring to the user's past usage history. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can select a delivery method by using an AI model for selecting an appropriate delivery method by referring to the user's past usage history.

[0076] The providing unit can provide the content in an appropriate format, taking into account the user's device information, when providing the content. For example, the providing unit can provide the content in an appropriate format, taking into account the user's device information, when providing the content. For example, if the user is using a smartphone, the content can be provided in a format optimized for the smartphone. Furthermore, if the user is using a tablet, the content can be provided in a format optimized for the tablet. Furthermore, if the user is using a personal computer, the content can be provided in a format optimized for the personal computer. This makes it possible to provide the content in an optimal format according to the user's device information. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can select a format using an AI model for providing the content in an appropriate format, taking into account the user's device information.

[0077] The providing unit can estimate the educator's emotions and adjust the display method of the e-book based on the estimated educator's emotions. For example, the providing unit can estimate the educator's emotions and adjust the display method of the e-book based on the estimated educator's emotions. For example, if the educator is stressed, a simple, highly visible display method can be provided. If the educator is relaxed, a display method including detailed information can be provided. If the educator is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to adjust the display method of the e-book according to the educator's emotions. 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-mentioned processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can estimate the educator's emotions and adjust the display method using an AI model for adjusting the display method of the e-book based on the estimated educator's emotions.

[0078] The providing unit can select an appropriate delivery method by taking into account the user's geographical location information when providing information. For example, the providing unit selects an appropriate delivery method by taking into account the user's geographical location information when providing information. For example, if the user is in a specific area, information related to that area can be provided preferentially. Also, if the user is in a specific facility, information related to that facility can be provided preferentially. Also, if the user is participating in a specific event, information related to that event can be provided preferentially. This makes it possible to select an optimal delivery method according to the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can select a delivery method by using an AI model for selecting an appropriate delivery method by taking into account the user's geographical location information.

[0079] The providing unit can analyze the user's social media activity and suggest related content at the time of providing. For example, the providing unit can analyze the user's social media activity and suggest related content at the time of providing. For example, the providing unit can suggest related content based on information shared by the user on social media. Also, the providing unit can suggest related content based on information about accounts the user follows on social media. Also, the providing unit can suggest related content based on information about groups the user participates in on social media. In this way, the user's social media activity can be analyzed and related content can be suggested. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the providing unit can analyze the user's social media activity and suggest content using an AI model for suggesting related content. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, generation unit, and provision 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 reception unit is realized by the reception device 38 of the smart device 14, and an educator inputs information about a child's age, interests, reading level, and educational objectives. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information received from the reception unit using a generation AI to generate an optimal story. The provision unit is realized, for example, by the output device 40 of the smart device 14, and provides the generated story as an e-book. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, generation unit, and provision 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 reception unit is realized by the microphone 238 of the smart glasses 214, through which an educator voice-inputs information such as a child's age, interests, reading level, and educational objectives. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, which analyzes the information received from the reception unit using a generation AI and generates an optimal story. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214, which provides the generated story by voice. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314, and an educator voice-inputs information such as the child's age, interests, reading level, and educational objectives. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information received from the reception unit using a generation AI to generate an optimal story. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314, and provides the generated story as an e-book. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and an educator voice-inputs information such as the child's age, interests, reading level, and educational objectives. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information received from the reception unit using a generation AI to generate an optimal story. The provision unit is realized, for example, by the speaker 240 of the robot 414, and provides the generated story by voice.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The reception unit can analyze the information entered by the educator in real time and provide immediate feedback based on the input. For example, when the educator enters the child's age and interests, it can instantly present relevant educational content and reference materials. It can also display appropriate questions or prompts for additional information based on the information entered by the educator. Furthermore, if the information entered by the educator is incomplete, it can make suggestions for completion. This allows educators to enter information efficiently and receive support for generating the optimal story.

[0082] During the story generation process, the generator can incorporate the latest research and data related to a specific theme or topic selected by the educator. For example, the latest discoveries and scientific knowledge about dinosaurs can be reflected in the story. The generator can also incorporate expert opinions and commentary related to the theme selected by the educator. Furthermore, the generator can evaluate whether the content of the story is aligned with the educational objectives and revise the content as necessary. This improves the quality of the story and maximizes the educational effect desired by the educator.

[0083] The generator can customize the story generation process to suit a child's learning style and preferences. For example, for a child who prefers visual learning, it can generate a story that makes extensive use of illustrations and diagrams. For a child who prefers auditory learning, it can also generate a story that incorporates audio and music. Furthermore, for a child who prefers tactile learning, it can generate a story that includes interactive elements. This makes it possible to provide stories that suit a child's learning style and improve learning effectiveness.

[0084] The providing unit can provide the generated story in multiple formats. For example, it can provide not only e-book format, but also audiobook format and interactive app format. It can also provide the story in printable PDF format depending on the content of the story. Furthermore, the providing unit can automatically select the optimal delivery method depending on the format selected by the educator. This allows educators to use the story in the format that is best suited to the child's learning style and environment.

[0085] The reception unit can support the generation of optimal stories by referring to similar cases in the past based on the information entered by the educator. For example, it can suggest optimal stories by utilizing data on stories previously generated for children of the same age group or interests. It can also improve the content and structure of stories based on past success stories and feedback. Furthermore, the reception unit can suggest approaches to achieve optimal educational objectives from past data based on the information entered by the educator. This allows educators to utilize past knowledge to generate more effective stories.

[0086] The generation unit can estimate the child's emotions and adjust the tempo and rhythm of the story based on the estimated child's emotions. For example, if the child is excited, a fast-paced story can be generated. If the child is relaxed, a story with a slower rhythm can be generated. Furthermore, if the child is lacking in concentration, a story that weaves together short episodes can be generated. In this way, the tempo and rhythm of the story can be adjusted according to the child's emotions, thereby attracting the child's interest.

[0087] The reception unit can estimate the educator's emotions and provide an input assistance function based on the estimated educator's emotions. For example, if the educator is feeling stressed, the input assistance function can be enhanced to provide an easy input method. Also, if the educator is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the educator is in a hurry, voice input and auto-completion functions can be provided with priority. In this way, the input assistance function can be adjusted according to the educator's emotions to support efficient information input.

[0088] The generation unit can estimate the child's emotions and adjust the characters and setting of the story based on the estimated child's emotions. For example, if the child is excited, a story including action scenes and adventurous settings can be generated. If the child is relaxed, a story including calm characters and everyday settings can be generated. Furthermore, if the child is sad, a story including characters with encouraging messages and hopeful settings can be generated. In this way, the characters and setting of the story can be adjusted according to the child's emotions to attract the child's interest.

[0089] The provision unit can estimate the educator's emotions and adjust the timing of story provision based on the estimated educator's emotions. For example, if the educator is feeling stressed, a story can be provided at a time when the educator is able to relax. Also, if the educator is relaxed, a story can be provided immediately. Furthermore, if the educator is in a hurry, a story that can be read in a short time can be provided preferentially. In this way, the timing of story provision can be adjusted according to the educator's emotions, and the story can be used at the optimal timing.

[0090] The provision unit can estimate the educator's emotions and customize the story provision method based on the estimated educator's emotions. For example, if the educator is feeling stressed, a simple and intuitive provision method can be provided. Alternatively, if the educator is relaxed, a provision method including detailed information and options can be provided. Furthermore, if the educator is in a hurry, a provision method that allows quick access can be provided. In this way, the story provision method can be customized according to the educator's emotions, allowing the educator to use the story in an optimal way.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The reception unit accepts information about the child's age, interests, reading level, and educational purpose. For example, an educator can enter information about a 5-year-old child who is interested in dinosaurs, has an elementary reading level, and whose educational purpose is to improve scientific knowledge. Step 2: The generator uses AI to analyze the information received by the reception unit and generate an optimal story. Based on a large amount of data, the AI ​​generates a story tailored to the child's age and interests. For example, a story containing scientific knowledge about dinosaurs is generated. The story is written in simple language to suit the child's reading level. Step 3: The provider provides the story generated by the generator as an e-book. Educators can download the generated story as an e-book and read it to children. This makes it possible to provide learning that meets educational objectives while attracting children's interest.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk that accepts information about the child's age, interests, reading level, and educational goals, A generation unit analyzes the information received by the reception unit and generates an appropriate story, The system comprises a providing unit that provides the story generated by the generation unit as an ebook. A system characterized by:

2. The generation unit Using generative AI, generate stories based on the child's age and interests.

2. The system of claim 1.

3. The generation unit Generate stories that include content tailored to educational purposes.

2. The system of claim 1.

4. The providing unit The generated stories will be provided as ebooks.

2. The system of claim 1.

5. The reception unit The information entered by the educator is input into the generating AI.

2. The system of claim 1.

6. The generation unit Generating a story based on multiple data points 2. The system of claim 1.

7. The reception unit It estimates the educator's emotions and adjusts the information input interface based on the estimated educator's emotions.

2. The system of claim 1.

8. The reception unit We analyze past input data and suggest appropriate input methods.

2. The system of claim 1.

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