system

Abstract

The system according to this embodiment aims to make literary works easier to understand by generating new books based on user choices. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a navigation unit. The reception unit receives the user's selections. The generation unit generates a new book based on the selections received by the reception unit. The navigation unit navigates the book generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to understand literary works due to language, age, and background of the times.

[0005] The system according to the embodiment aims to generate a new book based on user options and make literary works easier to understand.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a navigation unit. The reception unit receives user options. The generation unit generates a new book based on the options received by the reception unit. The navigation unit navigates the book generated by the generation unit.

Effects of the Invention

[0007] The system according to this embodiment can generate new books based on user choices, making literary works easier to understand. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a groundbreaking solution for people who have difficulty reading maps, or for all generations who can read maps but find them difficult to understand, by utilizing a generation AI. This system allows users to input options such as language, age, picture book style, and story progression, and then generate and enjoy a new book. For example, a user inputs options such as language, age, picture book style, and story progression. For instance, if a mother living in the United States wants to read "The Monkey and the Crab" to her 4-year-old son, she can input the following options: Language: Japanese, Reader: 4-year-old boy, Picture book desired, Illustrations: Pokémon-style characters. This information is input into the generation AI. Next, the generation AI analyzes the input information and generates a new book. The generation AI adjusts readability according to the selected language and age, and generates a picture book based on the selected style. The story progression also changes according to the user's choices. For example, midway through the story, options such as "The crab has a rice ball, and the monkey comes along and asks to trade. Trade? ⇒ Yes ⇒ No" are displayed, and the story progresses according to the user's choice. Furthermore, the generated books are Audible compatible, making them enjoyable even for children who cannot read. For example, they can be used in conjunction with devices to provide learning opportunities for children in developing countries. This can improve literacy rates and provide educational opportunities. Through this system, people all over the world can enjoy themselves through classic literature and live better lives. For example, in elderly care facilities such as day care centers, selecting the story creation mode (game) allows for multiple choices that lead to different endings, ensuring that users will not get bored. It also provides a lively and enjoyable experience by engaging both the mind and fingertips. The system can generate and navigate new books based on the user's choices.

[0029] The system according to this embodiment comprises a reception unit, a generation unit, and a navigation unit. The reception unit receives user selections. User selections include, but are not limited to, language, age, picture book style, and story progression. The reception unit receives, for example, selections such as language, age, picture book style, and story progression entered by the user. The generation unit uses a generation AI to generate a new book based on the selections received by the reception unit. The generation unit adjusts readability according to the selected language and age, for example. The generation unit can also generate a picture book based on the selected style. Furthermore, the generation unit can change the story progression according to the user's selection. For example, the generation unit may display a selection in the middle of the story such as, "A crab has a rice ball, and a monkey comes along and asks to trade. Trade? ⇒ Yes ⇒ No," and advance the story according to the user's selection. The navigation unit navigates the book generated by the generation unit. The navigation unit can, for example, make the generated book Audible compatible so that even children who cannot read can enjoy using it. The navigation unit has, for example, a function to read aloud the generated books. The navigation unit also has a function to display the generated books on the device. For example, the navigation unit displays the generated books on the device. This allows the system to generate new books based on user selections and provide navigation. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit inputs the user's choices into the generation AI, and the generation AI generates a new book. Some or all of the above-described processes in the navigation unit may be performed using AI or not. For example, the navigation unit inputs the generated books into the AI, and the AI ​​provides navigation.

[0030] The reception desk accepts user choices. These choices include, but are not limited to, language, age, picture book art style, and story progression. Specifically, the reception desk receives information entered by the user through the interface in real time and stores it in the system's database. When users enter choices, UI elements such as dropdown menus, radio buttons, and text boxes are used to ensure intuitive operation. For example, multiple languages ​​are available as language options, such as English, Japanese, and French, allowing users to select their preferred language. Age options include age groups such as 0-3, 4-6, and 7-9, allowing users to choose their target age group. For picture book art style, options such as classic, modern, and anime are provided, allowing users to choose according to their preference. For story progression, interactive elements are included where the story unfolds differently depending on the user's choices. For example, a character might present a choice midway through the story, and the user's decision to "exchange" or "not exchange" can alter the progression of the narrative. In this way, the reception desk builds a foundation for responding to diverse user needs and providing personalized experiences.

[0031] The generation unit uses a generation AI to generate new books based on the selections received by the reception unit. The generation unit adjusts readability according to the selected language and age, for example. It can also generate picture books based on the selected art style. Furthermore, the generation unit can change the story progression according to the user's choices. Specifically, the generation unit generates text using appropriate vocabulary and grammar based on the language selected by the user. For example, if English is selected, the generation AI constructs the story using English grammar and vocabulary; if Japanese is selected, it uses Japanese grammar and vocabulary. Age-appropriate adjustments are also made; for example, simple words and short sentences are used for preschoolers, while more complex vocabulary and sentence structures are used for elementary school children. Based on the art style selection, the generation AI generates illustrations in different styles. For example, if a classic art style is selected, the generation AI generates hand-drawn style illustrations; if a modern art style is selected, it generates digital art style illustrations. Regarding the story progression, the generation AI generates different scenarios according to the user's choices. For example, in the middle of a story, a choice might be displayed such as, "A crab is holding a rice ball when a monkey comes along and asks to trade. Will you trade? ⇒ Yes ⇒ No," and the story progresses according to the user's choice. In this way, the generation unit creates a personalized picture book based on the user's choices, providing the user with a unique experience.

[0032] The navigation unit navigates the books generated by the generation unit. For example, by making the generated books Audible-compatible, the navigation unit allows even children who cannot read to enjoy them. The navigation unit also has a function to read the generated books aloud. Furthermore, the navigation unit has a function to display the generated books on a device. Specifically, the navigation unit reads the text and illustrations of the generated picture books using speech synthesis technology. This speech synthesis technology generates voices with natural pronunciation and intonation, allowing children to enjoy the stories. In addition, the navigation unit has a function to display the generated picture books on devices such as smartphones and tablets. For example, it is device-compatible, allowing users to view the generated picture books through an app. On the device, page-turning animations and interactive elements are added, allowing users to enjoy a more immersive experience. The navigation unit also saves the generated picture books to the cloud, making them accessible from multiple devices. This allows users to enjoy picture books at home or on the go. Furthermore, the navigation unit records the user's progress, allowing them to resume from where they left off the next time they view the book. This allows the navigation unit to provide the generated picture books in a variety of ways, offering users a convenient and enjoyable experience.

[0033] The generation unit can generate new books using a generative AI. For example, the generation unit generates new books using a generative AI. The generation unit inputs user choices into the generative AI, and the generative AI generates new books. The generative AI generates new books using, for example, a text generation AI (e.g., LLM). The generative AI has learned from a large amount of text data and possesses advanced natural language processing capabilities. The generative AI generates new books based on user choices. This improves the accuracy of generating new books by using a generative AI.

[0034] The generation unit can adjust readability according to the selected language and age. For example, the generation unit can adjust the font size according to the selected language and age. The generation unit can also adjust the line spacing according to the selected language and age. The generation unit can also adjust the difficulty level of words according to the selected language and age. This allows for the provision of more appropriate books by adjusting readability according to the user's choices. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit inputs the user's choices into the AI, and the AI ​​adjusts the readability.

[0035] The generation unit can generate picture books based on the selected art style. For example, the generation unit can generate an anime-style picture book based on the selected art style. The generation unit can also generate a realistic-style picture book based on the selected art style. The generation unit can also generate an abstract-style picture book based on the selected art style. This allows for the provision of more appealing picture books by adjusting the art style according to the user's selection. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit inputs the user's choices into the AI, and the AI ​​adjusts the art style.

[0036] The navigation unit can change the story's progression based on user choices. For example, the navigation unit can display choices in the middle of the story and advance the story according to the user's choice. For example, the navigation unit can display choices such as, "A crab is holding a rice ball, and a monkey comes along and asks to trade. Trade? ⇒ Yes ⇒ No," and advance the story according to the user's choice. This allows for a more interactive experience by changing the story's progression based on user choices. Some or all of the above processing in the navigation unit may be performed using AI, or not. For example, the navigation unit can input the user's choices into the AI, and the AI ​​can change the story's progression.

[0037] The navigation unit is Audible compatible, so even children who cannot read can enjoy using it. The navigation unit has a function to read aloud the generated books. The navigation unit has a function to display the generated books on the device. For example, the navigation unit displays the generated books on the device. This makes it possible for children who cannot read to enjoy using it, thanks to Audible compatibility. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit inputs the generated book into the AI, and the AI ​​reads it aloud.

[0038] The reception desk can analyze the user's past selection history and suggest the most suitable options. For example, the reception desk can automatically display the most suitable options based on the language and age settings the user has previously selected. For example, the reception desk can analyze patterns of options the user has used in the past and prioritize suggesting the most frequently used options. For example, the reception desk can predict and suggest options appropriate for a specific time of day or situation based on the user's past selection history. In this way, the reception desk can suggest the most suitable options by analyzing past selection history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past selection history into the AI, and the AI ​​can suggest the most suitable options.

[0039] The reception desk can filter the options based on the user's current interests when receiving them. For example, the reception desk can prioritize relevant options based on information about books the user has recently read or movies they have watched. For example, the reception desk can analyze the user's social media activity and filter options based on their current interests. For example, the reception desk can suggest relevant options based on events or activities the user is currently participating in. This allows for the provision of more relevant options by filtering options based on current interests. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's current interests into the AI, and the AI ​​can filter the options.

[0040] The reception desk can prioritize presenting highly relevant options when receiving choices, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize presenting options related to that region. For example, if the user is traveling, the reception desk will prioritize presenting options related to their travel destination. For example, if the user is at home, the reception desk will prioritize presenting options related to their home area. In this way, highly relevant options can be provided by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location into the AI, and the AI ​​will prioritize presenting highly relevant options.

[0041] The reception desk can analyze the user's social media activity when receiving a selection request and present relevant options. For example, the reception desk may present relevant options based on posts the user has recently "liked." For example, the reception desk may present relevant options based on the activity of accounts the user follows. For example, the reception desk may present relevant options based on groups or events the user participates in. In this way, relevant options can be provided by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input the user's social media activity into an AI, and the AI ​​may present relevant options.

[0042] The generation unit can adjust the readability of a book according to the selected language and age group. For example, for children's books, the generation unit adjusts readability by using simple language and large fonts. For example, for adult books, the generation unit adjusts readability by using complex language and detailed explanations. For example, for foreign language books, the generation unit avoids technical jargon and uses simple expressions to improve translation quality. This allows for the provision of more appropriate books by adjusting readability according to the selected language and age group. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit inputs user choices into the AI, and the AI ​​adjusts the readability.

[0043] The generation unit can generate picture books based on the selected art style during book creation. For example, if the user selects an anime-style art style, the generation unit will generate a picture book using anime-style characters and backgrounds. For example, if the user selects a realistic art style, the generation unit will generate a picture book using detailed depictions and realistic colors. For example, if the user selects an abstract art style, the generation unit will generate a picture book using abstract shapes and colors. This allows for the provision of more appealing picture books by generating them based on the selected art style. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit inputs the user's choices into the AI, and the AI ​​adjusts the art style.

[0044] The generation unit can determine the generation priority based on the progression of the selected story when generating a book. For example, the generation unit may prioritize generating important scenes from the story selected by the user. For example, the generation unit may generate sequentially according to the progression of the story selected by the user. For example, the generation unit may generate the ending of the story selected by the user first, and then generate the subsequent scenes sequentially. By determining the generation priority based on the progression of the story, a more appropriate book can be provided. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit inputs the user's choices into the AI, and the AI ​​determines the generation priority.

[0045] The generation unit can adjust the generation order based on the selected theme when generating a book. For example, the generation unit may prioritize generating relevant scenes based on the theme selected by the user. For example, the generation unit may adjust the progression order of the story based on the theme selected by the user. For example, the generation unit may highlight specific scenes based on the theme selected by the user. This allows for the provision of a more appropriate book by adjusting the generation order based on the theme. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit may input the user's choices into the AI, and the AI ​​may adjust the generation order.

[0046] The navigation unit can select the optimal navigation method by referring to the user's past selection history during navigation. For example, the navigation unit may suggest the optimal method based on the navigation method the user has previously selected. For example, the navigation unit may prioritize suggesting the most frequently used navigation method from the user's past selection history. For example, the navigation unit may analyze the user's past selection history and suggest a navigation method appropriate to a specific situation. In this way, the optimal navigation method can be provided by referring to the past selection history. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit may input the user's past selection history into the AI, and the AI ​​may select the optimal navigation method.

[0047] The navigation unit can customize navigation methods based on the user's current interests and preferences during navigation. For example, the navigation unit can provide relevant navigation methods based on information about books the user has recently read or movies they have watched. For example, the navigation unit can analyze the user's social media activity and customize navigation methods based on their current interests and preferences. For example, the navigation unit can suggest relevant navigation methods based on events or activities the user is currently participating in. By customizing navigation methods based on current interests and preferences, the navigation unit can provide more relevant navigation. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit can input the user's current interests and preferences into the AI, and the AI ​​can customize the navigation methods.

[0048] The navigation unit can select the optimal navigation method during navigation by taking into account the user's geographical location information. For example, if the user is in a specific region, the navigation unit provides a navigation method related to that region. For example, if the user is traveling, the navigation unit provides a navigation method related to the travel destination. For example, if the user is at home, the navigation unit provides a navigation method related to the area around home. In this way, the optimal navigation method can be provided by taking into account geographical location information. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit inputs the user's geographical location information into the AI, and the AI ​​selects the optimal navigation method.

[0049] The navigation unit can analyze the user's social media activity during navigation and suggest navigation options. For example, the navigation unit may suggest relevant navigation options based on posts the user has recently "liked." For example, the navigation unit may suggest relevant navigation options based on the activity of accounts the user follows. For example, the navigation unit may suggest relevant navigation options based on groups or events the user participates in. In this way, relevant navigation options can be provided by analyzing social media activity. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit may input the user's social media activity into AI, and the AI ​​may suggest navigation options.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The reception desk can suggest the most suitable options by referring to the user's past selection history when receiving user choices. For example, it can automatically display the most suitable options based on the language and age settings the user has previously selected. Furthermore, it can analyze patterns in the choices the user has used in the past and prioritize suggesting the most frequently used options. In this way, by analyzing past selection history, the system can provide the most suitable options for the user. Some or all of the above processing in the reception desk may be performed using AI or not.

[0052] The navigation unit can customize navigation methods based on the user's current interests. For example, it can provide relevant navigation methods based on information about books the user has recently read or movies they have watched. It can also analyze the user's social media activity and customize navigation methods based on their current interests. Furthermore, it can suggest relevant navigation methods based on events and activities the user is currently participating in. By customizing navigation methods based on current interests, it is possible to provide more relevant navigation. Some or all of the above processing in the navigation unit may be performed using AI or not.

[0053] The generation unit can adjust the readability of a book according to the selected language and age group during the book generation process. For example, for children's books, readability can be adjusted by using simple language and large fonts. For adult books, readability can be adjusted by using complex language and detailed explanations. Furthermore, for foreign language books, technical terms can be avoided and simple expressions can be used to improve the quality of the translation. This allows for the provision of more appropriate books by adjusting readability according to the selected language and age group. Some or all of the above processing in the generation unit may be performed using AI or not.

[0054] The reception desk can prioritize presenting highly relevant options by considering the user's geographical location when receiving choices. For example, if the user is in a specific region, options related to that region can be prioritized. Similarly, if the user is traveling, options related to their travel destination can be prioritized. Furthermore, if the user is at home, options related to their home area can be prioritized. This allows for the provision of highly relevant options by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI, or it may be performed without AI.

[0055] The navigation unit can select the optimal navigation method by referring to the user's past selection history during navigation. For example, it can suggest the optimal method based on the navigation method the user has previously selected. It can also prioritize suggesting the most frequently used navigation method based on the user's past selection history. Furthermore, it can analyze the user's past selection history and suggest a navigation method appropriate to a specific situation. In this way, the optimal navigation method can be provided by referring to past selection history. Some or all of the above processing in the navigation unit may be performed using AI, or it may be performed without using AI.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk receives the user's choices. These choices may include, for example, language, age, style of illustration in the picture book, and the way the story progresses. The reception desk accepts these choices entered by the user. Step 2: The generation unit uses a generation AI to generate a new book based on the selections received by the reception unit. The generation unit adjusts readability according to the selected language and age, and generates a picture book based on the selected art style. It can also change the story progression according to the user's selections. For example, it can display choices in the middle of the story and advance the story according to the user's selection. Step 3: The navigation unit navigates the books generated by the generation unit. The navigation unit has functions to read the generated books aloud and to display them on the device. For example, by making the generated books Audible compatible, even children who cannot read can enjoy using them. It also displays the generated books on the device.

[0058] (Example of form 2) The system according to an embodiment of the present invention is a groundbreaking solution for people who have difficulty reading maps, or for all generations who can read maps but find them difficult to understand, by utilizing a generation AI. This system allows users to input options such as language, age, picture book style, and story progression, and then generate and enjoy a new book. For example, a user inputs options such as language, age, picture book style, and story progression. For instance, if a mother living in the United States wants to read "The Monkey and the Crab" to her 4-year-old son, she can input the following options: Language: Japanese, Reader: 4-year-old boy, Picture book desired, Illustrations: Pokémon-style characters. This information is input into the generation AI. Next, the generation AI analyzes the input information and generates a new book. The generation AI adjusts readability according to the selected language and age, and generates a picture book based on the selected style. The story progression also changes according to the user's choices. For example, midway through the story, options such as "The crab has a rice ball, and the monkey comes along and asks to trade. Trade? ⇒ Yes ⇒ No" are displayed, and the story progresses according to the user's choice. Furthermore, the generated books are Audible compatible, making them enjoyable even for children who cannot read. For example, they can be used in conjunction with devices to provide learning opportunities for children in developing countries. This can improve literacy rates and provide educational opportunities. Through this system, people all over the world can enjoy themselves through classic literature and live better lives. For example, in elderly care facilities such as day care centers, selecting the story creation mode (game) allows for multiple choices that lead to different endings, ensuring that users will not get bored. It also provides a lively and enjoyable experience by engaging both the mind and fingertips. The system can generate and navigate new books based on the user's choices.

[0059] The system according to this embodiment comprises a reception unit, a generation unit, and a navigation unit. The reception unit receives user selections. User selections include, but are not limited to, language, age, picture book style, and story progression. The reception unit receives, for example, selections such as language, age, picture book style, and story progression entered by the user. The generation unit uses a generation AI to generate a new book based on the selections received by the reception unit. The generation unit adjusts readability according to the selected language and age, for example. The generation unit can also generate a picture book based on the selected style. Furthermore, the generation unit can change the story progression according to the user's selection. For example, the generation unit may display a selection in the middle of the story such as, "A crab has a rice ball, and a monkey comes along and asks to trade. Trade? ⇒ Yes ⇒ No," and advance the story according to the user's selection. The navigation unit navigates the book generated by the generation unit. The navigation unit can, for example, make the generated book Audible compatible so that even children who cannot read can enjoy using it. The navigation unit has, for example, a function to read aloud the generated books. The navigation unit also has a function to display the generated books on the device. For example, the navigation unit displays the generated books on the device. This allows the system to generate new books based on user selections and provide navigation. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit inputs the user's choices into the generation AI, and the generation AI generates a new book. Some or all of the above-described processes in the navigation unit may be performed using AI or not. For example, the navigation unit inputs the generated books into the AI, and the AI ​​provides navigation.

[0060] The reception desk accepts user choices. These choices include, but are not limited to, language, age, picture book art style, and story progression. Specifically, the reception desk receives information entered by the user through the interface in real time and stores it in the system's database. When users enter choices, UI elements such as dropdown menus, radio buttons, and text boxes are used to ensure intuitive operation. For example, multiple languages ​​are available as language options, such as English, Japanese, and French, allowing users to select their preferred language. Age options include age groups such as 0-3, 4-6, and 7-9, allowing users to choose their target age group. For picture book art style, options such as classic, modern, and anime are provided, allowing users to choose according to their preference. For story progression, interactive elements are included where the story unfolds differently depending on the user's choices. For example, a character might present a choice midway through the story, and the user's decision to "exchange" or "not exchange" can alter the progression of the narrative. In this way, the reception desk builds a foundation for responding to diverse user needs and providing personalized experiences.

[0061] The generation unit uses a generation AI to generate new books based on the selections received by the reception unit. The generation unit adjusts readability according to the selected language and age, for example. It can also generate picture books based on the selected art style. Furthermore, the generation unit can change the story progression according to the user's choices. Specifically, the generation unit generates text using appropriate vocabulary and grammar based on the language selected by the user. For example, if English is selected, the generation AI constructs the story using English grammar and vocabulary; if Japanese is selected, it uses Japanese grammar and vocabulary. Age-appropriate adjustments are also made; for example, simple words and short sentences are used for preschoolers, while more complex vocabulary and sentence structures are used for elementary school children. Based on the art style selection, the generation AI generates illustrations in different styles. For example, if a classic art style is selected, the generation AI generates hand-drawn style illustrations; if a modern art style is selected, it generates digital art style illustrations. Regarding the story progression, the generation AI generates different scenarios according to the user's choices. For example, in the middle of a story, a choice might be displayed such as, "A crab is holding a rice ball when a monkey comes along and asks to trade. Will you trade? ⇒ Yes ⇒ No," and the story progresses according to the user's choice. In this way, the generation unit creates a personalized picture book based on the user's choices, providing the user with a unique experience.

[0062] The navigation unit navigates the books generated by the generation unit. For example, by making the generated books Audible-compatible, the navigation unit allows even children who cannot read to enjoy them. The navigation unit also has a function to read the generated books aloud. Furthermore, the navigation unit has a function to display the generated books on a device. Specifically, the navigation unit reads the text and illustrations of the generated picture books using speech synthesis technology. This speech synthesis technology generates voices with natural pronunciation and intonation, allowing children to enjoy the stories. In addition, the navigation unit has a function to display the generated picture books on devices such as smartphones and tablets. For example, it is device-compatible, allowing users to view the generated picture books through an app. On the device, page-turning animations and interactive elements are added, allowing users to enjoy a more immersive experience. The navigation unit also saves the generated picture books to the cloud, making them accessible from multiple devices. This allows users to enjoy picture books at home or on the go. Furthermore, the navigation unit records the user's progress, allowing them to resume from where they left off the next time they view the book. This allows the navigation unit to provide the generated picture books in a variety of ways, offering users a convenient and enjoyable experience.

[0063] The generation unit can generate new books using a generative AI. For example, the generation unit generates new books using a generative AI. The generation unit inputs user choices into the generative AI, and the generative AI generates new books. The generative AI generates new books using, for example, a text generation AI (e.g., LLM). The generative AI has learned from a large amount of text data and possesses advanced natural language processing capabilities. The generative AI generates new books based on user choices. This improves the accuracy of generating new books by using a generative AI.

[0064] The generation unit can adjust readability according to the selected language and age. For example, the generation unit can adjust the font size according to the selected language and age. The generation unit can also adjust the line spacing according to the selected language and age. The generation unit can also adjust the difficulty level of words according to the selected language and age. This allows for the provision of more appropriate books by adjusting readability according to the user's choices. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit inputs the user's choices into the AI, and the AI ​​adjusts the readability.

[0065] The generation unit can generate picture books based on the selected art style. For example, the generation unit can generate an anime-style picture book based on the selected art style. The generation unit can also generate a realistic-style picture book based on the selected art style. The generation unit can also generate an abstract-style picture book based on the selected art style. This allows for the provision of more appealing picture books by adjusting the art style according to the user's selection. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit inputs the user's choices into the AI, and the AI ​​adjusts the art style.

[0066] The navigation unit can change the story's progression based on user choices. For example, the navigation unit can display choices in the middle of the story and advance the story according to the user's choice. For example, the navigation unit can display choices such as, "A crab is holding a rice ball, and a monkey comes along and asks to trade. Trade? ⇒ Yes ⇒ No," and advance the story according to the user's choice. This allows for a more interactive experience by changing the story's progression based on user choices. Some or all of the above processing in the navigation unit may be performed using AI, or not. For example, the navigation unit can input the user's choices into the AI, and the AI ​​can change the story's progression.

[0067] The navigation unit is Audible compatible, so even children who cannot read can enjoy using it. The navigation unit has a function to read aloud the generated books. The navigation unit has a function to display the generated books on the device. For example, the navigation unit displays the generated books on the device. This makes it possible for children who cannot read to enjoy using it, thanks to Audible compatibility. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit inputs the generated book into the AI, and the AI ​​reads it aloud.

[0068] The reception desk can estimate the user's emotions and adjust how options are presented based on the estimated emotions. For example, if the user is stressed, the reception desk may provide a simple interface and minimize the number of options. If the user is relaxed, for example, the reception desk may provide more detailed options and increase the number of customizable options. If the user is in a hurry, for example, the reception desk may prioritize voice input and quickly present options. This allows for the provision of more appropriate options by adjusting how options are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input user emotion data into an AI, which may estimate the emotions and adjust how options are presented.

[0069] The reception desk can analyze the user's past selection history and suggest the most suitable options. For example, the reception desk can automatically display the most suitable options based on the language and age settings the user has previously selected. For example, the reception desk can analyze patterns of options the user has used in the past and prioritize suggesting the most frequently used options. For example, the reception desk can predict and suggest options appropriate for a specific time of day or situation based on the user's past selection history. In this way, the reception desk can suggest the most suitable options by analyzing past selection history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past selection history into the AI, and the AI ​​can suggest the most suitable options.

[0070] The reception desk can filter the options based on the user's current interests when receiving them. For example, the reception desk can prioritize relevant options based on information about books the user has recently read or movies they have watched. For example, the reception desk can analyze the user's social media activity and filter options based on their current interests. For example, the reception desk can suggest relevant options based on events or activities the user is currently participating in. This allows for the provision of more relevant options by filtering options based on current interests. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's current interests into the AI, and the AI ​​can filter the options.

[0071] The reception desk can estimate the user's emotions and determine the priority of options based on the estimated emotions. For example, if the user is excited, the reception desk will prioritize visually stimulating options. If the user is relaxed, the reception desk will prioritize calming options. If the user is tired, the reception desk will prioritize simple and highly visible options. This allows for the provision of more appropriate options by prioritizing options according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs user emotion data into the AI, the AI ​​estimates the emotions, and determines the priority of options.

[0072] The reception desk can prioritize presenting highly relevant options when receiving choices, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize presenting options related to that region. For example, if the user is traveling, the reception desk will prioritize presenting options related to their travel destination. For example, if the user is at home, the reception desk will prioritize presenting options related to their home area. In this way, highly relevant options can be provided by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location into the AI, and the AI ​​will prioritize presenting highly relevant options.

[0073] The reception desk can analyze the user's social media activity when receiving a selection request and present relevant options. For example, the reception desk may present relevant options based on posts the user has recently "liked." For example, the reception desk may present relevant options based on the activity of accounts the user follows. For example, the reception desk may present relevant options based on groups or events the user participates in. In this way, relevant options can be provided by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input the user's social media activity into an AI, and the AI ​​may present relevant options.

[0074] The generation unit can estimate the user's emotions and adjust the presentation of the generated book based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a book that progresses at a leisurely pace. If the user is in a hurry, the generation unit will generate a short, concise book. If the user is excited, the generation unit will generate a book with visually stimulating effects. This allows for the provision of more appropriate books by adjusting the presentation of the book according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit inputs user emotion data into the AI, the AI ​​estimates the emotions, and adjusts the presentation of the book.

[0075] The generation unit can adjust the readability of a book according to the selected language and age group. For example, for children's books, the generation unit adjusts readability by using simple language and large fonts. For example, for adult books, the generation unit adjusts readability by using complex language and detailed explanations. For example, for foreign language books, the generation unit avoids technical jargon and uses simple expressions to improve translation quality. This allows for the provision of more appropriate books by adjusting readability according to the selected language and age group. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit inputs user choices into the AI, and the AI ​​adjusts the readability.

[0076] The generation unit can generate picture books based on the selected art style during book creation. For example, if the user selects an anime-style art style, the generation unit will generate a picture book using anime-style characters and backgrounds. For example, if the user selects a realistic art style, the generation unit will generate a picture book using detailed depictions and realistic colors. For example, if the user selects an abstract art style, the generation unit will generate a picture book using abstract shapes and colors. This allows for the provision of more appealing picture books by generating them based on the selected art style. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit inputs the user's choices into the AI, and the AI ​​adjusts the art style.

[0077] The generation unit can estimate the user's emotions and adjust the length of the generated book based on the estimated emotions. For example, if the user is in a hurry, the generation unit will generate a short, concise book. If the user is relaxed, the generation unit will generate a longer book with detailed explanations. If the user is excited, the generation unit will generate a book with visually stimulating effects. By adjusting the length of the book according to the user's emotions, a more appropriate book can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit inputs user emotion data into the AI, the AI ​​estimates the emotions, and adjusts the length of the book.

[0078] The generation unit can determine the generation priority based on the progression of the selected story when generating a book. For example, the generation unit may prioritize generating important scenes from the story selected by the user. For example, the generation unit may generate sequentially according to the progression of the story selected by the user. For example, the generation unit may generate the ending of the story selected by the user first, and then generate the subsequent scenes sequentially. By determining the generation priority based on the progression of the story, a more appropriate book can be provided. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit inputs the user's choices into the AI, and the AI ​​determines the generation priority.

[0079] The generation unit can adjust the generation order based on the selected theme when generating a book. For example, the generation unit may prioritize generating relevant scenes based on the theme selected by the user. For example, the generation unit may adjust the progression order of the story based on the theme selected by the user. For example, the generation unit may highlight specific scenes based on the theme selected by the user. This allows for the provision of a more appropriate book by adjusting the generation order based on the theme. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit may input the user's choices into the AI, and the AI ​​may adjust the generation order.

[0080] The navigation unit can estimate the user's emotions and adjust the navigation method based on the estimated emotions. For example, if the user is tense, the navigation unit provides a simple and highly visible navigation method. For example, if the user is relaxed, the navigation unit provides a navigation method that includes detailed information. For example, if the user is in a hurry, the navigation unit provides a concise navigation method. By adjusting the navigation method according to the user's emotions, more appropriate navigation can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the navigation unit may be performed using AI or not using AI. For example, the navigation unit inputs user emotion data into the AI, the AI ​​estimates the emotions, and adjusts the navigation method.

[0081] The navigation unit can select the optimal navigation method by referring to the user's past selection history during navigation. For example, the navigation unit may suggest the optimal method based on the navigation method the user has previously selected. For example, the navigation unit may prioritize suggesting the most frequently used navigation method from the user's past selection history. For example, the navigation unit may analyze the user's past selection history and suggest a navigation method appropriate to a specific situation. In this way, the optimal navigation method can be provided by referring to the past selection history. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit may input the user's past selection history into the AI, and the AI ​​may select the optimal navigation method.

[0082] The navigation unit can customize navigation methods based on the user's current interests and preferences during navigation. For example, the navigation unit can provide relevant navigation methods based on information about books the user has recently read or movies they have watched. For example, the navigation unit can analyze the user's social media activity and customize navigation methods based on their current interests and preferences. For example, the navigation unit can suggest relevant navigation methods based on events or activities the user is currently participating in. By customizing navigation methods based on current interests and preferences, the navigation unit can provide more relevant navigation. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit can input the user's current interests and preferences into the AI, and the AI ​​can customize the navigation methods.

[0083] The navigation unit can estimate the user's emotions and determine navigation priorities based on the estimated emotions. For example, if the user is excited, the navigation unit will prioritize providing visually stimulating navigation methods. For example, if the user is relaxed, the navigation unit will prioritize providing calming navigation methods. For example, if the user is tired, the navigation unit will prioritize providing simple and highly visible navigation methods. This allows for more appropriate navigation by determining navigation priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit inputs user emotion data into the AI, the AI ​​estimates the emotions, and determines navigation priorities.

[0084] The navigation unit can select the optimal navigation method during navigation by taking into account the user's geographical location information. For example, if the user is in a specific region, the navigation unit provides a navigation method related to that region. For example, if the user is traveling, the navigation unit provides a navigation method related to the travel destination. For example, if the user is at home, the navigation unit provides a navigation method related to the area around home. In this way, the optimal navigation method can be provided by taking into account geographical location information. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit inputs the user's geographical location information into the AI, and the AI ​​selects the optimal navigation method.

[0085] The navigation unit can analyze the user's social media activity during navigation and suggest navigation options. For example, the navigation unit may suggest relevant navigation options based on posts the user has recently "liked." For example, the navigation unit may suggest relevant navigation options based on the activity of accounts the user follows. For example, the navigation unit may suggest relevant navigation options based on groups or events the user participates in. In this way, relevant navigation options can be provided by analyzing social media activity. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit may input the user's social media activity into AI, and the AI ​​may suggest navigation options.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The reception desk can suggest the most suitable options by referring to the user's past selection history when receiving user choices. For example, it can automatically display the most suitable options based on the language and age settings the user has previously selected. Furthermore, it can analyze patterns in the choices the user has used in the past and prioritize suggesting the most frequently used options. In this way, by analyzing past selection history, the system can provide the most suitable options for the user. Some or all of the above processing in the reception desk may be performed using AI or not.

[0088] The generation unit can estimate the user's emotions and adjust the way the generated book is presented based on those emotions. For example, if the user is relaxed, it can generate a book that progresses at a leisurely pace. If the user is in a hurry, it can generate a short, concise book. Furthermore, if the user is excited, it can generate a book with visually stimulating effects. By adjusting the way the book is presented according to the user's emotions, a more appropriate book can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. Some or all of the above processing in the generation unit may be performed using AI or not.

[0089] The navigation unit can customize navigation methods based on the user's current interests. For example, it can provide relevant navigation methods based on information about books the user has recently read or movies they have watched. It can also analyze the user's social media activity and customize navigation methods based on their current interests. Furthermore, it can suggest relevant navigation methods based on events and activities the user is currently participating in. By customizing navigation methods based on current interests, it is possible to provide more relevant navigation. Some or all of the above processing in the navigation unit may be performed using AI or not.

[0090] The reception desk can estimate the user's emotions and adjust how options are presented based on those emotions. For example, if the user is stressed, a simple interface can be provided and the number of options minimized. If the user is relaxed, more detailed options can be provided and the number of customizable options can be increased. Furthermore, if the user is in a hurry, voice input can be prioritized and options can be presented quickly. This allows for the provision of more appropriate options by adjusting how options are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above processing in the reception desk may be performed using AI or not.

[0091] The generation unit can adjust the readability of a book according to the selected language and age group during the book generation process. For example, for children's books, readability can be adjusted by using simple language and large fonts. For adult books, readability can be adjusted by using complex language and detailed explanations. Furthermore, for foreign language books, technical terms can be avoided and simple expressions can be used to improve the quality of the translation. This allows for the provision of more appropriate books by adjusting readability according to the selected language and age group. Some or all of the above processing in the generation unit may be performed using AI or not.

[0092] The navigation unit can estimate the user's emotions and adjust the navigation method based on the estimated emotions. For example, if the user is nervous, it can provide a simple and highly visible navigation method. If the user is relaxed, it can provide a navigation method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a navigation method that gets straight to the point. In this way, by adjusting the navigation method according to the user's emotions, more appropriate navigation can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Some or all of the above processing in the navigation unit may be performed using AI or not using AI.

[0093] The reception desk can prioritize presenting highly relevant options by considering the user's geographical location when receiving choices. For example, if the user is in a specific region, options related to that region can be prioritized. Similarly, if the user is traveling, options related to their travel destination can be prioritized. Furthermore, if the user is at home, options related to their home area can be prioritized. This allows for the provision of highly relevant options by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI, or it may be performed without AI.

[0094] The generation unit can estimate the user's emotions and adjust the length of the generated book based on the estimated emotions. For example, if the user is in a hurry, a short, concise book can be generated. If the user is relaxed, a longer book with detailed explanations can be generated. Furthermore, if the user is excited, a book with visually stimulating effects can be generated. This allows for the provision of more appropriate books by adjusting the length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. Some or all of the above processing in the generation unit may be performed using AI or not.

[0095] The navigation unit can select the optimal navigation method by referring to the user's past selection history during navigation. For example, it can suggest the optimal method based on the navigation method the user has previously selected. It can also prioritize suggesting the most frequently used navigation method based on the user's past selection history. Furthermore, it can analyze the user's past selection history and suggest a navigation method appropriate to a specific situation. In this way, the optimal navigation method can be provided by referring to past selection history. Some or all of the above processing in the navigation unit may be performed using AI, or it may be performed without using AI.

[0096] The navigation unit can estimate the user's emotions and determine navigation priorities based on those emotions. For example, if the user is excited, visually stimulating navigation methods can be prioritized. If the user is relaxed, calming navigation methods can be prioritized. Furthermore, if the user is tired, simple and easily visible navigation methods can be prioritized. By prioritizing navigation according to the user's emotions, more appropriate navigation can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above-described processing in the navigation unit may be performed using AI or not.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The reception desk receives the user's choices. These choices may include, for example, language, age, style of illustration in the picture book, and the way the story progresses. The reception desk accepts these choices entered by the user. Step 2: The generation unit uses a generation AI to generate a new book based on the selections received by the reception unit. The generation unit adjusts readability according to the selected language and age, and generates a picture book based on the selected art style. It can also change the story progression according to the user's selections. For example, it can display choices in the middle of the story and advance the story according to the user's selection. Step 3: The navigation unit navigates the books generated by the generation unit. The navigation unit has functions to read the generated books aloud and to display them on the device. For example, by making the generated books Audible compatible, even children who cannot read can enjoy using them. It also displays the generated books on the device.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the reception unit, generation unit, and navigation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and accepts the user's selection. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a new book using a generation AI. The navigation unit is implemented, for example, by the output device 40 of the smart device 14 and has the function of reading the generated book aloud. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the reception unit, generation unit, and navigation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and accepts the user's selection. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates a new book using a generation AI. The navigation unit is implemented by the speaker 240 of the smart glasses 214 and has the function of reading the generated book aloud. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the reception unit, generation unit, and navigation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and accepts the user's selection. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a new book using a generation AI. The navigation unit is implemented by the speaker 240 of the headset terminal 314 and has the function of reading the generated book aloud. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the reception unit, generation unit, and navigation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and accepts the user's selection. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a new book using a generation AI. The navigation unit is implemented by, for example, the speaker 240 of the robot 414 and has the function of reading the generated book aloud. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A reception desk that accepts user choices, A generation unit that generates a new book based on the selections received by the reception unit, The system includes a navigation unit that navigates the books generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Generate new books using a generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Adjust readability according to the selected language and age. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate a picture book based on the selected art style. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned navigation unit is The story's progression changes depending on the user's choices. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned navigation unit is Because it's Audible compatible, even children who can't read can enjoy using it. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts how choices are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past selection history and suggests the optimal choice. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving options, filtering is performed based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of choices based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving options, the system prioritizes presenting the most relevant options by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving a selection request, the system analyzes the user's social media activity and presents relevant options. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the way the book is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a book, readability is adjusted according to the selected language and age group. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating a book, create a picture book based on the selected art style. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the generated book based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating a book, the generation priority is determined based on the progress of the selected story. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating books, the generation order is adjusted based on the selected theme. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned navigation unit is It estimates the user's emotions and adjusts the navigation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned navigation unit is During navigation, the system selects the optimal navigation method by referring to the user's past selection history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned navigation unit is During navigation, the navigation method is customized based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned navigation unit is It estimates the user's emotions and determines navigation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned navigation unit is During navigation, the system selects the optimal navigation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned navigation unit is During navigation, the system analyzes the user's social media activity and suggests navigation methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts user choices, A generation unit that generates a new book based on the selections received by the reception unit, The system includes a navigation unit that navigates the books generated by the generation unit. A system characterized by the following features.

2. The generating unit is Generating new books using AI. The system according to feature 1.

3. The generating unit is Adjust readability according to the selected language and age. The system according to feature 1.

4. The generating unit is Generate a picture book based on the selected art style. The system according to feature 1.

5. The aforementioned navigation unit is The story's progression changes depending on the user's choices. The system according to feature 1.

6. The aforementioned navigation unit is Because it's Audible compatible, even children who can't read can enjoy using it. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts how choices are presented based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past selection history and suggests the optimal choice. The system according to feature 1.

9. The aforementioned reception unit is When receiving options, filtering is performed based on the user's current interests and preferences. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of choices based on the estimated user emotions. The system according to feature 1.

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