System

Abstract

An object of the system according to the embodiment is to keep children interested in reading.SOLUTION: A system includes a reception unit, a generation unit, and a navigation unit. The reception unit selects a book that the user wants to read. The generation unit analyzes the content of the book selected by the reception unit and generates a virtual space. The navigation unit performs navigation in the virtual space generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult for children to maintain their interest in reading.

[0005] The system according to the embodiment aims to keep children interested in reading. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a navigation unit. The reception unit selects a book that a user wants to read. The generation unit analyzes the content of the book selected by the reception unit and generates a virtual space. The navigation unit navigates within the virtual space generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can keep children interested in reading. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention utilizes generative AI to revolutionize children's reading experiences. This system combines virtual reality technology and generative AI to provide a virtual library. In this library, the world of a story selected by a child is recreated in real time in the virtual space. For example, after reading a particular story, the user can explore that story's world. This experience transforms reading from a mere activity into an adventure, fostering children's imagination and interest in reading. This allows the system to significantly transform children's reading experience. For example, children can enjoy reading while feeling as if they are the protagonist of the story. Furthermore, through the experience in the virtual space, children can gain a deeper understanding of the story's content and maximize the enjoyment of reading.

[0029] A reading experience system according to an embodiment includes a reception unit, a generation unit, and a navigation unit. The reception unit selects a book the user wants to read. Examples of books the user wants to read include, but are not limited to, novels, non-fiction, and technical books. The reception unit allows the user to, for example, search bookshelves in a virtual space and select a book of interest. The generation unit uses a generation AI to analyze the content of the book selected by the reception unit and generate a virtual space. The generation AI analyzes the content of the book using, for example, a natural language processing model or a machine learning algorithm and generates the virtual space. The generation unit generates the virtual space based on, for example, a specific story. The navigation unit navigates within the virtual space generated by the generation unit. The navigation unit allows, for example, the user to interact with characters in the story and participate in events occurring in the story. The navigation unit can also navigate based on, for example, the orientation of the user's smartphone. The navigation unit can also display videos based on the user's walking speed. As a result, the reading experience system according to an embodiment generates a virtual space based on the content of a book selected by the user and navigates through it, thereby improving the reading experience.

[0030] The generation unit can analyze the contents of the book using a generation AI and generate a virtual space. Examples of the generation AI include, but are not limited to, natural language processing models and machine learning algorithms. For example, the generation unit can analyze the contents of the book using a generation AI and generate a virtual space based on the contents. For example, the generation unit can also generate a virtual space based on a specific story. This improves the accuracy of generating the virtual space by using the generation AI. Some or all of the above-mentioned processing in the generation unit can be performed using, or without, the generation AI. For example, the generation unit can input the contents of the book into the generation AI and cause the generation AI to generate the virtual space.

[0031] The navigation unit allows the user to interact with characters in the story and participate in events that occur within the story. For example, the navigation unit allows the user to interact with characters in the story. The interaction may be performed by, for example, voice interaction or text interaction, but is not limited to these examples. The navigation unit also allows the user to participate in events that occur within the story. For example, events include, but are not limited to, events related to the progression of the story and events based on user selections. This allows the user to enjoy reading while deeply engaging in the world of the story. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit may input user selections into AI and have the AI ​​execute the interaction and progress of events.

[0032] The generation unit can generate a virtual space based on a specific story. Examples of specific stories include, but are not limited to, works by a specific author or stories of a specific genre. The generation unit can generate a virtual space based on, for example, a specific story. The generation unit can also analyze the content of the story and generate a virtual space based on that content. This allows the world of the story to be realistically reproduced by generating a virtual space based on a specific story. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the content of the specific story into the generation AI and have the generation AI generate the virtual space.

[0033] The navigation unit can perform navigation according to the orientation of the user's smartphone. The orientation of the smartphone can be detected using, for example, a gyro sensor or an acceleration sensor, but is not limited to such examples. The navigation unit can perform navigation according to, for example, the orientation of the user's smartphone. The navigation unit can change the viewpoint in the virtual space according to, for example, the orientation of the smartphone. This allows for more intuitive operation by performing navigation according to the orientation of the user's smartphone. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input smartphone orientation data into AI and have the AI ​​adjust the navigation.

[0034] The navigation unit can display videos in accordance with the user's walking speed. The walking speed can be detected using, for example, a pedometer or GPS data, but is not limited to these examples. The navigation unit can display videos in accordance with the user's walking speed, for example. The navigation unit can slow down the playback speed of the video when the user is walking slowly. The navigation unit can also speed up the playback speed of the video when the user is walking fast. Furthermore, the navigation unit can pause playback of the video when the user stops and resume it when the user starts walking again. This provides a more natural experience by displaying videos in accordance with the user's walking speed. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input walking speed data into AI and have the AI ​​adjust the playback speed of the video.

[0035] The reception unit can analyze the user's past reading history and recommend appropriate books. Reading history includes, for example, a list of books read in the past and reading time, but is not limited to these examples. The reception unit, for example, recommends related books based on the genre or theme of books read in the past by the user. The reception unit can also recommend new works by authors of books that the user has previously rated highly. The reception unit can also recommend unread series works from the user's reading history. In this way, by analyzing the past reading history, it is possible to recommend the most suitable book for the user. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's reading history data into AI and have the AI ​​recommend appropriate books.

[0036] When selecting a book, the reception unit can filter based on the user's current interests and concerns. Interests and concerns include, but are not limited to, survey results and past behavioral data. The reception unit, for example, filters related books based on keywords recently searched by the user. The reception unit can also filter books related to events or activities the user recently participated in. The reception unit can also filter related books based on videos or movies the user recently watched. By filtering books based on the user's current interests and concerns, more appropriate books can be provided. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's interest and concern data into AI and have the AI ​​perform the filtering.

[0037] When selecting a book, the reception unit can provide an appropriate selection means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, when a user uses voice input, the reception unit allows the user to input the book title and author name using voice recognition technology. For example, when a user uses text input, the reception unit can also provide keyword search or category search. Furthermore, when a user uses image input, the reception unit can also allow the user to upload a book cover image and search for similar books. This provides an optimal selection means depending on the user's input method, making the system easier to use. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input voice data or image data into AI and have the AI ​​provide an appropriate selection means.

[0038] When selecting a book, the reception unit can prioritize displaying highly relevant books based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. For example, if the user is in a specific area, the reception unit can prioritize displaying books related to that area. For example, if the user is traveling, the reception unit can also prioritize displaying books related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can also prioritize displaying books related to the event. In this way, highly relevant books can be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's location information data into AI and cause the AI ​​to display highly relevant books.

[0039] When selecting a book, the reception unit can analyze the user's social media activity and recommend related books. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the reception unit can recommend new books by authors the user follows on social media. For example, the reception unit can also recommend books related to articles or posts the user has shared on social media. The reception unit can also display books recommended by the user's friends on social media. In this way, related books can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's social media data into AI and have the AI ​​recommend related books.

[0040] The reception unit can customize the selection method by reflecting the user's past feedback when selecting a book. Feedback includes, but is not limited to, user reviews and rating scores. For example, the reception unit can prioritize displaying genres of books that the user has previously rated highly. For example, the reception unit can also exclude genres of books that the user has previously rated poorly. The reception unit can also adjust the recommendation algorithm based on the user's feedback to suggest optimal books. This allows for more appropriate books to be provided by reflecting the user's past feedback. Some or all of the above-described processing by the reception unit can be performed using, or without, AI. For example, the reception unit can input user feedback data into AI and have the AI ​​customize the selection method.

[0041] When generating the virtual space, the generation unit can adjust the level of detail based on important scenes in the story. Important scenes include, but are not limited to, the climax and major events of the story. For example, the generation unit can use detailed graphics and effects to enhance the sense of realism in the climax scene of the story. For example, the generation unit can draw the user in with simple graphics in the introduction of the story. The generation unit can also provide a memorable experience for the user by adding moving effects in the conclusion of the story. In this way, a realistic experience can be provided by adjusting the level of detail based on important scenes in the story. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data of important scenes in the story into the generation AI and have the generation AI adjust the level of detail.

[0042] When generating a virtual space, the generation unit can apply different generation algorithms depending on the genre of the story. Genres include, but are not limited to, fantasy, mystery, and science fiction. For example, in the case of a fantasy story, the generation unit can apply a generation algorithm that emphasizes magical and fantastical elements. For example, in the case of a science fiction story, the generation unit can apply a generation algorithm that emphasizes futuristic designs and technology. Furthermore, in the case of a mystery story, the generation unit can apply a generation algorithm that emphasizes dark colors and an enigmatic atmosphere. By applying different generation algorithms depending on the genre of the story, a more appropriate virtual space can be provided. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input data on the genre of the story into the generation AI and cause the generation AI to apply the generation algorithm.

[0043] When generating a virtual space, the generation unit can improve the accuracy of the generation by referring to the user's past experiences. Past experiences include, but are not limited to, past usage history of the virtual space and user feedback. For example, the generation unit generates a space that reflects the user's preferences and interests based on data on virtual spaces the user has experienced in the past. For example, the generation unit can generate a space by incorporating elements of virtual spaces that the user has previously rated highly. The generation unit can also analyze the user's past experience data and generate an optimal virtual space. This makes it possible to provide a more appropriate virtual space by referring to the user's past experiences. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past experience data into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0044] When generating a virtual space, the generation unit can determine the generation priority based on the publication date of the story. Publication dates include, but are not limited to, publication dates and release dates. For example, the generation unit can prioritize generating a virtual space for a new story. For example, the generation unit can also prioritize generating a virtual space for a popular story. The generation unit can also prioritize generating a virtual space for a story that is frequently requested by users. By determining the generation priority based on the publication date of the story, the latest stories and popular stories can be provided preferentially. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input data on the publication date of the story into the generation AI and have the generation AI determine the generation priority.

[0045] When generating a virtual space, the generation unit can adjust the order of generation based on the relevance of the stories. Examples of relevance include, but are not limited to, sequels to a story or works by the same author. For example, if there is a first and second part of a story, the generation unit can generate the first part first and the second part later. For example, if there is a series of works in a story, the generation unit can generate them in the order of the series. Furthermore, if there is a spin-off work of the story, the generation unit can generate the original story first and the spin-off later. This allows the user to experience a natural flow of the story by adjusting the order of generation based on the relevance of the stories. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the relevance of the stories into the generation AI and have the generation AI adjust the order of generation.

[0046] When generating a virtual space, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation and past learning history. For example, if the user is a beginner, the generation unit can avoid technical terminology and use simple language to explain things. For example, if the user is an intermediate learner, the generation unit can use appropriate technical terminology to explain things. Furthermore, if the user is an advanced learner, the generation unit can use a lot of technical terminology to explain things in detail. This allows for adjusting the use of technical terminology according to the user's level of expertise, thereby providing a more understandable virtual space. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input data on the user's level of expertise into the generation AI and have the generation AI adjust the use of technical terminology.

[0047] During navigation, the navigation unit can adjust the level of detail of the navigation based on the progress of the story. The progress includes, but is not limited to, for example, chapters and episodes of the story. For example, the navigation unit provides detailed navigation in the climax scene of the story. For example, the navigation unit can also provide simple navigation in the introduction of the story. Furthermore, the navigation unit can also provide navigation with an emotional impact in the conclusion of the story. In this way, by adjusting the level of detail of the navigation based on the progress of the story, more appropriate navigation can be provided. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input data on the progress of the story into AI and have the AI ​​adjust the level of detail of the navigation.

[0048] During navigation, the navigation unit can improve the accuracy of navigation by referring to the user's past navigation history. The navigation history includes, for example, past navigation routes and the user's behavior history, but is not limited to these examples. The navigation unit, for example, suggests an optimal navigation method based on routes the user has used in the past. The navigation unit can also suggest a navigation method that avoids congestion, for example, based on the user's past navigation history. The navigation unit can also analyze the user's past navigation history and suggest the most efficient navigation method. By referring to the user's past navigation history, more appropriate navigation can be provided. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's navigation history data into AI and have the AI ​​improve the accuracy of navigation.

[0049] During navigation, the navigation unit can customize navigation based on the user's current location information. Location information includes, but is not limited to, GPS data and location-based services. For example, when the user is in a specific location, the navigation unit provides navigation information related to that location. For example, when the user is moving, the navigation unit can update the user's current location in real time and suggest an optimal route. The navigation unit can also provide detailed navigation information as the user approaches the destination. This allows for more appropriate navigation by customizing navigation based on the user's current location information. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's location information data into AI and have the AI ​​customize the navigation.

[0050] During navigation, the navigation unit can provide an optimal navigation method by taking into account the user's device information. Device information includes, but is not limited to, the device type, OS version, and sensor information. For example, if the user is using a smartphone, the navigation unit can provide a navigation method tailored to the screen size. For example, if the user is using a tablet, the navigation unit can provide a navigation method optimized for a large screen. Furthermore, if the user is using a smartwatch, the navigation unit can provide a simple and highly visible navigation method. This allows for more appropriate navigation by taking into account the user's device information. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input device information into AI and have the AI ​​provide the optimal navigation method.

[0051] During navigation, the navigation unit can make the navigation content multilingual according to the user's language setting. Examples of language settings include, but are not limited to, the device's language setting and the user's selected language. For example, the navigation unit automatically sets the navigation language based on the user's device's language setting. For example, the navigation unit can provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the navigation unit can provide navigation in that language. This makes it possible to provide more appropriate navigation by making the navigation content multilingual according to the user's language setting. Some or all of the above-described processing in the navigation unit may be performed using, or without, AI. For example, the navigation unit can input language setting data into AI and have the AI ​​execute multilingual navigation.

[0052] During navigation, the navigation unit can adjust the navigation speed based on the user's walking speed. Examples of walking speed include, but are not limited to, pedometer and GPS data. For example, the navigation unit slows down the navigation speed when the user is walking slowly. For example, the navigation unit can also speed up the navigation speed when the user is walking fast. The navigation unit can also pause navigation when the user stops and resume it when the user starts walking again. This allows for more appropriate navigation by adjusting the navigation speed based on the user's walking speed. Some or all of the above-described processing in the navigation unit may be performed using, or without, AI. For example, the navigation unit can input walking speed data to AI and have the AI ​​adjust the navigation speed.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The reception unit can track the user's reading progress based on the user's reading history and automatically display unread chapters or pages. For example, the reception unit can automatically display the next page from the page the user read last time. The reception unit can also display a chapter again later if the user skips a particular chapter. Furthermore, if the user stops reading, the reception unit can save the page at that time as a bookmark and resume reading from that page the next time the user reads. This makes the user's reading experience smoother and more efficient.

[0055] The generator can analyze the user's reading speed and adjust the speed at which the virtual space is generated according to the reading speed. For example, if the user reads quickly, the generator can speed up the generation of the virtual space. Alternatively, if the user reads slowly, the generator can slow down the generation of the virtual space. Furthermore, the generator can adjust the progress of the story based on the user's reading speed, allowing the user to gain a deeper understanding of the story content. This makes it possible to generate a virtual space according to the user's reading speed, providing a more personalized reading experience.

[0056] The generation unit can analyze the user's past reading history and reflect the story elements that the user likes in the virtual space. For example, if the user likes fantasy stories, magical and fantastical elements can be added to the virtual space. If the user likes mystery stories, a puzzle-solving element can be added to the virtual space. Furthermore, if the user likes action stories, action scenes can be added to the virtual space. This makes it possible to generate a virtual space according to the user's preferences, providing a more engaging reading experience.

[0057] The generation unit can adjust the difficulty of the story based on the user's reading history. For example, if the user is a beginner, the difficulty of the story can be set low. If the user is an intermediate reader, the difficulty of the story can be set medium. Furthermore, if the user is an advanced reader, the difficulty of the story can be set high. This provides a story difficulty that corresponds to the user's reading level, allowing for a more appropriate reading experience.

[0058] The generator can adjust the background music of a story based on the user's reading history. For example, if the user likes fantasy stories, fantastical background music can be added. If the user likes mystery stories, tense background music can be added. If the user likes action stories, energetic background music can be added. This provides background music according to the user's preferences, providing a more engaging reading experience.

[0059] The generator can customize the appearance of a story character based on the user's reading history. For example, if a user prefers a particular character's appearance, the generator can generate a character that reflects that appearance. Also, if a user prefers particular clothing or accessories, the generator can generate a character that reflects those preferences. Furthermore, if a user prefers a particular hairstyle or color, the generator can generate a character that reflects those preferences. This allows the generator to provide character appearances that match the user's preferences, providing a more engaging reading experience.

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

[0061] Step 1: The receptionist selects the book the user wants to read. The books the user wants to read can include novels, non-fiction, technical books, etc. The receptionist allows the user to search the bookshelves in the virtual space and select the book that interests them. Step 2: The generator uses a generation AI to analyze the contents of the book selected by the reception unit and generate a virtual space. The generator AI analyzes the contents of the book using natural language processing models and machine learning algorithms, and generates a virtual space based on a specific story. Step 3: The navigation unit navigates within the virtual space generated by the generation unit. The navigation unit allows the user to interact with characters in the story and participate in events that occur within the story. It can also navigate according to the orientation of the user's smartphone and display videos according to the user's walking speed.

[0062] (Example 2) A system according to an embodiment of the present invention utilizes generative AI to revolutionize children's reading experiences. This system combines virtual reality technology and generative AI to provide a virtual library. In this library, the world of a story selected by a child is recreated in real time in the virtual space. For example, after reading a particular story, the user can explore that story's world. This experience transforms reading from a mere activity into an adventure, fostering children's imagination and interest in reading. This allows the system to significantly transform children's reading experience. For example, children can enjoy reading while feeling as if they are the protagonist of the story. Furthermore, through the experience in the virtual space, children can gain a deeper understanding of the story's content and maximize the enjoyment of reading.

[0063] A reading experience system according to an embodiment includes a reception unit, a generation unit, and a navigation unit. The reception unit selects a book the user wants to read. Examples of books the user wants to read include, but are not limited to, novels, non-fiction, and technical books. The reception unit allows the user to, for example, search bookshelves in a virtual space and select a book of interest. The generation unit uses a generation AI to analyze the content of the book selected by the reception unit and generate a virtual space. The generation AI analyzes the content of the book using, for example, a natural language processing model or a machine learning algorithm and generates the virtual space. The generation unit generates the virtual space based on, for example, a specific story. The navigation unit navigates within the virtual space generated by the generation unit. The navigation unit allows, for example, the user to interact with characters in the story and participate in events occurring in the story. The navigation unit can also navigate based on, for example, the orientation of the user's smartphone. The navigation unit can also display videos based on the user's walking speed. As a result, the reading experience system according to an embodiment generates a virtual space based on the content of a book selected by the user and navigates through it, thereby improving the reading experience.

[0064] The generation unit can analyze the contents of the book using a generation AI and generate a virtual space. Examples of the generation AI include, but are not limited to, natural language processing models and machine learning algorithms. For example, the generation unit can analyze the contents of the book using a generation AI and generate a virtual space based on the contents. For example, the generation unit can also generate a virtual space based on a specific story. This improves the accuracy of generating the virtual space by using the generation AI. Some or all of the above-mentioned processing in the generation unit can be performed using, or without, the generation AI. For example, the generation unit can input the contents of the book into the generation AI and cause the generation AI to generate the virtual space.

[0065] The navigation unit allows the user to interact with characters in the story and participate in events that occur within the story. For example, the navigation unit allows the user to interact with characters in the story. The interaction may be performed by, for example, voice interaction or text interaction, but is not limited to these examples. The navigation unit also allows the user to participate in events that occur within the story. For example, events include, but are not limited to, events related to the progression of the story and events based on user selections. This allows the user to enjoy reading while deeply engaging in the world of the story. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit may input user selections into AI and have the AI ​​execute the interaction and progress of events.

[0066] The generation unit can generate a virtual space based on a specific story. Examples of specific stories include, but are not limited to, works by a specific author or stories of a specific genre. The generation unit can generate a virtual space based on, for example, a specific story. The generation unit can also analyze the content of the story and generate a virtual space based on that content. This allows the world of the story to be realistically reproduced by generating a virtual space based on a specific story. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the content of the specific story into the generation AI and have the generation AI generate the virtual space.

[0067] The navigation unit can perform navigation according to the orientation of the user's smartphone. The orientation of the smartphone can be detected using, for example, a gyro sensor or an acceleration sensor, but is not limited to such examples. The navigation unit can perform navigation according to, for example, the orientation of the user's smartphone. The navigation unit can change the viewpoint in the virtual space according to, for example, the orientation of the smartphone. This allows for more intuitive operation by performing navigation according to the orientation of the user's smartphone. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input smartphone orientation data into AI and have the AI ​​adjust the navigation.

[0068] The navigation unit can display videos in accordance with the user's walking speed. The walking speed can be detected using, for example, a pedometer or GPS data, but is not limited to these examples. The navigation unit can display videos in accordance with the user's walking speed, for example. The navigation unit can slow down the playback speed of the video when the user is walking slowly. The navigation unit can also speed up the playback speed of the video when the user is walking fast. Furthermore, the navigation unit can pause playback of the video when the user stops and resume it when the user starts walking again. This provides a more natural experience by displaying videos in accordance with the user's walking speed. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input walking speed data into AI and have the AI ​​adjust the playback speed of the video.

[0069] The reception unit can estimate the user's emotions and recommend books based on the estimated user emotions. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expressions. The reception unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations. This allows for more appropriate books to be recommended based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0070] The reception unit can analyze the user's past reading history and recommend appropriate books. Reading history includes, for example, a list of books read in the past and reading time, but is not limited to these examples. The reception unit, for example, recommends related books based on the genre or theme of books read in the past by the user. The reception unit can also recommend new works by authors of books that the user has previously rated highly. The reception unit can also recommend unread series works from the user's reading history. In this way, by analyzing the past reading history, it is possible to recommend the most suitable book for the user. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's reading history data into AI and have the AI ​​recommend appropriate books.

[0071] When selecting a book, the reception unit can filter based on the user's current interests and concerns. Interests and concerns include, but are not limited to, survey results and past behavioral data. The reception unit, for example, filters related books based on keywords recently searched by the user. The reception unit can also filter books related to events or activities the user recently participated in. The reception unit can also filter related books based on videos or movies the user recently watched. By filtering books based on the user's current interests and concerns, more appropriate books can be provided. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's interest and concern data into AI and have the AI ​​perform the filtering.

[0072] When selecting a book, the reception unit can provide an appropriate selection means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, when a user uses voice input, the reception unit allows the user to input the book title and author name using voice recognition technology. For example, when a user uses text input, the reception unit can also provide keyword search or category search. Furthermore, when a user uses image input, the reception unit can also allow the user to upload a book cover image and search for similar books. This provides an optimal selection means depending on the user's input method, making the system easier to use. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input voice data or image data into AI and have the AI ​​provide an appropriate selection means.

[0073] The reception unit can estimate the user's emotions and adjust the selection order of books based on the estimated user emotions. For example, the reception unit captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression. The reception unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations. This allows the selection order of books to be adjusted based on the user's emotions, thereby providing more appropriate books. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0074] When selecting a book, the reception unit can prioritize displaying highly relevant books based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. For example, if the user is in a specific area, the reception unit can prioritize displaying books related to that area. For example, if the user is traveling, the reception unit can also prioritize displaying books related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can also prioritize displaying books related to the event. In this way, highly relevant books can be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's location information data into AI and cause the AI ​​to display highly relevant books.

[0075] When selecting a book, the reception unit can analyze the user's social media activity and recommend related books. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the reception unit can recommend new books by authors the user follows on social media. For example, the reception unit can also recommend books related to articles or posts the user has shared on social media. The reception unit can also display books recommended by the user's friends on social media. In this way, related books can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's social media data into AI and have the AI ​​recommend related books.

[0076] The reception unit can customize the selection method by reflecting the user's past feedback when selecting a book. Feedback includes, but is not limited to, user reviews and rating scores. For example, the reception unit can prioritize displaying genres of books that the user has previously rated highly. For example, the reception unit can also exclude genres of books that the user has previously rated poorly. The reception unit can also adjust the recommendation algorithm based on the user's feedback to suggest optimal books. This allows for more appropriate books to be provided by reflecting the user's past feedback. Some or all of the above-described processing by the reception unit can be performed using, or without, AI. For example, the reception unit can input user feedback data into AI and have the AI ​​customize the selection method.

[0077] The generation unit can estimate the user's emotions and adjust the way the virtual space is presented based on the estimated user emotions. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expressions. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations. This allows the user to adjust the way the virtual space is presented based on the user's emotions, thereby providing a more appropriate experience. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0078] When generating the virtual space, the generation unit can adjust the level of detail based on important scenes in the story. Important scenes include, but are not limited to, the climax and major events of the story. For example, the generation unit can use detailed graphics and effects to enhance the sense of realism in the climax scene of the story. For example, the generation unit can draw the user in with simple graphics in the introduction of the story. The generation unit can also provide a memorable experience for the user by adding moving effects in the conclusion of the story. In this way, a realistic experience can be provided by adjusting the level of detail based on important scenes in the story. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data of important scenes in the story into the generation AI and have the generation AI adjust the level of detail.

[0079] When generating a virtual space, the generation unit can apply different generation algorithms depending on the genre of the story. Genres include, but are not limited to, fantasy, mystery, and science fiction. For example, in the case of a fantasy story, the generation unit can apply a generation algorithm that emphasizes magical and fantastical elements. For example, in the case of a science fiction story, the generation unit can apply a generation algorithm that emphasizes futuristic designs and technology. Furthermore, in the case of a mystery story, the generation unit can apply a generation algorithm that emphasizes dark colors and an enigmatic atmosphere. By applying different generation algorithms depending on the genre of the story, a more appropriate virtual space can be provided. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input data on the genre of the story into the generation AI and cause the generation AI to apply the generation algorithm.

[0080] When generating a virtual space, the generation unit can improve the accuracy of the generation by referring to the user's past experiences. Past experiences include, but are not limited to, past usage history of the virtual space and user feedback. For example, the generation unit generates a space that reflects the user's preferences and interests based on data on virtual spaces the user has experienced in the past. For example, the generation unit can generate a space by incorporating elements of virtual spaces that the user has previously rated highly. The generation unit can also analyze the user's past experience data and generate an optimal virtual space. This makes it possible to provide a more appropriate virtual space by referring to the user's past experiences. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past experience data into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0081] The generation unit can estimate the user's emotion and adjust the length of the virtual space based on the estimated user's emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expression. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations. This allows the length of the virtual space to be adjusted based on the user's emotion, providing a more appropriate experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0082] When generating a virtual space, the generation unit can determine the generation priority based on the publication date of the story. Publication dates include, but are not limited to, publication dates and release dates. For example, the generation unit can prioritize generating a virtual space for a new story. For example, the generation unit can also prioritize generating a virtual space for a popular story. The generation unit can also prioritize generating a virtual space for a story that is frequently requested by users. By determining the generation priority based on the publication date of the story, the latest stories and popular stories can be provided preferentially. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input data on the publication date of the story into the generation AI and have the generation AI determine the generation priority.

[0083] When generating a virtual space, the generation unit can adjust the order of generation based on the relevance of the stories. Examples of relevance include, but are not limited to, sequels to a story or works by the same author. For example, if there is a first and second part of a story, the generation unit can generate the first part first and the second part later. For example, if there is a series of works in a story, the generation unit can generate them in the order of the series. Furthermore, if there is a spin-off work of the story, the generation unit can generate the original story first and the spin-off later. This allows the user to experience a natural flow of the story by adjusting the order of generation based on the relevance of the stories. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the relevance of the stories into the generation AI and have the generation AI adjust the order of generation.

[0084] When generating a virtual space, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation and past learning history. For example, if the user is a beginner, the generation unit can avoid technical terminology and use simple language to explain things. For example, if the user is an intermediate learner, the generation unit can use appropriate technical terminology to explain things. Furthermore, if the user is an advanced learner, the generation unit can use a lot of technical terminology to explain things in detail. This allows for adjusting the use of technical terminology according to the user's level of expertise, thereby providing a more understandable virtual space. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input data on the user's level of expertise into the generation AI and have the generation AI adjust the use of technical terminology.

[0085] The navigation unit can estimate the user's emotions and adjust the navigation display method based on the estimated user's emotions. For example, the navigation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the navigation unit calculates an emotion score based on changes in facial expressions. The navigation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the navigation unit analyzes the tone and speed of the voice and calculates an emotion score. The navigation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the navigation unit calculates an emotion score based on heart rate fluctuations. This allows the navigation unit to adjust the navigation display method based on the user's emotions, thereby providing more appropriate navigation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, AI, or without AI. For example, the navigation unit can input image data of the user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0086] During navigation, the navigation unit can adjust the level of detail of the navigation based on the progress of the story. The progress includes, but is not limited to, for example, chapters and episodes of the story. For example, the navigation unit provides detailed navigation in the climax scene of the story. For example, the navigation unit can also provide simple navigation in the introduction of the story. Furthermore, the navigation unit can also provide navigation with an emotional impact in the conclusion of the story. In this way, by adjusting the level of detail of the navigation based on the progress of the story, more appropriate navigation can be provided. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input data on the progress of the story into AI and have the AI ​​adjust the level of detail of the navigation.

[0087] During navigation, the navigation unit can improve the accuracy of navigation by referring to the user's past navigation history. The navigation history includes, for example, past navigation routes and the user's behavior history, but is not limited to these examples. The navigation unit, for example, suggests an optimal navigation method based on routes the user has used in the past. The navigation unit can also suggest a navigation method that avoids congestion, for example, based on the user's past navigation history. The navigation unit can also analyze the user's past navigation history and suggest the most efficient navigation method. By referring to the user's past navigation history, more appropriate navigation can be provided. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's navigation history data into AI and have the AI ​​improve the accuracy of navigation.

[0088] During navigation, the navigation unit can customize navigation based on the user's current location information. Location information includes, but is not limited to, GPS data and location-based services. For example, when the user is in a specific location, the navigation unit provides navigation information related to that location. For example, when the user is moving, the navigation unit can update the user's current location in real time and suggest an optimal route. The navigation unit can also provide detailed navigation information as the user approaches the destination. This allows for more appropriate navigation by customizing navigation based on the user's current location information. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's location information data into AI and have the AI ​​customize the navigation.

[0089] The navigation unit can estimate the user's emotions and adjust the navigation order based on the estimated user emotions. For example, the navigation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the navigation unit calculates an emotion score based on changes in facial expressions. The navigation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the navigation unit analyzes the tone and speed of the voice and calculates an emotion score. The navigation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the navigation unit calculates an emotion score based on heart rate fluctuations. This allows the navigation order to be adjusted based on the user's emotions, thereby providing more appropriate navigation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the navigation unit can be performed using, for example, AI, or without AI. For example, the navigation unit can input image data of the user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0090] During navigation, the navigation unit can provide an optimal navigation method by taking into account the user's device information. Device information includes, but is not limited to, the device type, OS version, and sensor information. For example, if the user is using a smartphone, the navigation unit can provide a navigation method tailored to the screen size. For example, if the user is using a tablet, the navigation unit can provide a navigation method optimized for a large screen. Furthermore, if the user is using a smartwatch, the navigation unit can provide a simple and highly visible navigation method. This allows for more appropriate navigation by taking into account the user's device information. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input device information into AI and have the AI ​​provide the optimal navigation method.

[0091] During navigation, the navigation unit can make the navigation content multilingual according to the user's language setting. Examples of language settings include, but are not limited to, the device's language setting and the user's selected language. For example, the navigation unit automatically sets the navigation language based on the user's device's language setting. For example, the navigation unit can provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the navigation unit can provide navigation in that language. This makes it possible to provide more appropriate navigation by making the navigation content multilingual according to the user's language setting. Some or all of the above-described processing in the navigation unit may be performed using, or without, AI. For example, the navigation unit can input language setting data into AI and have the AI ​​execute multilingual navigation.

[0092] During navigation, the navigation unit can adjust the navigation speed based on the user's walking speed. Examples of walking speed include, but are not limited to, pedometer and GPS data. For example, the navigation unit slows down the navigation speed when the user is walking slowly. For example, the navigation unit can also speed up the navigation speed when the user is walking fast. The navigation unit can also pause navigation when the user stops and resume it when the user starts walking again. This allows for more appropriate navigation by adjusting the navigation speed based on the user's walking speed. Some or all of the above-described processing in the navigation unit may be performed using, or without, AI. For example, the navigation unit can input walking speed data to AI and have the AI ​​adjust the navigation speed. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, generation unit, and navigation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, allowing the user to search bookshelves in the virtual space and select a book of interest. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzing the contents of the selected book using a generation AI and generating the virtual space. The navigation unit is realized, for example, by the control unit 46A of the smart device 14, allowing the user to interact with characters in the story and participate in events occurring in the story. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, generation unit, and navigation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, allowing the user to search bookshelves in the virtual space and select a book of interest. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzing the contents of the selected book using a generation AI and generating the virtual space. The navigation unit is realized, for example, by the control unit 46A of the smart glasses 214, allowing the user to interact with characters in the story and participate in events occurring in the story. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, and navigation unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314, allowing the user to search bookshelves in the virtual space and select a book of interest. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzing the contents of the selected book using a generation AI and generating a virtual space. The navigation unit is realized, for example, by the control unit 46A of the headset-type terminal 314, allowing the user to interact with characters in the story and participate in events that occur within the story. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, and navigation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, allowing the user to search bookshelves in the virtual space and select a book of interest. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzing the contents of the selected book using a generation AI and generating a virtual space. The navigation unit is realized, for example, by the control unit 46A of the robot 414, allowing the user to interact with characters in the story and participate in events that occur in the story.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The reception unit can track the user's reading progress based on the user's reading history and automatically display unread chapters or pages. For example, the reception unit can automatically display the next page from the page the user read last time. The reception unit can also display a chapter again later if the user skips a particular chapter. Furthermore, if the user stops reading, the reception unit can save the page at that time as a bookmark and resume reading from that page the next time the user reads. This makes the user's reading experience smoother and more efficient.

[0095] The generator can analyze the user's reading speed and adjust the speed at which the virtual space is generated according to the reading speed. For example, if the user reads quickly, the generator can speed up the generation of the virtual space. Alternatively, if the user reads slowly, the generator can slow down the generation of the virtual space. Furthermore, the generator can adjust the progress of the story based on the user's reading speed, allowing the user to gain a deeper understanding of the story content. This makes it possible to generate a virtual space according to the user's reading speed, providing a more personalized reading experience.

[0096] The navigation unit can estimate the user's emotions and adjust the progress of the story based on the estimated user emotions. For example, if the user is excited, the navigation unit can speed up the progress of the story. Also, if the user is relaxed, the navigation unit can slow down the progress of the story. Furthermore, if the user is feeling anxious, the navigation unit can pause the progress of the story and insert a relaxing scene. This allows the story to progress in accordance with the user's emotions, providing a more appropriate reading experience.

[0097] The generation unit can analyze the user's past reading history and reflect the story elements that the user likes in the virtual space. For example, if the user likes fantasy stories, magical and fantastical elements can be added to the virtual space. If the user likes mystery stories, a puzzle-solving element can be added to the virtual space. Furthermore, if the user likes action stories, action scenes can be added to the virtual space. This makes it possible to generate a virtual space according to the user's preferences, providing a more engaging reading experience.

[0098] The navigation unit can estimate the user's emotions and adjust the voice guidance of the navigation based on the estimated user's emotions. For example, if the user is excited, the tone of the voice guidance can be made calmer. Also, if the user is relaxed, the tone of the voice guidance can be made brighter. Furthermore, if the user is feeling anxious, the tone of the voice guidance can be made gentler to give a sense of security. This makes it possible to provide voice guidance that corresponds to the user's emotions, and more appropriate navigation can be provided.

[0099] The generation unit can adjust the difficulty of the story based on the user's reading history. For example, if the user is a beginner, the difficulty of the story can be set low. If the user is an intermediate reader, the difficulty of the story can be set medium. Furthermore, if the user is an advanced reader, the difficulty of the story can be set high. This provides a story difficulty that corresponds to the user's reading level, allowing for a more appropriate reading experience.

[0100] The reception unit can estimate the user's emotions, track the reading progress based on the estimated user emotions, and suggest breaks at appropriate times. For example, if the user is tired, the reception unit can suggest a break. Also, if the user is concentrating, the reception unit can not suggest a break. Furthermore, if the user is feeling stressed, the reception unit can suggest a break to relax. This makes it possible to suggest breaks according to the user's emotions, providing a healthier reading experience.

[0101] The generator can adjust the background music of a story based on the user's reading history. For example, if the user likes fantasy stories, fantastical background music can be added. If the user likes mystery stories, tense background music can be added. If the user likes action stories, energetic background music can be added. This provides background music according to the user's preferences, providing a more engaging reading experience.

[0102] The navigation unit can estimate the user's emotions and adjust the visual effects of the navigation based on the estimated user's emotions. For example, if the user is excited, the visual effects can be calming. If the user is relaxed, the visual effects can be brighter. Furthermore, if the user is feeling anxious, the visual effects can be gentler to give a sense of security. This makes it possible to provide visual effects that correspond to the user's emotions, thereby providing more appropriate navigation.

[0103] The generator can customize the appearance of a story character based on the user's reading history. For example, if a user prefers a particular character's appearance, the generator can generate a character that reflects that appearance. Also, if a user prefers particular clothing or accessories, the generator can generate a character that reflects those preferences. Furthermore, if a user prefers a particular hairstyle or color, the generator can generate a character that reflects those preferences. This allows the generator to provide character appearances that match the user's preferences, providing a more engaging reading experience.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The receptionist selects the book the user wants to read. The books the user wants to read can include novels, non-fiction, technical books, etc. The receptionist allows the user to search the bookshelves in the virtual space and select the book that interests them. Step 2: The generator uses a generation AI to analyze the contents of the book selected by the reception unit and generate a virtual space. The generator AI analyzes the contents of the book using natural language processing models and machine learning algorithms, and generates a virtual space based on a specific story. Step 3: The navigation unit navigates within the virtual space generated by the generation unit. The navigation unit allows the user to interact with characters in the story and participate in events that occur within the story. It can also navigate according to the orientation of the user's smartphone and display videos according to the user's walking speed.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] 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.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception section where users can select the book they want to read; a generation unit that analyzes the contents of the book selected by the reception unit and generates a virtual space; a navigation unit that performs navigation within the virtual space generated by the generation unit. A system characterized by:

2. The generation unit Analyze the contents of the book using generative AI and generate a virtual space 2. The system of claim 1.

3. The navigation unit Users can interact with characters in the story and participate in events that occur within the story.

2. The system of claim 1.

4. The generation unit Creating a virtual space based on a specific story 2. The system of claim 1.

5. The navigation unit Navigation based on the orientation of the user's smartphone 2. The system of claim 1.

6. The navigation unit Display videos at the user's walking speed 2. The system of claim 1.

7. The reception unit Estimate user emotions and recommend books based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyzes the user's past reading history and recommends appropriate books 2. The system of claim 1.

9. The reception unit Filtering book selection based on the user's current interests 2. The system of claim 1.

10. The reception unit When selecting a book, provide appropriate selection methods depending on the user's input method.

2. The system of claim 1.

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