System

Abstract

An object of a system according to an embodiment is to make it easy to find a book in a new field on the Internet.SOLUTION: A system includes a virtual bookstore construction part, a book introduction part, and a discovery promotion part. A virtual bookstore construction part constructs a virtual bookstore. A book introduction part introduces books in the virtual bookstore constructed by the virtual bookstore construction part. The discovery promoting unit promotes new discovery based on the book introduced by the book introducing 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] With conventional technology, book recommendations on the Internet tended to be biased toward personal interests, making it difficult to discover new fields.

[0005] The system according to the embodiment aims to make it easier to discover books in new fields on the Internet. [Means for solving the problem]

[0006] A system according to an embodiment includes a virtual bookstore construction unit, a book introduction unit, and a discovery promotion unit. The virtual bookstore construction unit constructs a virtual bookstore. The book introduction unit introduces books in the virtual bookstore constructed by the virtual bookstore construction unit. The discovery promotion unit encourages new discoveries based on the books introduced by the book introduction unit. [Effects of the Invention]

[0007] The system according to the embodiment can make it easier to discover books in new fields on the Internet. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The book recommendation system according to the embodiment of the present invention is a system that recreates a real large bookstore on the Internet, recommends books using personal Internet history and accumulated data from AI, and encourages new discoveries. As a result, the book recommendation system allows users to access a variety of books from the comfort of their own homes and gain new knowledge and perspectives.

[0029] A book recommendation system according to an embodiment includes a virtual bookstore construction unit, a book recommendation unit, and a discovery promotion unit. The virtual bookstore construction unit constructs a virtual bookstore. For example, the virtual bookstore construction unit recreates the layout of bookshelves and books in a bookstore in a virtual space, allowing users to browse freely. The virtual bookstore construction unit also allows users to walk around the virtual bookstore, pick up books of interest, and check their contents. For example, the virtual bookstore construction unit recreates the atmosphere of a realistic bookstore using a 3D model, allowing users to search for books as if they were in a real bookstore. The book recommendation unit introduces books in the virtual bookstore constructed by the virtual bookstore construction unit. For example, the book recommendation unit analyzes a user's internet history and recommends books based on that history. The book recommendation unit also suggests related books based on the user's recently searched keywords and the content of websites they have visited. For example, the book recommendation unit uses a generative AI to analyze the user's internet history and select appropriate books. The discovery promotion unit encourages new discoveries based on the books introduced by the book recommendation unit. For example, the discovery promotion unit utilizes the AI's large amount of accumulated data to encourage new discoveries even in fields in which the user is not personally interested. The discovery promotion unit can also suggest books in fields in which the user is not normally interested, thereby broadening the user's knowledge of those fields. For example, the discovery promotion unit uses the generation AI to suggest books in other related fields based on the user's interests. This allows the book recommendation system according to the embodiment to enable the user to access a variety of books from the comfort of their own home and gain new knowledge and perspectives. For example, by being suggested books in fields in which the user is not normally interested, the user can gain new interests and knowledge. Furthermore, through detailed book information and purchasing support, the user can efficiently obtain books and deepen their knowledge.

[0030] The virtual bookstore construction unit can track user movements and analyze user behavior patterns to automatically adjust optimal book placement. For example, the virtual bookstore construction unit tracks user movements within the virtual bookstore and analyzes how much time users spend on each bookshelf. This allows the placement of popular bookshelves and books to be optimized. The virtual bookstore construction unit also records which books users pick up within the virtual bookstore and which bookshelves they frequently visit, and automatically adjusts book placement based on this data. For example, by analyzing user behavior patterns, if there are many users who are interested in a particular genre or theme, books in that genre can be placed in prominent locations. This allows the book placement to be optimized based on user behavior patterns.

[0031] The virtual bookstore construction unit can personalize the bookstore layout based on the user's past browsing history or purchase history. For example, the virtual bookstore construction unit analyzes the user's past browsing history or purchase history and personalizes the book arrangement in the bookstore based on that data. For example, books in genres that the user frequently reads are placed in prominent locations. The virtual bookstore construction unit also provides a different bookstore layout for each user and arranges books according to individual interests. For example, books by a particular author or series are displayed preferentially. The virtual bookstore construction unit also dynamically changes the book arrangement in the bookstore based on the user's behavior data and places books that are likely to interest the user in prominent locations. This makes it possible to provide a different bookstore layout for each user.

[0032] The virtual bookstore construction unit can arrange not only books but also related video and audio content to provide a multimedia experience. For example, the virtual bookstore construction unit arranges not only books but also related video and audio content in the virtual bookstore, allowing users to enjoy a multimedia experience. For example, it provides a video introducing the book or an audio interview of the author. The virtual bookstore construction unit also arranges video and audio content related to the content of the book in the virtual bookstore, allowing users to gain a deeper understanding of the book's content. For example, it provides documentary videos and audiobooks. The virtual bookstore construction unit also arranges multimedia content in the virtual bookstore, allowing users to enjoy the book's content visually and audibly. For example, it provides content that visualizes parts of the book or audio readings. This allows users to gain a deeper understanding of the book's content.

[0033] The virtual bookstore construction unit can add a real-time chat function and a review sharing function to promote interaction between users. For example, the virtual bookstore construction unit adds a real-time chat function within the virtual bookstore to enable users to exchange opinions about books. For example, users can hold discussions about specific books. The virtual bookstore construction unit also adds a review sharing function within the virtual bookstore to enable users to post book reviews and share them with other users. For example, users can share their impressions and ratings of books they have read. The virtual bookstore construction unit also adds a forum or group function within the virtual bookstore to promote interaction between users, to form communities around specific themes or genres. This allows users to exchange opinions about books.

[0034] The book recommendation unit analyzes not only the user's internet history but also social media posts or comments, allowing for more accurate book recommendations. The book recommendation unit, for example, analyzes social media posts and comments in addition to the user's internet history, allowing for more accurate book recommendations. For example, it recommends books based on keywords mentioned by the user on social media. The book recommendation unit also analyzes social media data to recommend books based on a detailed understanding of the user's interests. For example, it selects books based on information about accounts the user follows and groups the user participates in. The book recommendation unit also analyzes the user's social media posts and comments to recommend related books. For example, it suggests books related to the content the user has posted or the topics on which the user has commented. In this way, more accurate book recommendations are possible by analyzing the user's social media data.

[0035] The book recommendation unit can recommend books by analyzing the user's past book purchase history and review history and understanding the user's reading tendencies in detail. The book recommendation unit, for example, analyzes the user's past book purchase history and understands the user's reading tendencies based on that data to recommend books. For example, it may preferentially suggest books in a particular genre or author. The book recommendation unit may also analyze the user's book review history and recommend books related to books that the user has given high ratings. For example, it may suggest books on the same theme or style. The book recommendation unit may also understand the user's reading tendencies in detail based on the user's past purchase history and review history and make personalized book recommendations. For example, it may suggest books in the user's favorite genre or theme. This allows for a detailed understanding of the user's reading tendencies, enabling more personalized book recommendations.

[0036] When analyzing the user's Internet history, the book recommendation unit can also recommend events or seminars that may interest the user. For example, the book recommendation unit analyzes the user's Internet history and recommends events and seminars that may interest the user based on that data. For example, it suggests events related to a specific theme. The book recommendation unit also recommends events and seminars that the user may be interested in based on the user's search history and browsing history. For example, it suggests seminars in areas that the user is interested in. The book recommendation unit also analyzes the user's Internet history and recommends related events and seminars. For example, it suggests events related to keywords that the user recently searched for. In this way, by recommending events and seminars that interest the user, the user's learning and the range of their interests can be broadened.

[0037] The book recommendation unit may also suggest related online courses or learning resources when recommending a book, thereby broadening the scope of the user's learning. For example, the book recommendation unit may also suggest related online courses or learning resources when recommending a book, thereby broadening the scope of the user's learning. For example, it may suggest online courses related to a specific theme. The book recommendation unit may also analyze the user's internet history and suggest related learning resources when recommending a book. For example, it may suggest online courses in areas that interest the user. The book recommendation unit may also suggest related online courses or learning resources when recommending a book, thereby broadening the scope of the user's learning. For example, it may suggest online courses related to the content of the book. In this way, by suggesting related learning resources when recommending a book, it is possible to broaden the scope of the user's learning.

[0038] The discovery promotion unit can draw new interests by analyzing the user's reading history and intentionally recommending books in less relevant fields. The discovery promotion unit, for example, analyzes the user's reading history and intentionally recommends books in less relevant fields. For example, it suggests books in genres that the user does not usually read. The discovery promotion unit also recommends books in less relevant fields based on the user's reading history to draw new interests. For example, it intentionally suggests books in fields that the user is not interested in. The discovery promotion unit also draws new interests by analyzing the user's reading history and recommending books in less relevant fields. For example, it suggests books in fields that the user is not usually interested in. In this way, it is possible to broaden the user's knowledge by recommending books that draw new interests based on the user's reading history.

[0039] The discovery promotion unit can recommend the latest research papers and academic articles related to the user's field of interest, encouraging the user to dig deeper into knowledge. The discovery promotion unit, for example, recommends the latest research papers and academic articles related to the user's field of interest, encouraging the user to dig deeper into knowledge. For example, it suggests the latest research results related to a topic that interests the user. The discovery promotion unit also recommends academic articles related to the user's field of interest, encouraging the user to dig deeper into knowledge. For example, it suggests the latest research papers in a topic that interests the user. The discovery promotion unit also recommends the latest research papers and academic articles related to the user's field of interest, encouraging the user to dig deeper into knowledge. For example, it suggests academic articles related to a topic that interests the user. In this way, by recommending the latest research papers and academic articles related to the user's field of interest, the user can be encouraged to dig deeper into knowledge.

[0040] When recommending a book in a field that the user is not normally interested in, the discovery promotion unit may simultaneously suggest a documentary or interview video related to that field. For example, when recommending a book in a field that the user is not normally interested in, the discovery promotion unit simultaneously suggests a documentary or interview video related to that field. For example, video content that complements the content of the book is provided. Furthermore, when recommending a book in a field that the user is not normally interested in, the discovery promotion unit suggests a documentary or interview video related to that field to arouse interest. For example, video content related to the theme of the book is provided. Furthermore, when recommending a book in a field that the user is not normally interested in, the discovery promotion unit suggests a documentary or interview video related to that field to arouse interest. For example, video content that complements the content of the book is provided. In this way, when recommending a book in a field that the user is not normally interested in, the discovery promotion unit suggests a related documentary or interview video to arouse interest.

[0041] When recommending books in a new field, the discovery promotion unit can provide commentary and reviews by experts in that field to deepen understanding. For example, when recommending books in a new field, the discovery promotion unit can provide commentary and reviews by experts in that field to deepen the user's understanding. For example, it can provide comments from experts and explanatory videos. Furthermore, when recommending books in a new field, the discovery promotion unit can provide reviews by experts in that field to deepen the user's understanding. For example, it can provide evaluations and opinions from experts. Furthermore, when recommending books in a new field, the discovery promotion unit can provide commentary and reviews by experts in that field to deepen the user's understanding. For example, it can provide comments from experts and explanatory videos. In this way, when recommending books in a new field, the discovery promotion unit can provide commentary and reviews by experts to deepen the user's understanding.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The virtual bookstore construction unit can track user movements and analyze user behavior patterns to automatically adjust optimal book placement. For example, it can track user movements within the virtual bookstore and analyze how much time a user spends on each bookshelf. This allows it to optimize the placement of popular bookshelves and books. The virtual bookstore construction unit also records which books users pick up within the virtual bookstore and which bookshelves they frequently visit, and automatically adjusts book placement based on this data. For example, it can analyze user behavior patterns and, if there are many users who are interested in a particular genre or theme, it can place books of that genre in prominent locations. This allows it to optimize book placement based on user behavior patterns.

[0044] The virtual bookstore construction unit can personalize the bookstore layout based on the user's past browsing history or purchase history. For example, it can analyze the user's past browsing history and purchase history and personalize the book arrangement in the bookstore based on that data. For example, books in genres that the user frequently reads can be placed in prominent locations. The virtual bookstore construction unit can also provide a different bookstore layout for each user and arrange books according to individual interests. For example, books by a particular author or series can be displayed preferentially. The virtual bookstore construction unit can also dynamically change the book arrangement in the bookstore based on the user's behavior data and place books that are likely to interest the user in prominent locations. This makes it possible to provide a different bookstore layout for each user.

[0045] The virtual bookstore construction unit can arrange not only books but also related video and audio content to provide a multimedia experience. For example, not only books but also related video and audio content can be arranged in the virtual bookstore, allowing users to enjoy a multimedia experience. For example, a video introducing the book or an audio interview with the author can be provided. The virtual bookstore construction unit can also arrange video and audio content related to the contents of the book in the virtual bookstore, allowing users to gain a deeper understanding of the book's contents. For example, documentary videos and audiobooks can be provided. The virtual bookstore construction unit can also arrange multimedia content in the virtual bookstore, allowing users to enjoy the contents of the book visually and audibly. For example, video content of parts of the book or audio readings can be provided. This allows users to gain a deeper understanding of the book's contents.

[0046] The virtual bookstore construction unit can add real-time chat functions and review sharing functions to promote interaction between users. For example, a real-time chat function can be added to the virtual bookstore to allow users to exchange opinions about books. For example, a discussion can be held about a specific book. The virtual bookstore construction unit can also add a review sharing function to the virtual bookstore to allow users to post book reviews and share them with other users. For example, users can share their impressions and ratings of books they have read. The virtual bookstore construction unit can also add forum and group functions to the virtual bookstore to promote interaction between users, to form communities around specific themes or genres. This allows users to exchange opinions about books.

[0047] The book recommendation unit can analyze not only the user's internet history but also social media posts or comments to make more accurate book recommendations. For example, in addition to the user's internet history, the unit can analyze social media posts and comments to make more accurate book recommendations. For example, books can be recommended based on keywords mentioned by the user on social media. The book recommendation unit can also analyze social media data to recommend books based on a detailed understanding of the user's interests. For example, books can be selected based on information about accounts the user follows and groups the user participates in. The book recommendation unit can also analyze the user's social media posts and comments to recommend related books. For example, books related to the content the user has posted or the topics on which the user has commented can be suggested. In this way, more accurate book recommendations can be made by analyzing the user's social media data.

[0048] The book recommendation unit can recommend books by analyzing the user's past book purchase history and review history and understanding the user's reading tendencies in detail. For example, the unit can analyze the user's past book purchase history and understand the user's reading tendencies based on that data to recommend books. For example, it can prioritize suggesting books in a particular genre or by a particular author. The book recommendation unit can also analyze the user's book review history and recommend books related to books that the user has given high ratings. For example, it can suggest books on the same theme or style. The book recommendation unit can also understand the user's reading tendencies in detail based on the user's past purchase history and review history and make personalized book recommendations. For example, it can suggest books in the user's favorite genre or theme. This allows for a detailed understanding of the user's reading tendencies, making it possible to make more personalized book recommendations.

[0049] The discovery promotion unit can draw new interests by analyzing the user's reading history and intentionally recommending books in less relevant fields. For example, the unit can analyze the user's reading history and intentionally recommend books in less relevant fields. For example, it can suggest books in genres that the user does not usually read. The discovery promotion unit can also draw new interests by recommending books in less relevant fields based on the user's reading history. For example, it can intentionally suggest books in fields that the user is not interested in. The discovery promotion unit can also draw new interests by analyzing the user's reading history and recommending books in less relevant fields. For example, it can suggest books in fields that the user is not usually interested in. In this way, the user's knowledge can be broadened by recommending books that draw new interests based on the user's reading history.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The virtual bookstore construction unit constructs a virtual bookstore. For example, the virtual bookstore construction unit recreates the layout of bookshelves and books in a bookstore in a virtual space, allowing users to browse freely. The virtual bookstore construction unit also allows users to walk around the virtual bookstore, pick up books of interest, and check their contents. For example, the virtual bookstore construction unit recreates the atmosphere of a real bookstore using a 3D model, allowing users to search for books as if they were in a real bookstore. Step 2: The book recommendation unit introduces books in the virtual bookstore constructed by the virtual bookstore construction unit. For example, the book recommendation unit analyzes the user's internet history and recommends books based on that history. The book recommendation unit also suggests related books based on keywords the user recently searched for and the content of websites they visited. For example, the book recommendation unit uses a generation AI to analyze the user's internet history and select appropriate books. Step 3: The discovery promotion unit encourages new discoveries based on the books introduced by the book introduction unit. For example, the discovery promotion unit utilizes the AI's large amount of accumulated data to encourage new discoveries even in fields in which the user is not personally interested. The discovery promotion unit can also suggest books in fields in which the user is not normally interested, thereby expanding the user's knowledge of those fields. For example, the discovery promotion unit uses generative AI to suggest books in other related fields based on the user's interests.

[0052] (Example 2) The book recommendation system according to the embodiment of the present invention is a system that recreates a real large bookstore on the Internet, recommends books using personal Internet history and accumulated data from AI, and encourages new discoveries. As a result, the book recommendation system allows users to access a variety of books from the comfort of their own homes and gain new knowledge and perspectives.

[0053] A book recommendation system according to an embodiment includes a virtual bookstore construction unit, a book recommendation unit, and a discovery promotion unit. The virtual bookstore construction unit constructs a virtual bookstore. For example, the virtual bookstore construction unit recreates the layout of bookshelves and books in a bookstore in a virtual space, allowing users to browse freely. The virtual bookstore construction unit also allows users to walk around the virtual bookstore, pick up books of interest, and check their contents. For example, the virtual bookstore construction unit recreates the atmosphere of a realistic bookstore using a 3D model, allowing users to search for books as if they were in a real bookstore. The book recommendation unit introduces books in the virtual bookstore constructed by the virtual bookstore construction unit. For example, the book recommendation unit analyzes a user's internet history and recommends books based on that history. The book recommendation unit also suggests related books based on the user's recently searched keywords and the content of websites they have visited. For example, the book recommendation unit uses a generative AI to analyze the user's internet history and select appropriate books. The discovery promotion unit encourages new discoveries based on the books introduced by the book recommendation unit. For example, the discovery promotion unit utilizes the AI's large amount of accumulated data to encourage new discoveries even in fields in which the user is not personally interested. The discovery promotion unit can also suggest books in fields in which the user is not normally interested, thereby broadening the user's knowledge of those fields. For example, the discovery promotion unit uses the generation AI to suggest books in other related fields based on the user's interests. This allows the book recommendation system according to the embodiment to enable the user to access a variety of books from the comfort of their own home and gain new knowledge and perspectives. For example, by being suggested books in fields in which the user is not normally interested, the user can gain new interests and knowledge. Furthermore, through detailed book information and purchasing support, the user can efficiently obtain books and deepen their knowledge.

[0054] The virtual bookstore construction unit can track user movements and analyze user behavior patterns to automatically adjust optimal book placement. For example, the virtual bookstore construction unit tracks user movements within the virtual bookstore and analyzes how much time users spend on each bookshelf. This allows the placement of popular bookshelves and books to be optimized. The virtual bookstore construction unit also records which books users pick up within the virtual bookstore and which bookshelves they frequently visit, and automatically adjusts book placement based on this data. For example, by analyzing user behavior patterns, if there are many users who are interested in a particular genre or theme, books in that genre can be placed in prominent locations. This allows the book placement to be optimized based on user behavior patterns.

[0055] The virtual bookstore construction unit can personalize the bookstore layout based on the user's past browsing history or purchase history. For example, the virtual bookstore construction unit analyzes the user's past browsing history or purchase history and personalizes the book arrangement in the bookstore based on that data. For example, books in genres that the user frequently reads are placed in prominent locations. The virtual bookstore construction unit also provides a different bookstore layout for each user and arranges books according to individual interests. For example, books by a particular author or series are displayed preferentially. The virtual bookstore construction unit also dynamically changes the book arrangement in the bookstore based on the user's behavior data and places books that are likely to interest the user in prominent locations. This makes it possible to provide a different bookstore layout for each user.

[0056] The virtual bookstore construction unit can use the emotion estimation function to analyze the emotion a user shows toward a specific book and arrange the books in a way that elicits a positive emotional response. The virtual bookstore construction unit, for example, analyzes the emotion a user shows toward a specific book in the virtual bookstore and arranges the books in a way that elicits a positive emotional response. For example, books that the user has shown interest in are arranged in a prominent location. The virtual bookstore construction unit also uses the emotion estimation function to analyze the emotion a user shows toward a specific book in real time and arranges the books in a way that elicits a positive emotional response. For example, the virtual bookstore construction unit dynamically changes the book arrangement in the bookstore based on the user's emotion data and arranges books that elicit a positive emotional response in a prominent location. This makes it possible to optimize the book arrangement based on the user's emotions.

[0057] The virtual bookstore construction unit can arrange not only books but also related video and audio content to provide a multimedia experience. For example, the virtual bookstore construction unit arranges not only books but also related video and audio content in the virtual bookstore, allowing users to enjoy a multimedia experience. For example, it provides a video introducing the book or an audio interview of the author. The virtual bookstore construction unit also arranges video and audio content related to the content of the book in the virtual bookstore, allowing users to gain a deeper understanding of the book's content. For example, it provides documentary videos and audiobooks. The virtual bookstore construction unit also arranges multimedia content in the virtual bookstore, allowing users to enjoy the book's content visually and audibly. For example, it provides content that visualizes parts of the book or audio readings. This allows users to gain a deeper understanding of the book's content.

[0058] The virtual bookstore construction unit can add a real-time chat function and a review sharing function to promote interaction between users. For example, the virtual bookstore construction unit adds a real-time chat function within the virtual bookstore to enable users to exchange opinions about books. For example, users can hold discussions about specific books. The virtual bookstore construction unit also adds a review sharing function within the virtual bookstore to enable users to post book reviews and share them with other users. For example, users can share their impressions and ratings of books they have read. The virtual bookstore construction unit also adds a forum or group function within the virtual bookstore to promote interaction between users, to form communities around specific themes or genres. This allows users to exchange opinions about books.

[0059] The virtual bookstore construction unit can use the emotion estimation function to detect the stress or fatigue the user feels in the virtual bookstore and suggest content or areas where the user can relax. The virtual bookstore construction unit can, for example, use the emotion estimation function to detect the stress or fatigue the user feels in the virtual bookstore in real time and suggest content or areas where the user can relax. For example, it can provide relaxing music or videos. The virtual bookstore construction unit can also detect stress or fatigue in the virtual bookstore based on the user's emotion data and suggest areas where the user can relax. For example, it can provide a quiet reading space or a relaxing interface. The virtual bookstore construction unit can also use the emotion estimation function to detect the stress or fatigue the user feels in the virtual bookstore and suggest content where the user can relax. For example, it can provide relaxing video or audio content. This makes it possible to suggest content or areas where the user can relax.

[0060] The book recommendation unit analyzes not only the user's internet history but also social media posts or comments, allowing for more accurate book recommendations. The book recommendation unit, for example, analyzes social media posts and comments in addition to the user's internet history, allowing for more accurate book recommendations. For example, it recommends books based on keywords mentioned by the user on social media. The book recommendation unit also analyzes social media data to recommend books based on a detailed understanding of the user's interests. For example, it selects books based on information about accounts the user follows and groups the user participates in. The book recommendation unit also analyzes the user's social media posts and comments to recommend related books. For example, it suggests books related to the content the user has posted or the topics on which the user has commented. In this way, more accurate book recommendations are possible by analyzing the user's social media data.

[0061] The book recommendation unit can recommend books by analyzing the user's past book purchase history and review history and understanding the user's reading tendencies in detail. The book recommendation unit, for example, analyzes the user's past book purchase history and understands the user's reading tendencies based on that data to recommend books. For example, it may preferentially suggest books in a particular genre or author. The book recommendation unit may also analyze the user's book review history and recommend books related to books that the user has given high ratings. For example, it may suggest books on the same theme or style. The book recommendation unit may also understand the user's reading tendencies in detail based on the user's past purchase history and review history and make personalized book recommendations. For example, it may suggest books in the user's favorite genre or theme. This allows for a detailed understanding of the user's reading tendencies, enabling more personalized book recommendations.

[0062] When analyzing the user's Internet history, the book recommendation unit can also recommend events or seminars that may interest the user. For example, the book recommendation unit analyzes the user's Internet history and recommends events and seminars that may interest the user based on that data. For example, it suggests events related to a specific theme. The book recommendation unit also recommends events and seminars that the user may be interested in based on the user's search history and browsing history. For example, it suggests seminars in areas that the user is interested in. The book recommendation unit also analyzes the user's Internet history and recommends related events and seminars. For example, it suggests events related to keywords that the user recently searched for. In this way, by recommending events and seminars that interest the user, the user's learning and the range of their interests can be broadened.

[0063] The book recommendation unit may also suggest related online courses or learning resources when recommending a book, thereby broadening the scope of the user's learning. For example, the book recommendation unit may also suggest related online courses or learning resources when recommending a book, thereby broadening the scope of the user's learning. For example, it may suggest online courses related to a specific theme. The book recommendation unit may also analyze the user's internet history and suggest related learning resources when recommending a book. For example, it may suggest online courses in areas that interest the user. The book recommendation unit may also suggest related online courses or learning resources when recommending a book, thereby broadening the scope of the user's learning. For example, it may suggest online courses related to the content of the book. In this way, by suggesting related learning resources when recommending a book, it is possible to broaden the scope of the user's learning.

[0064] The book introduction unit can use the emotion estimation function to analyze the emotion a user shows toward a specific book in real time, and preferentially recommend books that elicit a positive emotional response. The book introduction unit, for example, uses the emotion estimation function to analyze the emotion a user shows toward a specific book in real time, and preferentially recommends books that elicit a positive emotional response. For example, it preferentially suggests books in which the user has shown interest. The book introduction unit also analyzes the emotion a user shows toward a specific book in real time based on the user's emotion data, and preferentially recommends books that elicit a positive emotional response. For example, it preferentially suggests books that the user has enjoyed. The book introduction unit also uses the emotion estimation function to analyze the emotion a user shows toward a specific book in real time, and preferentially recommends books that elicit a positive emotional response. For example, it preferentially suggests books that moved the user. In this way, by recommending books in real time based on the user's emotions, it is possible to provide more satisfying book recommendations.

[0065] The discovery promotion unit can draw new interests by analyzing the user's reading history and intentionally recommending books in less relevant fields. The discovery promotion unit, for example, analyzes the user's reading history and intentionally recommends books in less relevant fields. For example, it suggests books in genres that the user does not usually read. The discovery promotion unit also recommends books in less relevant fields based on the user's reading history to draw new interests. For example, it intentionally suggests books in fields that the user is not interested in. The discovery promotion unit also draws new interests by analyzing the user's reading history and recommending books in less relevant fields. For example, it suggests books in fields that the user is not usually interested in. In this way, it is possible to broaden the user's knowledge by recommending books that draw new interests based on the user's reading history.

[0066] The discovery promotion unit can recommend the latest research papers and academic articles related to the user's field of interest, encouraging the user to dig deeper into knowledge. The discovery promotion unit, for example, recommends the latest research papers and academic articles related to the user's field of interest, encouraging the user to dig deeper into knowledge. For example, it suggests the latest research results related to a topic that interests the user. The discovery promotion unit also recommends academic articles related to the user's field of interest, encouraging the user to dig deeper into knowledge. For example, it suggests the latest research papers in a topic that interests the user. The discovery promotion unit also recommends the latest research papers and academic articles related to the user's field of interest, encouraging the user to dig deeper into knowledge. For example, it suggests academic articles related to a topic that interests the user. In this way, by recommending the latest research papers and academic articles related to the user's field of interest, the user can be encouraged to dig deeper into knowledge.

[0067] The discovery promotion unit can use the emotion estimation function to analyze the emotional reactions shown by the user to books in a new field and preferentially recommend books that elicit a positive reaction. The discovery promotion unit, for example, uses the emotion estimation function to analyze the emotional reactions shown by the user to books in a new field and preferentially recommend books that elicit a positive reaction. For example, it suggests books in a new field in which the user has shown interest. The discovery promotion unit also analyzes the emotional reactions shown by the user to books in a new field based on the user's emotion data and preferentially recommends books that elicit a positive reaction. For example, it suggests books in a new field that the user has enjoyed. The discovery promotion unit also uses the emotion estimation function to analyze the emotional reactions shown by the user to books in a new field and preferentially recommends books that elicit a positive reaction. For example, it suggests books in a new field that the user has been moved by. In this way, it is possible to arouse the user's interest by recommending books in a new field based on the user's emotional reaction.

[0068] When recommending a book in a field that the user is not normally interested in, the discovery promotion unit may simultaneously suggest a documentary or interview video related to that field. For example, when recommending a book in a field that the user is not normally interested in, the discovery promotion unit simultaneously suggests a documentary or interview video related to that field. For example, video content that complements the content of the book is provided. Furthermore, when recommending a book in a field that the user is not normally interested in, the discovery promotion unit suggests a documentary or interview video related to that field to arouse interest. For example, video content related to the theme of the book is provided. Furthermore, when recommending a book in a field that the user is not normally interested in, the discovery promotion unit suggests a documentary or interview video related to that field to arouse interest. For example, video content that complements the content of the book is provided. In this way, when recommending a book in a field that the user is not normally interested in, the discovery promotion unit suggests a related documentary or interview video to arouse interest.

[0069] When recommending books in a new field, the discovery promotion unit can provide commentary and reviews by experts in that field to deepen understanding. For example, when recommending books in a new field, the discovery promotion unit can provide commentary and reviews by experts in that field to deepen the user's understanding. For example, it can provide comments from experts and explanatory videos. Furthermore, when recommending books in a new field, the discovery promotion unit can provide reviews by experts in that field to deepen the user's understanding. For example, it can provide evaluations and opinions from experts. Furthermore, when recommending books in a new field, the discovery promotion unit can provide commentary and reviews by experts in that field to deepen the user's understanding. For example, it can provide comments from experts and explanatory videos. In this way, when recommending books in a new field, the discovery promotion unit can provide commentary and reviews by experts to deepen the user's understanding.

[0070] The discovery promotion unit can use the emotion estimation function to analyze the emotional reactions shown by the user to books in a new field in real time, and preferentially recommend books that elicit a positive emotional reaction. The discovery promotion unit, for example, uses the emotion estimation function to analyze the emotional reactions shown by the user to books in a new field in real time, and preferentially recommend books that elicit a positive emotional reaction. For example, it suggests books in a new field in which the user has shown interest. The discovery promotion unit also analyzes the emotional reactions shown by the user to books in a new field in real time based on the user's emotion data, and preferentially recommends books that elicit a positive emotional reaction. For example, it suggests books in a new field that the user has enjoyed. The discovery promotion unit also uses the emotion estimation function to analyze the emotional reactions shown by the user to books in a new field in real time, and preferentially recommends books that elicit a positive emotional reaction. For example, it suggests books in a new field that the user has been moved by. In this way, it is possible to arouse the user's interest by recommending books in a new field in real time based on the user's emotional reaction.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The virtual bookstore construction unit can track user movements and analyze user behavior patterns to automatically adjust optimal book placement. For example, it can track user movements within the virtual bookstore and analyze how much time a user spends on each bookshelf. This allows it to optimize the placement of popular bookshelves and books. The virtual bookstore construction unit also records which books users pick up within the virtual bookstore and which bookshelves they frequently visit, and automatically adjusts book placement based on this data. For example, it can analyze user behavior patterns and, if there are many users who are interested in a particular genre or theme, it can place books of that genre in prominent locations. This allows it to optimize book placement based on user behavior patterns.

[0073] The virtual bookstore construction unit can personalize the bookstore layout based on the user's past browsing history or purchase history. For example, it can analyze the user's past browsing history and purchase history and personalize the book arrangement in the bookstore based on that data. For example, books in genres that the user frequently reads can be placed in prominent locations. The virtual bookstore construction unit can also provide a different bookstore layout for each user and arrange books according to individual interests. For example, books by a particular author or series can be displayed preferentially. The virtual bookstore construction unit can also dynamically change the book arrangement in the bookstore based on the user's behavior data and place books that are likely to interest the user in prominent locations. This makes it possible to provide a different bookstore layout for each user.

[0074] The virtual bookstore construction unit can use the emotion estimation function to analyze the emotion a user shows toward a specific book and arrange the books in a way that elicits a positive emotional response. For example, the emotion a user shows toward a specific book in the virtual bookstore can be analyzed and a book arrangement that elicits a positive emotional response can be performed. For example, books that the user has shown interest in can be arranged in a prominent location. The virtual bookstore construction unit can also use the emotion estimation function to analyze the emotion a user shows toward a specific book in real time and arrange the books in a way that elicits a positive emotional response. For example, the virtual bookstore construction unit can dynamically change the book arrangement in the bookstore based on the user's emotion data and arrange books that elicit a positive emotional response in a prominent location. This makes it possible to optimize the book arrangement based on the user's emotions.

[0075] The virtual bookstore construction unit can arrange not only books but also related video and audio content to provide a multimedia experience. For example, not only books but also related video and audio content can be arranged in the virtual bookstore, allowing users to enjoy a multimedia experience. For example, a video introducing the book or an audio interview with the author can be provided. The virtual bookstore construction unit can also arrange video and audio content related to the contents of the book in the virtual bookstore, allowing users to gain a deeper understanding of the book's contents. For example, documentary videos and audiobooks can be provided. The virtual bookstore construction unit can also arrange multimedia content in the virtual bookstore, allowing users to enjoy the contents of the book visually and audibly. For example, video content of parts of the book or audio readings can be provided. This allows users to gain a deeper understanding of the book's contents.

[0076] The virtual bookstore construction unit can add real-time chat functions and review sharing functions to promote interaction between users. For example, a real-time chat function can be added to the virtual bookstore to allow users to exchange opinions about books. For example, a discussion can be held about a specific book. The virtual bookstore construction unit can also add a review sharing function to the virtual bookstore to allow users to post book reviews and share them with other users. For example, users can share their impressions and ratings of books they have read. The virtual bookstore construction unit can also add forum and group functions to the virtual bookstore to promote interaction between users, to form communities around specific themes or genres. This allows users to exchange opinions about books.

[0077] The virtual bookstore construction unit can use the emotion estimation function to detect the stress or fatigue a user feels in the virtual bookstore and suggest content or areas where they can relax. For example, the emotion estimation function can be used to detect the stress or fatigue a user feels in the virtual bookstore in real time and suggest content or areas where they can relax. For example, relaxing music or videos can be provided. The virtual bookstore construction unit can also detect stress or fatigue in the virtual bookstore based on the user's emotion data and suggest areas where they can relax. For example, a quiet reading space or a relaxing interface can be provided. The virtual bookstore construction unit can also use the emotion estimation function to detect the stress or fatigue a user feels in the virtual bookstore and suggest content where they can relax. For example, relaxing video or audio content can be provided. This makes it possible to suggest content or areas where the user can relax.

[0078] The book recommendation unit can analyze not only the user's internet history but also social media posts or comments to make more accurate book recommendations. For example, in addition to the user's internet history, the unit can analyze social media posts and comments to make more accurate book recommendations. For example, books can be recommended based on keywords mentioned by the user on social media. The book recommendation unit can also analyze social media data to recommend books based on a detailed understanding of the user's interests. For example, books can be selected based on information about accounts the user follows and groups the user participates in. The book recommendation unit can also analyze the user's social media posts and comments to recommend related books. For example, books related to the content the user has posted or the topics on which the user has commented can be suggested. In this way, more accurate book recommendations can be made by analyzing the user's social media data.

[0079] The book recommendation unit can recommend books by analyzing the user's past book purchase history and review history and understanding the user's reading tendencies in detail. For example, the unit can analyze the user's past book purchase history and understand the user's reading tendencies based on that data to recommend books. For example, it can prioritize suggesting books in a particular genre or by a particular author. The book recommendation unit can also analyze the user's book review history and recommend books related to books that the user has given high ratings. For example, it can suggest books on the same theme or style. The book recommendation unit can also understand the user's reading tendencies in detail based on the user's past purchase history and review history and make personalized book recommendations. For example, it can suggest books in the user's favorite genre or theme. This allows for a detailed understanding of the user's reading tendencies, making it possible to make more personalized book recommendations.

[0080] The book introduction unit can use the emotion estimation function to analyze the emotion a user shows toward a specific book in real time, and preferentially recommend books that elicit a positive emotional response. For example, the emotion estimation function can be used to analyze the emotion a user shows toward a specific book in real time, and preferentially recommend books that elicit a positive emotional response. For example, books in which the user has shown interest can be preferentially suggested. The book introduction unit can also analyze the emotion a user shows toward a specific book in real time based on the user's emotion data, and preferentially recommend books that elicit a positive emotional response. For example, books that the user has enjoyed can be preferentially suggested. The book introduction unit can also use the emotion estimation function to analyze the emotion a user shows toward a specific book in real time, and preferentially recommend books that elicit a positive emotional response. For example, books that moved the user can be preferentially suggested. In this way, books can be recommended in real time based on the user's emotions, enabling book recommendations that provide greater satisfaction.

[0081] The discovery promotion unit can draw new interests by analyzing the user's reading history and intentionally recommending books in less relevant fields. For example, the unit can analyze the user's reading history and intentionally recommend books in less relevant fields. For example, it can suggest books in genres that the user does not usually read. The discovery promotion unit can also draw new interests by recommending books in less relevant fields based on the user's reading history. For example, it can intentionally suggest books in fields that the user is not interested in. The discovery promotion unit can also draw new interests by analyzing the user's reading history and recommending books in less relevant fields. For example, it can suggest books in fields that the user is not usually interested in. In this way, the user's knowledge can be broadened by recommending books that draw new interests based on the user's reading history.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The virtual bookstore construction unit constructs a virtual bookstore. For example, the virtual bookstore construction unit recreates the layout of bookshelves and books in a bookstore in a virtual space, allowing users to browse freely. The virtual bookstore construction unit also allows users to walk around the virtual bookstore, pick up books of interest, and check their contents. For example, the virtual bookstore construction unit recreates the atmosphere of a real bookstore using a 3D model, allowing users to search for books as if they were in a real bookstore. Step 2: The book recommendation unit introduces books in the virtual bookstore constructed by the virtual bookstore construction unit. For example, the book recommendation unit analyzes the user's internet history and recommends books based on that history. The book recommendation unit also suggests related books based on keywords the user recently searched for and the content of websites they visited. For example, the book recommendation unit uses a generation AI to analyze the user's internet history and select appropriate books. Step 3: The discovery promotion unit encourages new discoveries based on the books introduced by the book introduction unit. For example, the discovery promotion unit utilizes the AI's large amount of accumulated data to encourage new discoveries even in fields in which the user is not personally interested. The discovery promotion unit can also suggest books in fields in which the user is not normally interested, thereby expanding the user's knowledge of those fields. For example, the discovery promotion unit uses generative AI to suggest books in other related fields based on the user's interests.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] 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.

[0099] 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.

[0100] 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 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] 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 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] 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 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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. [Explanation of symbols]

[0151] 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 virtual bookstore construction unit that constructs a virtual bookstore; a book introduction unit that introduces books in the virtual bookstore constructed by the virtual bookstore construction unit; a discovery promotion unit that promotes new discoveries based on the books introduced by the book introduction unit. A system characterized by:

2. The virtual bookstore construction unit Tracking user movements and analyzing the user's behavioral patterns to automatically adjust optimal book placement 2. The system of claim 1.

3. The virtual bookstore construction unit In addition to the book, related video and audio content will also be displayed to provide a multimedia experience.

2. The system of claim 1.

4. The book introduction section Analyzing not only a user's internet history but also their social media posts and comments to make more accurate book recommendations 2. The system of claim 1.

5. The discovery promotion unit Analyzing a user's reading history and intentionally recommending books in less relevant fields to spark new interests 2. The system of claim 1.

6. The virtual bookstore construction unit Analyze the emotions users show toward specific books and arrange books that elicit positive emotional responses.

2. The system of claim 1.

7. The book introduction section Analyzes the user's emotional response to books they have read in the past and recommends books that elicit positive emotional responses.

2. The system of claim 1.

8. The discovery promotion unit Analyzes users' emotional reactions to books in new fields and prioritizes recommendations of books that evoke positive reactions 2. The system of claim 1.

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