system

Abstract

Provide a system. The solution is a recommendation method using a generative AI model that presents recommended books based on a user's reading history and interests; location information means for acquiring location information of a user and providing inventory information of the nearest bookstore or library; a trading means for providing an online marketplace for users to trade their books with other users; an event management means for managing schedules of events and workshops and notifying users; a translation means for translating books in a foreign language and providing them in the user's native language; A surprise recommendation method that recommends books from new genres and authors that deviate from traditional reading trends, A system including:

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] In today's society, it is difficult for users to find books that perfectly match their interests, and they are also subject to geographical and time constraints when obtaining books. Furthermore, there is a demand for books written in languages ​​other than one's native language, and for increased exposure to new genres and authors outside of traditional reading habits. However, current systems cannot meet these needs. Therefore, a system that can solve these issues and provide a diverse reading experience is needed. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides the following means: a system including a recommendation means using a generative AI model to present recommended books based on the user's reading history and interests, a location information means acquiring the user's location information and providing inventory information for the nearest bookstore or library, a trading means providing an online marketplace for users to trade books they own with other users, an event management means managing event and workshop schedules and notifying users, a translation means translating books in a foreign language and providing them in the user's native language, and a surprise recommendation means recommending books in new genres or by new authors that deviate from conventional reading trends.

[0006] A "generative AI model" refers to artificial intelligence that uses algorithms to predict and suggest recommended books based on a user's past reading history and interests.

[0007] "Recommendation method" refers to the function of using a generative AI model to recommend and present the most suitable books to users.

[0008] "Location information means" refers to a function that obtains the user's current location and provides inventory information for the nearest bookstore or library based on that information.

[0009] "Transaction means" refers to the functionality of an online marketplace that allows users to buy, sell, lend, or borrow books they own with other users.

[0010] "Event management means" refers to the function of scheduling book-related events and workshops and notifying users.

[0011] "Translation means" refers to a function that translates a book in a foreign language into the user's native language and provides it to the user.

[0012] "Surprise recommendation means" refers to a function that suggests books by new genres or authors to users that are outside of their traditional reading habits. [Brief explanation of the drawings]

[0013] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] 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, a 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), and an APU (Accelerated Processing Unit).

[0017] 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.

[0018] 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.

[0019] 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), Bluetooth (registered trademark), etc.

[0020] 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."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 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.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0025] 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.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0028] 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.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] The system of the present invention combines a generative AI model with various functions to provide customized book suggestions and related information to improve the user's reading experience. The main components of this system include data exchange between a server, terminals, and users.

[0035] System Programming and Processing

[0036] 1. Book Search Navigator

[0037] The server uses generative AI to suggest recommended books based on data acquired from the user, such as their subscription history, genres of interest, and authors. For example, if a user likes "mystery novels," the server uses that data to generate a list of the latest mystery novels and presents it to the user. When a user enters a specific author using the search function, all works related to that author are returned as search results.

[0038] 2. Book Destination Guide

[0039] The device obtains the user's location information and provides inventory information for the nearest bookstore or library. For example, if a user searches for "Harry Potter" and has location information turned on, the server uses the user's location information and search keywords to obtain inventory information for the nearest bookstore or library, and the device displays it to the user. A pin is displayed on a map, and the user can access that location to obtain the book.

[0040] 3. Book Hunting Market

[0041] Users register books they own that they no longer need in the app and wish to trade with other users. This information is sent to the server and registered in the database. When other users search for the book, the server retrieves matching information and notifies the user who wishes to trade. Once negotiations for a trade begin between users and the transaction is concluded, the server saves a log of the transaction.

[0042] 4. Exciting Book Classes

[0043] The server registers information about famous authors and related events in a database and notifies users. When a user registers to participate in an event, the information is sent to the server, and registration is completed. For example, if a "reading group by a famous author" is being held, users are notified of the information and those who wish to participate can register. The server then sends reminders.

[0044] 5. Word Adventurer

[0045] When a user selects a book written in a specific foreign language, the server uses generative AI to translate the book into the user's native language. For example, if the user selects "Le Petit Prince" (French), the server uses generative AI to translate the entire text and sends it to the user. The user's device then displays the translated text, providing a comfortable reading experience.

[0046] 6. Reading Surprise

[0047] The server acquires the user's past reading habits and runs an algorithm to suggest books in different genres or by different authors. For example, for a user who mainly reads "business books," the server might suggest new books such as "poetry collections" or "science fiction novels." This gives the user the opportunity to be exposed to new genres.

[0048] Through these functions, the system of the present invention can provide a diverse and customized reading experience, enhancing users' interest in books. This allows users to easily find and conveniently obtain books they want to read, while also encouraging them to explore new reading areas.

[0049] The processing flow will be explained below.

[0050] 1. Book Search Navigator

[0051] Subject: Server

[0052] Step 1:

[0053] When a user logs in, the device sends the user ID and token to the server.

[0054] Step 2:

[0055] The server retrieves the user's subscription history, genres of interest, and author information from the database.

[0056] Step 3:

[0057] The server uses generative AI to calculate book recommendations based on user data.

[0058] Step 4:

[0059] Based on the calculation results, the server creates a list of recommended books and sends it to the device.

[0060] Step 5:

[0061] The terminal displays the received list to the user.

[0062] 2. Book Destination Guide

[0063] Subject: Terminal

[0064] Step 1:

[0065] The user allows the use of location information.

[0066] Step 2:

[0067] The device obtains the user's current location and sends it to the server.

[0068] Step 3:

[0069] The user enters a search keyword and sends it to the server.

[0070] Step 4:

[0071] The server uses the location information and search keywords to retrieve inventory information from the database of the nearest bookstore or library.

[0072] Step 5:

[0073] The server transmits the acquired inventory information to the terminal.

[0074] Step 6:

[0075] The device displays a pin on a map and shows the user the inventory information of the nearest bookstore or library.

[0076] 3. Book Hunting Market

[0077] Subject: User and Server

[0078] Step 1:

[0079] The user inputs information about the books he owns and sends it to the server.

[0080] Step 2:

[0081] The server registers the book information received from the user in a database.

[0082] Step 3:

[0083] Other users search for specific books and send requests to the server.

[0084] Step 4:

[0085] The server searches the database, retrieves the matching book information, and returns it to the requesting user.

[0086] Step 5:

[0087] When a user expresses interest in a particular book and requests a transaction, the server notifies both users.

[0088] Step 6:

[0089] The users negotiate a transaction and the transaction is concluded.

[0090] 4. Exciting Book Classes

[0091] Subject: Server

[0092] Step 1:

[0093] The server receives event information from authors and related parties and registers it in a database.

[0094] Step 2:

[0095] The server notifies the user of the registered event information.

[0096] Step 3:

[0097] When a user wishes to participate, they send a request to the server.

[0098] Step 4:

[0099] The server approves the request and registers the user.

[0100] Step 5:

[0101] The server sends a reminder to the user the day before the event.

[0102] 5. Word Adventurer

[0103] Subject: Server

[0104] Step 1:

[0105] The user selects the book they wish to translate and sends a request to the server.

[0106] Step 2:

[0107] The server launches a generative AI to translate the book text into the user's native language.

[0108] Step 3:

[0109] The server generates the translated text data and sends it to the user.

[0110] Step 4:

[0111] The user's device displays the translated text.

[0112] 6. Reading Surprise

[0113] Subject: Server

[0114] Step 1:

[0115] The server retrieves the user's reading history from the database.

[0116] Step 2:

[0117] The server uses generative AI to run algorithms that recommend new genres and authors outside of the user's interests.

[0118] Step 3:

[0119] The server creates a surprise recommendation list based on the calculation results and sends it to the user.

[0120] Step 4:

[0121] The user's device will display suggestions, encouraging a new reading experience.

[0122] The above processing steps realize a system that can provide a customized reading experience to the user.

[0123] Example 1

[0124] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0125] For many modern book lovers, selecting and acquiring the right books to enhance their reading experience is a key challenge. In addition, they have diverse needs, such as attending events of interest, trading unwanted books, exploring new genres, and translating foreign language books. Conventional systems must address these needs individually, requiring users to use multiple applications and services, which is inconvenient. Furthermore, recommendations based on a user's reading history and interests often lack accuracy. It is desirable to solve these issues with a single system.

[0126] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0127] In this invention, the server includes a recommendation unit using a generative AI model to present recommended books based on the user's reading history and interests, a location information unit that acquires the user's location information and provides inventory information for the nearest bookstore or library, a transaction unit that provides an online marketplace for users to trade books they own with other users, an event management unit that manages event and workshop schedules and notifies users, a translation unit that translates books in foreign languages ​​and provides them in the user's native language, a surprise recommendation unit that recommends books in new genres or by authors outside of traditional reading trends, a recommendation generation unit that inputs prompts into the generative AI model to generate a list of recommended books, and a transaction storage unit that stores transaction details and logs when a book transaction between users is completed. This allows users to satisfy a wide range of reading-related needs with a single system, enabling highly accurate book recommendations, convenient book search and purchase, event participation, exploration of new genres, and translation of foreign language books.

[0128] 1. "Recommendation" is a feature that uses a generative AI model to suggest books based on a user's reading history and interests.

[0129] 2. "Location information means" is a function that obtains the user's location information and provides inventory information of the nearest bookstore or library.

[0130] 3. "Transaction Means" means a feature that provides an online marketplace for users to trade books they own with other users.

[0131] 4. "Event management means" is a function that manages the schedule of events and workshops and notifies users.

[0132] 5. "Translation means" refers to the function of translating books in foreign languages ​​and providing them in the user's native language.

[0133] 6. "Surprise recommendation" is a function that recommends books in new genres or by new authors that deviate from traditional reading trends.

[0134] 7. "Recommendation generation means" is a function that generates a book recommendation list by inputting a prompt sentence into the generative AI model.

[0135] 8. "Transaction storage means" is a function that stores transaction details and logs when a book transaction is concluded between users.

[0136] The present invention provides a system that combines a generative AI model with various functions to improve the user's reading experience. Specific embodiments of the system are described below.

[0137] System Configuration

[0138] This system functions mainly through the exchange of data between the server, terminals, and users. The following hardware and software are used:

[0139] Hardware: High-performance cloud server (e.g., AWS (registered trademark), Google (registered trademark) Cloud), smartphone with GPS function

[0140] Software: Generative AI models (e.g., GPT-4®), databases (e.g., MySQL®), mapping applications (e.g., Google Maps), event management systems (e.g., Eventbrite), notification systems (e.g., Firebase), translation APIs (e.g., Google Translate API)

[0141] Recommendation method

[0142] The server uses a generative AI model to recommend books based on the user's reading history and interests. When a user logs in to the app and enters their genre and author information, the server collects that data and inputs prompts into the generative AI model to generate a list of book recommendations.

[0143] Example prompt sentence:

[0144] A user's subscription history and genre interest is mystery novels. Generate a list of the latest mystery novels.

[0145] This recommendation list is sent to the user's terminal, and the recommended book list is displayed on the screen.

[0146] Location information means

[0147] The device acquires the user's location information and provides inventory information for the nearest bookstore or library. When the user enters search keywords and location information, the server uses that information to gather inventory information for the nearest bookstore or library and sends it to the user's device. A pin is displayed on the map, and the user can access that location to obtain the book.

[0148] Example prompt sentence:

[0149] A user searches for Harry Potter and is located in Tokyo. Please provide availability information for the nearest bookstore or library.

[0150] Trading Instruments

[0151] Users register unwanted books on the online marketplace and wish to trade with other users. The server stores the registered book information in a database, and when other users search, matching book information is provided. When a trade is made, the server stores the transaction details and logs.

[0152] Event Management Methods

[0153] The server manages schedules for famous authors and related events and notifies users. When a user applies to participate in an event, the information is sent to the server and the registration is completed. For example, if a "reading group of famous authors" is being held, the information is notified to users, and users who wish to participate can apply.

[0154] Example prompt sentence:

[0155] A reading group for a famous author is being held. Please explain the process for notifying users and accepting applications to participate.

[0156] Translation tools

[0157] The server uses a generative AI model to translate a foreign language book selected by the user and provide it in the user's native language. For example, if the user selects "Le Petit Prince" (French), the server translates the entire text and sends it to the user's device.

[0158] Example prompt sentence:

[0159] Please translate Le Petit Prince (French) into the user's native language (Japanese).

[0160] Surprise recommendation method

[0161] The server will suggest books from different genres and authors based on the user's past reading habits. For example, if a user primarily reads business books, the server might suggest new books such as poetry collections or science fiction novels, providing an opportunity for a new reading experience.

[0162] Example prompt sentence:

[0163] For a user who likes business books, suggest books in different genres, such as poetry collections and science fiction novels.

[0164] Recommendation generator and transaction store

[0165] The server inputs prompts into the generative AI model to generate a list of recommended books. When a book transaction is completed between users, the server stores the transaction details and logs in a database.

[0166] This allows users to satisfy a wide range of reading-related needs in one system, providing accurate book recommendations, convenient book search and purchase, event participation, exploring new genres, and foreign language book translation.

[0167] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0168] Recommendation method

[0169] Step 1:

[0170] A user logs in to the app and enters the genres and author information they are interested in. This information is sent from the app to the server. The input data includes the genres and author information selected by the user.

[0171] Step 2:

[0172] The server collects the user's subscription history and input data. It retrieves past subscription history from the database and integrates it with newly entered genre and author information.

[0173] Specific behavior:

[0174] The server queries the database to retrieve the subscription history associated with the user ID, and then adds the newly entered genre and author information to it.

[0175] Step 3:

[0176] The server inputs the following prompt sentence into the generative AI model to generate a list of book recommendations:

[0177] A user's subscription history and genre interest is mystery novels. Generate a list of the latest mystery novels.

[0178] A generative AI model creates a recommendation list based on this prompt.

[0179] Step 4:

[0180] The server sends the generated recommendation list to the user's device via an API, and the output data includes the recommended book list.

[0181] Step 5:

[0182] The device displays a list of recommended books to the user, who can then view the recommended books on the screen and click on links for more information or to purchase.

[0183] Location information means

[0184] Step 1:

[0185] The user enters a search term and location information. The user turns on location services and enters the search term into the app.

[0186] Step 2:

[0187] The device uses the GPS function to obtain the user's current location. This location information is sent to the server. The GPS information of the current location is included as input data.

[0188] Specific behavior:

[0189] The device activates the GPS sensor, acquires the current location, and sends it to the server.

[0190] Step 3:

[0191] The server collects inventory information from the nearest bookstore or library based on the location information and search keywords, and sends a query to the inventory management system to obtain the inventory information for the corresponding book.

[0192] Specific behavior:

[0193] The server queries a database or API to retrieve inventory information that matches the location and search keywords.

[0194] Step 4:

[0195] The server sends the collected inventory information and its location to the terminal. The output data includes the inventory information and location data.

[0196] Step 5:

[0197] The device will use a map app to display the location of the bookstore or library you retrieved, and a pin will appear on the map, allowing you to visit the location and get the book.

[0198] Trading Instruments

[0199] Step 1:

[0200] The user registers unwanted books in the app and requests a transaction. The user enters the book information and sends the data to the server.

[0201] Step 2:

[0202] The server stores the registered book information in a database. Input data includes the book title, condition, price, contact information, etc.

[0203] Specific behavior:

[0204] The server registers the book information in the database using an INSERT query.

[0205] Step 3:

[0206] Other users search for specific books by entering a search query and sending it to the server.

[0207] Step 4:

[0208] The server retrieves book information that matches the search query from the database, and sends the retrieved book information to the user via API.

[0209] Specific behavior:

[0210] The server sends a SELECT query to the database and retrieves the search results.

[0211] Step 5:

[0212] The server notifies the user of the matching book information, and the search results are displayed on the user's device.

[0213] Step 6:

[0214] When a transaction negotiation begins between users and the transaction is concluded, the server stores the transaction details and log in the database. The output data includes the transaction details and log data.

[0215] Specific behavior:

[0216] The server records the transaction information in the database using an INSERT query.

[0217] Event Management Methods

[0218] Step 1:

[0219] The server registers the schedule of famous authors and related events in a database and notifies the user. The event information is stored in the database.

[0220] Specific behavior:

[0221] The server registers the event information in the database with an INSERT query and sends the information to the notification system.

[0222] Step 2:

[0223] A user applies to participate in an event. The application information is sent to the server and stored in a database.

[0224] Step 3:

[0225] The server completes the registration and sends a confirmation message to the user. The output data includes the registration confirmation message.

[0226] Step 4:

[0227] The server sends a reminder before the event, and the event reminder message is displayed on the user's device.

[0228] Translation tools

[0229] Step 1:

[0230] A user selects a particular foreign language book and submits that information to a server, including the book's foreign language text as input data.

[0231] Step 2:

[0232] The server translates the foreign language book using a generative AI model. The generative AI model is given the following prompt:

[0233] Please translate Le Petit Prince (French) into the user's native language (Japanese).

[0234] The model generates the translation result.

[0235] Step 3:

[0236] The server sends the translated book information to the user's device, and the output data includes the translated text.

[0237] Step 4:

[0238] The device displays the translated text to the user, who can comfortably read the text in their native language.

[0239] Surprise recommendation method

[0240] Step 1:

[0241] The server analyzes the user's past reading habits, retrieves past subscription history from the database, and performs the analysis.

[0242] Specific behavior:

[0243] The server queries the database to analyze the subscription history associated with the user ID.

[0244] Step 2:

[0245] The server inputs the following prompt sentence into the generative AI model to recommend new books that deviate from the user's preferences.

[0246] For a user who likes business books, suggest books in different genres, such as poetry collections and science fiction novels.

[0247] The model creates a new recommendation list.

[0248] Step 3:

[0249] The server sends the generated new recommendation list to the user's device, and the output data includes the new genre and author recommendation list.

[0250] Step 4:

[0251] The device displays a list of newly recommended books to the user, giving the user the opportunity to discover books in a new genre.

[0252] (Application example 1)

[0253] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0254] Conventional reading support systems are limited to recommending books based on a user's interests and reading history, and providing book inventory information. They lack comprehensive support for efficient book searches within bookstores and for improving the user experience. Furthermore, they lack the integration of a wide range of services, such as trading books owned by users, managing event information, and translating foreign language books. As a result, users are forced to use various services individually, resulting in a lack of a consistent reading experience.

[0255] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0256] In this invention, the server includes a recommendation unit using a generative AI model to present recommended books based on the user's reading history and interests, a location information unit that acquires the user's location information and provides inventory information for the nearest bookstore or library, a trading unit that provides an online marketplace for the user to trade books owned by the user with other users, an event management unit that manages event and workshop schedules and notifies the user, a translation unit that translates books in foreign languages ​​and provides them in the user's native language, a surprise recommendation unit that recommends books in new genres or by new authors outside of traditional reading habits, and a smartphone application provision unit that assists navigation within the bookstore and suggests books based on the user's preferences. This allows the user to have a consistent and comprehensive reading experience.

[0257] A "generative AI model" is an algorithm or program that automatically suggests books based on a user's reading history and interests.

[0258] A "recommendation tool" is a mechanism or method for using a generative AI model to suggest suitable books to a user.

[0259] "Location information means" refers to a system or device that acquires the user's current location and provides inventory information for the nearest bookstore or library based on that information.

[0260] "Exchange vehicle" means a method or system that provides an online marketplace for users to trade or sell their books with other users.

[0261] An "event management means" is a method or system for managing the schedule of events or workshops and notifying users.

[0262] A "translation means" is a method or system for automatically translating a book written in a foreign language into a user's native language and providing the results to the user.

[0263] A "surprise recommendation method" is a mechanism or method for recommending books in new genres or by new authors that are outside the user's usual reading habits.

[0264] "Means for providing smartphone applications" refers to a method or system for providing smartphone applications that support efficient book searches and navigation within a bookstore and suggest books based on the user's preferences.

[0265] This invention is a system for improving users' reading experience by providing customized book suggestions and related information using a generative AI model. The main components of this system include data exchange between a server, a terminal, and a user.

[0266] The system has the following main functions:

[0267] 1. Recommendation method:

[0268] The server uses a generative AI model to suggest books based on the user's reading history and interests. This allows users to easily find books that match their interests. An example of a prompt is, "Please suggest books based on mystery novels I've read in the past."

[0269] 2. Location information means:

[0270] The server uses the location information obtained from the device to provide inventory information for the nearest bookstore or library. This function allows users to quickly check where the book they are looking for is in stock, making book searches in bookstores more efficient.

[0271] 3. Trading instruments:

[0272] It provides an online marketplace for users to trade their books with other users, allowing them to give away or sell books they have finished reading to other users. This information is sent to a server and registered in a database.

[0273] 4. Event Management Measures:

[0274] The server manages schedules for events and workshops and notifies users. This function allows users to find out about book clubs by famous authors and related events, and to apply to participate.

[0275] 5. Translation Methods:

[0276] The server translates books in a foreign language into the user's native language and provides the results to the user. This allows the user to enjoy a variety of books across language barriers. As a specific example, the server may translate "Le Petit Prince" (French) into the user's native language and provide it to the user.

[0277] 6. Surprise Recommendation:

[0278] The server suggests books from new genres or authors that deviate from the user's usual reading habits. This allows the user to discover new reading pleasures. An example of a prompt sentence is "Please suggest books from new genres or authors that deviate from the user's usual reading habits."

[0279] 7. Means of providing smartphone applications:

[0280] We provide a smartphone application that assists users in navigating bookstores and guides them to the location of desired books. This application allows users to quickly find specific books in bookstores. Specifically, it provides navigation on a store map based on the user's location information.

[0281] To implement this system, a smartphone is required as hardware. On the software side, Python programming, an API for generative AI models, and Geopy for handling geographic information are used. The server processes this data, generates the information to be provided to the user, and sends it to the device. Specific examples of prompts used include those that suggest books based on the user's interests and reading history, such as the following:

[0282] "Please suggest books based on mystery novels you've read in the past."

[0283] "Generate new book lists based on your genre interests."

[0284] "I'm looking for a work by a particular author. Please list all related works."

[0285] This system allows users to have a consistent and comprehensive reading experience.

[0286] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0287] Step 1: Receive the user request

[0288] A user opens the application and requests book recommendations, including input about the user's reading history, genre interests, and specific author information. This information is then sent to the server.

[0289] Step 2: Data processing by generative AI models

[0290] The server inputs the received user data into a generative AI model and generates a list of recommended books using prompts. The input data includes past reading history and genre interests, and the output data is a list of recommended books. An example prompt is: "Please suggest books based on mystery novels I've read in the past."

[0291] Step 3: Generate and submit a recommendation list

[0292] The server sends the list of recommended books output by the generative AI model to the device. In this process, the recommended book list is sent to the user's smartphone and displayed on the device.

[0293] Step 4: Obtaining location information

[0294] The user turns on location information to search for a specific book. This input includes the user's current location and the book they are looking for. The device sends the location information to the server.

[0295] Step 5: Retrieve and display inventory information

[0296] The server retrieves inventory information from the database of the nearest bookstore or library using the user's location information and search keywords. Based on this information, the server sends the inventory information to the terminal, which displays it to the user. The output includes the book's location and inventory status.

[0297] Step 6: Performing Navigation

[0298] A user enters a bookstore and uses an application to locate a particular book. The input includes the book's ID and the user's current location. The device navigates the book's location within the store and provides the user with specific directions. The output includes the book's location (aisle number and shelf number).

[0299] Step 7: Request a translation

[0300] A user selects a foreign language book and requests that it be translated into their native language. The server uses a generative AI model to translate the foreign language book into the user's native language and sends the text to the user. The input data is the original text, and the output data is the translated text.

[0301] Step 8: Event Information Notification

[0302] The server uses an event management mechanism to notify users of related events. When a user registers to participate in an event, the information is sent to the server, which manages the schedule. Input data includes details of the event and the user's response, and output data includes confirmation of participation in the event.

[0303] These steps will enable us to provide a comprehensive reading experience and improve user convenience and satisfaction.

[0304] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0305] The system of the present invention combines a generative AI model and an emotion engine to provide customized book suggestions and related information to enhance the user's reading experience. This system is realized through the exchange of data between a server, terminals, and users.

[0306] System Programming and Processing

[0307] 1. Book Search Navigator

[0308] The server uses generative AI to suggest recommended books based on data obtained from the user, such as their subscription history and their favorite genres and authors. For example, if a user likes "mystery novels," the server uses that data to generate a list of the latest mystery novels and presents it to the user. When a user enters a specific author using the search function, all works related to that author are returned as search results.

[0309] 2. Book Destination Guide

[0310] The device obtains the user's location information and provides inventory information for the nearest bookstore or library. For example, if a user searches for "Harry Potter" and has location information turned on, the server uses the user's location information and search keywords to obtain inventory information for the nearest bookstore or library and displays it on the device. A pin is displayed on a map, allowing the user to access that location and obtain the book.

[0311] 3. Book Hunting Market

[0312] Users register books they own that they no longer need in the app and wish to trade with other users. This information is sent to the server and registered in the database. When other users search for the book, the server retrieves matching information and notifies the user who wishes to trade. Once negotiations for a trade begin between users and the transaction is concluded, the server saves a log of the transaction.

[0313] 4. Exciting Book Classes

[0314] The server registers event information from famous authors and related parties in a database and notifies users. When a user registers to participate in an event, the information is sent to the server and registration is completed. For example, if a "reading group of famous authors" is being held, the information is notified to users, and users who wish to participate can register. The server then sends reminders.

[0315] 5. Word Adventurer

[0316] When a user selects a book written in a specific foreign language, the server uses generative AI to translate the book into the user's native language. For example, if the user selects "Le Petit Prince" (French), the server uses generative AI to translate the entire text and sends it to the user. The user's device displays the translated text, providing a comfortable reading experience.

[0317] 6. Reading Surprise

[0318] The server retrieves the user's past reading habits from a database and runs an algorithm to suggest books in different genres or by different authors. For example, if a user primarily reads business books, the server might suggest new books such as poetry collections or science fiction novels. This gives users the opportunity to discover new genres.

[0319] 7. Emotion Engine

[0320] The server uses an emotion engine to recognize the user's emotions and recommends books based on those emotions. As a specific example, when a user expresses their emotions using a camera or microphone, the device analyzes the user's facial expressions and tone of voice and sends the data to the server. The server then uses an emotion engine to analyze the emotion data and recommends books that best suit the user's mood and emotions.

[0321] Example of emotion engine

[0322] For example, if a user feels tired, the emotion engine will recommend books in relaxing genres, such as essays or short stories. Conversely, if the user feels excited, it will recommend books in genres such as suspense or action. In this way, book suggestions tailored to the user's current emotional state provide a more personalized reading experience.

[0323] By combining each of the above functions with an emotion engine, the system of the present invention can meet the diverse needs of users and provide a more highly customized reading experience.

[0324] The processing flow will be explained below.

[0325] 1. Book Search Navigator

[0326] Subject: Server

[0327] Step 1:

[0328] When a user logs in, the device sends the user ID and token to the server.

[0329] Step 2:

[0330] The server retrieves the user's subscription history, genres of interest, and author information from the database.

[0331] Step 3:

[0332] The server uses generative AI to calculate book recommendations based on user data.

[0333] Step 4:

[0334] Based on the calculation results, the server creates a list of recommended books and sends it to the device.

[0335] Step 5:

[0336] The terminal displays the received list to the user.

[0337] 2. Book Destination Guide

[0338] Subject: Terminal

[0339] Step 1:

[0340] The user allows the use of location information.

[0341] Step 2:

[0342] The device obtains the user's current location and sends it to the server.

[0343] Step 3:

[0344] The user enters a search keyword and sends it to the server.

[0345] Step 4:

[0346] The server uses the location information and search keywords to retrieve inventory information from the database of the nearest bookstore or library.

[0347] Step 5:

[0348] The server transmits the acquired inventory information to the terminal.

[0349] Step 6:

[0350] The device displays a pin on a map and shows the user the inventory information of the nearest bookstore or library.

[0351] 3. Book Hunting Market

[0352] Subject: User and Server

[0353] Step 1:

[0354] The user inputs information about the books he owns and sends it to the server.

[0355] Step 2:

[0356] The server registers the book information received from the user in a database.

[0357] Step 3:

[0358] Other users search for specific books and send requests to the server.

[0359] Step 4:

[0360] The server searches the database, retrieves the matching book information, and returns it to the requesting user.

[0361] Step 5:

[0362] When a user expresses interest in a particular book and requests a transaction, the server notifies both users.

[0363] Step 6:

[0364] The users negotiate a transaction and the transaction is concluded.

[0365] 4. Exciting Book Classes

[0366] Subject: Server

[0367] Step 1:

[0368] The server receives event information from authors and related parties and registers it in a database.

[0369] Step 2:

[0370] The server notifies the user of the registered event information.

[0371] Step 3:

[0372] When a user wishes to participate, they send a request to the server.

[0373] Step 4:

[0374] The server approves the request and registers the user.

[0375] Step 5:

[0376] The server sends a reminder to the user the day before the event.

[0377] 5. Word Adventurer

[0378] Subject: Server

[0379] Step 1:

[0380] The user selects the book they wish to translate and sends a request to the server.

[0381] Step 2:

[0382] The server launches a generative AI to translate the book text into the user's native language.

[0383] Step 3:

[0384] The server generates the translated text data and sends it to the user.

[0385] Step 4:

[0386] The user's device displays the translated text.

[0387] 6. Reading Surprise

[0388] Subject: Server

[0389] Step 1:

[0390] The server retrieves the user's reading history from the database.

[0391] Step 2:

[0392] The server uses generative AI to run algorithms that recommend new genres and authors outside of the user's interests.

[0393] Step 3:

[0394] The server creates a surprise recommendation list based on the calculation results and sends it to the user.

[0395] Step 4:

[0396] The user's device will display suggestions, encouraging a new reading experience.

[0397] 7. Emotion Engine

[0398] Subject: Server and terminal

[0399] Step 1:

[0400] The user turns on the camera and microphone and allows emotional data capture.

[0401] Step 2:

[0402] The device captures the user's facial expressions and tone of voice in real time and sends the data to a server.

[0403] Step 3:

[0404] The server uses an emotion engine to analyze the received emotion data.

[0405] Step 4:

[0406] Based on the emotional data, the server uses a generative AI model to recommend books that best fit the user's current emotional state.

[0407] Step 5:

[0408] The server creates a recommendation list and sends it to the terminal.

[0409] Step 6:

[0410] The terminal displays a recommendation list based on the received emotions to the user.

[0411] The above specific processing steps enable highly customized reading suggestions tailored to the user's emotional state, allowing users to easily find the book that best suits their mood and emotions at any given time.

[0412] Example 2

[0413] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0414] Conventional book recommendation systems are limited to recommending books based on users' reading history and interests, and lack recommendations based on new genres or emotions. Furthermore, they lack an integrated system that allows users to conveniently check inventory by location information when searching for specific books, trade books with other users, and manage events and workshops, making it difficult to meet the diverse needs of users.

[0415] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0416] In this invention, the server includes a recommendation means using a generative AI model to present recommended books based on the user's reading history and interests, a location information means for acquiring the user's location information and providing inventory information of the nearest retailer or library facility, a trading means for providing an online trading venue for the user to trade books owned by the user with other users, an event management means for managing event and workshop schedules and notifying the user, a translation means for translating books in foreign languages ​​and providing them in the user's native language, a surprise recommendation means for recommending books in new genres or by new authors that are outside of traditional reading trends, and an emotion evaluation means for recognizing the user's emotions based on an emotion engine and recommending books that match those emotions. This makes it possible to meet the diverse needs of users and provide a personalized reading experience.

[0417] A "generative AI model" is an artificial intelligence model that generates a list of recommended books based on a user's reading history and interests.

[0418] A "recommendation method" is a method for recommending books suitable for a user using a generative AI model.

[0419] The "location information means" is a means for acquiring the user's location information and providing inventory information of the nearest retail store or library facility.

[0420] A "trading means" is a means for providing an online trading space for a user to trade books with other users.

[0421] The "event management means" is a means for managing schedules of events and workshops and notifying users of the schedules.

[0422] "Translation means" refers to a means for translating a book in a foreign language into the user's native language and providing it to the user.

[0423] A "surprise recommendation method" is a method for recommending to a user books in new genres or by new authors that deviate from traditional reading habits.

[0424] The "emotion evaluation means" is a means for recognizing the user's emotions using an emotion engine and recommending books that match those emotions.

[0425] The system of the present invention combines a generative AI model, a location information acquisition system, an online trading platform, an event management system, a translation function, a surprise recommendation function, and an emotion evaluation system to meet the diverse reading needs of users. Each component of the system is described in detail below.

[0426] 1. Recommendation method

[0427] The server uses a generative AI model to generate a book list based on the user's reading history and interests. The user inputs their genres and authors of interest through the app interface, and their device sends that information to the server. The server then generates prompts and inputs them into the generative AI model.

[0428] As a concrete example, if the user is interested in "mystery novels," the following prompt sentence may be used:

[0429] Based on your users' reading history and interests, create a list of the latest mystery novels, along with related information and reviews.

[0430] The generated book list is sent to the user's terminal along with additional information and displayed on the screen.

[0431] 2. Location information means

[0432] The device acquires the user's current location and provides inventory information for the nearest retailer or library. When the user searches for a specific book with location information turned on, the device sends that information to the server. The server then consults a database to collect inventory and location information and displays it to the user.

[0433] For example, if you searched for "Harry Potter," you would use the following prompt:

[0434] The user's current location is a specific location. Please provide information about retailers and libraries that stock Harry Potter. Please also display map information.

[0435] As a result, facilities with inventory are displayed on the map and the user can access the location.

[0436] 3. Trading Instruments

[0437] We provide an online trading platform where users can trade unwanted books with other users. Users input information about the book they wish to trade, and their terminals send it to the server. The server registers the information in a database, and when other users search for the same book, it matches them.

[0438] 4. Event Management Tools

[0439] The server has the function of registering event information from authors and event organizers in a database and notifying users. When a user registers to participate in an event, the information is sent to the server and registration is completed. Before the event begins, the server sends a reminder to the user.

[0440] 5. Translation Methods

[0441] When a user selects a book written in a specific foreign language, the server uses a generative AI model to translate the book. After the user selects a book, the device sends the information to the server. The server generates a translation prompt, inputs it into the AI ​​model, and obtains the translation result.

[0442] For example, if the user selects "Le Petit Prince (French)", use the following prompt text:

[0443] Please translate this French book into your native language.

[0444] The translated text is sent to the terminal and displayed to the user.

[0445] 6. Surprise Recommendations

[0446] The server suggests books from new genres and authors that are outside the user's past reading habits, giving the user the opportunity to be exposed to new genres.

[0447] 7. Emotional Assessment Instrument

[0448] The server uses an emotion engine to recognize the user's emotions and recommends books based on those emotions. When the user expresses their emotions using the camera or microphone, the device sends the data to the server for emotion analysis.

[0449] As a concrete example, if it is determined that the user is tired, the following prompt sentence is used:

[0450] List books in genres that users find relaxing.

[0451] The server provides the list obtained from the generative AI model to the user.

[0452] As described above, the system of the present invention can provide users with a personalized and sophisticated reading experience by flexibly combining each function.

[0453] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0454] "Book Search Navigator" program processing flow

[0455] Step 1: Accepting User Input

[0456] Users input genres and authors of interest through the app's interface. The input data is temporarily stored on the user's device (input). Specifically, the user enters "mystery novels" or "a specific author's name" into the app's search bar.

[0457] Step 2: Send data

[0458] The terminal sends the user's input data to the server (output). Specifically, when the send button on the terminal is pressed, the input data is sent to the back-end server.

[0459] Step 3: Data analysis

[0460] The server receives the transmitted data and generates a prompt based on the user's interests (input). The data is analyzed using text mining and natural language processing to communicate with the generative AI model. For example, a prompt might be created that reads, "Generate a list of the latest mystery novels based on the user's reading history and interests."

[0461] Step 4: Query the AI ​​model

[0462] The server inputs the generated prompt sentence into the generative AI model and obtains a list of appropriate books from the generative AI model (output). Specifically, the generative AI model generates a book list based on the prompt sentence and returns it to the server.

[0463] Step 5: List Generation

[0464] The server compares the book list returned by the generative AI model with its internal database and adds additional information such as reviews and ratings for each book (input and output). Specifically, it retrieves review information from the database and merges it with the retrieved book list.

[0465] Step 6: Sending the results

[0466] The server then sends the final book list and additional information to the user's device (output). Specifically, it converts the list into the required data format and sends it to the device using an API.

[0467] Step 7: Display to the user

[0468] The device displays the received book list and additional information in response to the user's request (input). Specifically, the book list is displayed in the app's UI, and when the user clicks, detailed information is displayed in a pop-up.

[0469] "Book Whereabouts Guide" program processing flow

[0470] Step 1: Accepting User Input

[0471] The user enters search keywords for a specific book within the app and turns on location services. The entered data is temporarily stored on the device (input). For example, the user enters "Harry Potter" as a search keyword.

[0472] Step 2: Send data

[0473] The device sends the search keywords and location information to the server (output). Specifically, when the user presses the send button, the device sends the GPS information and search keywords to the server.

[0474] Step 3: Data analysis

[0475] The server receives the location information and search keywords and searches the database for inventory information of the nearest retail store or library facility (input and output).Specifically, it queries facilities within a certain radius from the user's current location and retrieves inventory information that matches the search keywords from the database.

[0476] Step 4: Result generation

[0477] The server generates a list of the nearest retail stores and library facilities based on the location information and search keywords, and adds inventory information to the list (input and output).The server attaches inventory status to the acquired facility information and generates a list.

[0478] Step 5: Sending the results

[0479] The server sends the generated list and inventory information to the user's device (output). Specifically, it uses an API to convert the data into a display format and sends it to the device.

[0480] Step 6: Display to the user

[0481] The device displays the received list and inventory information along with a map to the user (input). Specifically, it uses the app's map function to map the facility's location and displays an icon indicating whether the item is in stock.

[0482] "Book Search Market" program processing flow

[0483] Step 1: Accepting User Input

[0484] The user enters information about the book they wish to trade into the app, including the book's title, author, condition, price, etc. (Input). The entered data is temporarily stored on the device.

[0485] Step 2: Send data

[0486] The terminal sends the entered book information to the server (output). When the user presses the "Register" button, the terminal sends the book information to the server.

[0487] Step 3: Database registration

[0488] The server receives the book information and registers it in the database (input and output). Specifically, it adds a new book record to the database.

[0489] Step 4: Search by other users

[0490] When other users search for a particular book, the keywords are sent to the server (input). Specifically, the user enters the keywords into the search bar and executes the search.

[0491] Step 5: Database Matching

[0492] The server matches the search keywords with the desired book information in the database and presents the matching books to the user who searches (input and output). Specifically, it queries the book records that match the keywords and generates the results.

[0493] Step 6: Submit matches

[0494] The server notifies the searched user and the user who wishes to trade of the matching book information (output). Specifically, it sends a notification email or an in-app notification to the user who wishes to trade.

[0495] Step 7: Negotiate the deal

[0496] A deal is negotiated between two users, who use the in-app chat feature to discuss the price and details of the deal (input and output).

[0497] Step 8: Keep a transaction log

[0498] When a transaction is completed, the server saves the transaction log in the database (input and output) and records the transaction details in the transaction log table.

[0499] (Application example 2)

[0500] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0501] In recent years, electronic payment services and online book sales platforms have become popular, but simply purchasing a book is not enough to enrich the user's reading experience. There is a need for a system that can recommend books and support purchases in an appropriate and timely manner based on the user's individual interests and emotional state. However, existing systems lack advanced customization based on the user's emotions and reading habits, and are therefore unable to provide the optimal reading experience for the user. To solve this problem, a system is needed that can recommend books based on the user's reading history, interests, and emotional state, and then allow for electronic payment.

[0502] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a recommendation unit using a generative AI model to present recommended books based on the user's reading history and interests; a location information unit that acquires the user's location information and provides inventory information for the nearest bookstore or library; a transaction unit that provides an online marketplace for the user to trade books owned by the user with other users; an event management unit that manages event and workshop schedules and notifies the user; a translation unit that translates books in foreign languages ​​and provides them in the user's native language; a surprise recommendation unit that recommends books in new genres or by authors that deviate from traditional reading habits; an emotion recommendation unit that analyzes the user's emotional state and recommends books based on the user's emotions; and an electronic payment unit that can automatically purchase books based on the user's reading history and emotions. This enables customized book recommendations based on the user's individual interests and emotional state and a one-stop purchasing experience.

[0503] A "generative AI model" is an artificial intelligence algorithm that recommends books based on a user's reading history and interests.

[0504] "Recommendation method" refers to a function that uses a generative AI model to suggest the most suitable books to the user.

[0505] "Location information means" is a function that acquires the user's current location and provides inventory information for the nearest bookstore or library.

[0506] "Transaction means" is a function that provides an online marketplace for users to trade books they own with other users.

[0507] The "event management means" is a function for managing schedules for events and workshops and notifying users of them.

[0508] "Translation means" is a function that translates books in foreign languages ​​and provides them in the user's native language.

[0509] The "surprise recommendation means" is a function that recommends books by new genres or authors that deviate from the user's usual reading habits.

[0510] The "emotion recommendation means" is a function that analyzes the user's emotional state and recommends books based on that emotion.

[0511] "Electronic payment methods" is a feature that allows users to automatically purchase books based on their subscription history and sentiment.

[0512] The system of the present invention combines a generative AI model and an emotion engine to provide customized book suggestions and related information to enhance the user's reading experience. This system is realized through the exchange of data between a server, terminals, and users.

[0513] System Programming and Processing

[0514] 1. Recommendation methods using generative AI models

[0515] The server uses generative AI to suggest recommended books based on data obtained from the user, such as their subscription history and their favorite genres and authors. For example, if a user likes "mystery novels," the server uses that data to generate a list of the latest mystery novels and presents it to the user. When a user enters a specific author using the search function, all works related to that author are returned as search results.

[0516] 2. Location information means

[0517] The device acquires the user's location information and provides inventory information for the nearest bookstore or library. For example, if a user searches for a specific book and has location information turned on, the server uses the user's location information and search keywords to acquire inventory information for the nearest bookstore or library and displays it on the device. A pin is displayed on a map, allowing the user to access that location and obtain the book.

[0518] 3. Trading instruments that provide online marketplaces

[0519] Users register books they own that they no longer need in the app and wish to trade with other users. This information is sent to the server and registered in the database. When other users search for the book, the server retrieves matching information and notifies the user who wishes to trade. Once negotiations for a trade begin between users and the transaction is concluded, the server saves a log of the transaction.

[0520] 4. Event Management Tools

[0521] The server registers event information from famous authors and related parties in a database and notifies users. When a user registers to participate in an event, the information is sent to the server and registration is completed. For example, if a "reading group of famous authors" is being held, the information is notified to users, and users who wish to participate can register. The server then sends reminders.

[0522] 5. Translation Methods

[0523] When a user selects a book written in a specific foreign language, the server uses generative AI to translate the book into the user's native language. When a user selects a specific book, the server uses generative AI to translate the entire text and sends it to the user. The user's device displays the translated text, providing a comfortable reading experience. For example, if a user selects "Le Petit Prince" (French), the server uses generative AI to translate the entire text and sends it to the user.

[0524] 6. Surprise Recommendations

[0525] The server retrieves the user's past reading habits from a database and runs an algorithm to suggest books in different genres or by different authors. For example, if a user primarily reads business books, the server might suggest new books such as poetry collections or science fiction novels. This gives users the opportunity to discover new genres.

[0526] 7. Emotion recommendation means

[0527] The server uses an emotion engine to recognize the user's emotions and recommends books based on those emotions. As a specific example, when a user expresses emotions using a camera or microphone, the device analyzes the user's facial expressions and tone of voice and sends the data to the server. The server then analyzes the emotion data using an emotion engine and recommends books that best suit the user's mood and emotions. For example, if the user feels tired, the emotion engine will recommend books in relaxing genres, such as essays and short stories. Conversely, if the user is excited, it will recommend books in genres such as suspense and action.

[0528] 8. Electronic Payment Methods

[0529] This system allows users to automatically purchase books based on their subscription history and emotions. The server processes payment information and completes the purchase process for the book selected by the user. For example, when a user selects a suggested book and presses the purchase button, the server completes the payment through a payment gateway and provides the book to the user in digital format.

[0530] This allows users to enjoy a one-stop shopping experience with customized book recommendations based on their individual interests and emotional state.

[0531] Specific usage examples and prompts

[0532] For example, suppose user "A" likes mystery novels and has been feeling stressed lately. In this case, the emotion engine can detect this stressful state and recommend relaxing essays or short stories. An example prompt for the generative AI model is as follows:

[0533] "The user's reading history primarily includes mystery novels. They're also currently feeling stressed. Suggest five relaxing essays and short stories to them."

[0534] Based on these prompts, the generative AI model suggests the best books for the user, who can then easily purchase them using electronic payment methods.

[0535] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0536] Step 1:

[0537] A user logs in to the app and enters data such as their subscription history, genres of interest, authors, etc. This data is sent from the device to the server and saved as a user profile.

[0538] Input: User subscription history, genre interests, author data

[0539] Output: User profile stored on the server

[0540] What happens: A user enters data into a form in your app, and that data is sent to your server.

[0541] Step 2:

[0542] The server generates book recommendations by sending prompts to the generative AI model based on the user's profile information. The prompts include information about the user's interests and past reading habits.

[0543] Input: User profile, prompt

[0544] Output: Book recommendation list

[0545] How it works: The generative AI model generates a list of book recommendations based on the prompt.

[0546] Step 3:

[0547] The generated book recommendation list is sent from the server to the terminal and presented to the user, who then selects the books they want to read from the presented list.

[0548] Input: Book recommendation list

[0549] Output: A list of recommended books displayed on the user's device

[0550] Specific operation: Data is sent from the server to the device, and a list is displayed on the app screen.

[0551] Step 4:

[0552] When a user selects a particular book, the server uses an emotion engine to recognize the user's emotions and re-adjusts book recommendations based on that data.

[0553] Input: User book selection, emotion data

[0554] Output: A refined list of book recommendations based on emotions

[0555] How it works: The device analyzes the user's facial expressions and tone of voice, and sends the data to the server, which then analyzes the data using an emotion engine.

[0556] Step 5:

[0557] The server generates a book recommendation list based on the emotions and sends it back to the user's device, where the user can select books they want to purchase from the new recommendation list.

[0558] Input: A list of book recommendations based on sentiment

[0559] Output: The re-adjusted recommended book list displayed on the user's device.

[0560] Specific operation: The server sends the recommendation list to the device, which is then displayed in the app.

[0561] Step 6:

[0562] When a user selects a book to purchase and presses the purchase button, the server processes the payment information and completes the electronic transaction. Once the payment is complete, the book is provided to the user.

[0563] Input: User purchase selection, payment information

[0564] Output: Purchase completion notification, book provided to user

[0565] What happens: The server processes the payment through the payment gateway and adds the book data to the user's account.

[0566] 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.

[0567] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0568] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0569] [Second embodiment]

[0570] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0571] 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.

[0572] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0573] 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.

[0574] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0575] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0576] 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.

[0577] 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.

[0578] 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 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.

[0579] 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.

[0580] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0581] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0582] The system of the present invention combines a generative AI model with various functions to provide customized book suggestions and related information to improve the user's reading experience. The main components of this system include data exchange between a server, terminals, and users.

[0583] System Programming and Processing

[0584] 1. Book Search Navigator

[0585] The server uses generative AI to suggest recommended books based on data acquired from the user, such as their subscription history, genres of interest, and authors. For example, if a user likes "mystery novels," the server uses that data to generate a list of the latest mystery novels and presents it to the user. When a user enters a specific author using the search function, all works related to that author are returned as search results.

[0586] 2. Book Destination Guide

[0587] The device obtains the user's location information and provides inventory information for the nearest bookstore or library. For example, if a user searches for "Harry Potter" and has location information turned on, the server uses the user's location information and search keywords to obtain inventory information for the nearest bookstore or library, and the device displays it to the user. A pin is displayed on a map, and the user can access that location to obtain the book.

[0588] 3. Book Hunting Market

[0589] Users register books they own that they no longer need in the app and wish to trade with other users. This information is sent to the server and registered in the database. When other users search for the book, the server retrieves matching information and notifies the user who wishes to trade. Once negotiations for a trade begin between users and the transaction is concluded, the server saves a log of the transaction.

[0590] 4. Exciting Book Classes

[0591] The server registers information about famous authors and related events in a database and notifies users. When a user registers to participate in an event, the information is sent to the server, and registration is completed. For example, if a "reading group by a famous author" is being held, users are notified of the information and those who wish to participate can register. The server then sends reminders.

[0592] 5. Word Adventurer

[0593] When a user selects a book written in a specific foreign language, the server uses generative AI to translate the book into the user's native language. For example, if the user selects "Le Petit Prince" (French), the server uses generative AI to translate the entire text and sends it to the user. The user's device then displays the translated text, providing a comfortable reading experience.

[0594] 6. Reading Surprise

[0595] The server acquires the user's past reading habits and runs an algorithm to suggest books in different genres or by different authors. For example, for a user who mainly reads "business books," the server might suggest new books such as "poetry collections" or "science fiction novels." This gives the user the opportunity to be exposed to new genres.

[0596] Through these functions, the system of the present invention can provide a diverse and customized reading experience, enhancing users' interest in books. This allows users to easily find and conveniently obtain books they want to read, while also encouraging them to explore new reading areas.

[0597] The processing flow will be explained below.

[0598] 1. Book Search Navigator

[0599] Subject: Server

[0600] Step 1:

[0601] When a user logs in, the device sends the user ID and token to the server.

[0602] Step 2:

[0603] The server retrieves the user's subscription history, genres of interest, and author information from the database.

[0604] Step 3:

[0605] The server uses generative AI to calculate book recommendations based on user data.

[0606] Step 4:

[0607] Based on the calculation results, the server creates a list of recommended books and sends it to the device.

[0608] Step 5:

[0609] The terminal displays the received list to the user.

[0610] 2. Book Destination Guide

[0611] Subject: Terminal

[0612] Step 1:

[0613] The user allows the use of location information.

[0614] Step 2:

[0615] The device obtains the user's current location and sends it to the server.

[0616] Step 3:

[0617] The user enters a search keyword and sends it to the server.

[0618] Step 4:

[0619] The server uses the location information and search keywords to retrieve inventory information from the database of the nearest bookstore or library.

[0620] Step 5:

[0621] The server transmits the acquired inventory information to the terminal.

[0622] Step 6:

[0623] The device displays a pin on a map and shows the user the inventory information of the nearest bookstore or library.

[0624] 3. Book Hunting Market

[0625] Subject: User and Server

[0626] Step 1:

[0627] The user inputs information about the books he owns and sends it to the server.

[0628] Step 2:

[0629] The server registers the book information received from the user in a database.

[0630] Step 3:

[0631] Other users search for specific books and send requests to the server.

[0632] Step 4:

[0633] The server searches the database, retrieves the matching book information, and returns it to the requesting user.

[0634] Step 5:

[0635] When a user expresses interest in a particular book and requests a transaction, the server notifies both users.

[0636] Step 6:

[0637] The users negotiate a transaction and the transaction is concluded.

[0638] 4. Exciting Book Classes

[0639] Subject: Server

[0640] Step 1:

[0641] The server receives event information from authors and related parties and registers it in a database.

[0642] Step 2:

[0643] The server notifies the user of the registered event information.

[0644] Step 3:

[0645] When a user wishes to participate, they send a request to the server.

[0646] Step 4:

[0647] The server approves the request and registers the user.

[0648] Step 5:

[0649] The server sends a reminder to the user the day before the event.

[0650] 5. Word Adventurer

[0651] Subject: Server

[0652] Step 1:

[0653] The user selects the book they wish to translate and sends a request to the server.

[0654] Step 2:

[0655] The server launches a generative AI to translate the book text into the user's native language.

[0656] Step 3:

[0657] The server generates the translated text data and sends it to the user.

[0658] Step 4:

[0659] The user's device displays the translated text.

[0660] 6. Reading Surprise

[0661] Subject: Server

[0662] Step 1:

[0663] The server retrieves the user's reading history from the database.

[0664] Step 2:

[0665] The server uses generative AI to run algorithms that recommend new genres and authors outside of the user's interests.

[0666] Step 3:

[0667] The server creates a surprise recommendation list based on the calculation results and sends it to the user.

[0668] Step 4:

[0669] The user's device will display suggestions, encouraging a new reading experience.

[0670] The above processing steps realize a system that can provide a customized reading experience to the user.

[0671] Example 1

[0672] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0673] For many modern book lovers, selecting and acquiring the right books to enhance their reading experience is a key challenge. In addition, they have diverse needs, such as attending events of interest, trading unwanted books, exploring new genres, and translating foreign language books. Conventional systems must address these needs individually, requiring users to use multiple applications and services, which is inconvenient. Furthermore, recommendations based on a user's reading history and interests often lack accuracy. It is desirable to solve these issues with a single system.

[0674] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0675] In this invention, the server includes a recommendation unit using a generative AI model to present recommended books based on the user's reading history and interests, a location information unit that acquires the user's location information and provides inventory information for the nearest bookstore or library, a transaction unit that provides an online marketplace for users to trade books they own with other users, an event management unit that manages event and workshop schedules and notifies users, a translation unit that translates books in foreign languages ​​and provides them in the user's native language, a surprise recommendation unit that recommends books in new genres or by authors outside of traditional reading trends, a recommendation generation unit that inputs prompts into the generative AI model to generate a list of recommended books, and a transaction storage unit that stores transaction details and logs when a book transaction between users is completed. This allows users to satisfy a wide range of reading-related needs with a single system, enabling highly accurate book recommendations, convenient book search and purchase, event participation, exploration of new genres, and translation of foreign language books.

[0676] 1. "Recommendation" is a feature that uses a generative AI model to suggest books based on a user's reading history and interests.

[0677] 2. "Location information means" is a function that obtains the user's location information and provides inventory information of the nearest bookstore or library.

[0678] 3. "Transaction Means" means a feature that provides an online marketplace for users to trade books they own with other users.

[0679] 4. "Event management means" is a function that manages the schedule of events and workshops and notifies users.

[0680] 5. "Translation means" refers to the function of translating books in foreign languages ​​and providing them in the user's native language.

[0681] 6. "Surprise recommendation" is a function that recommends books in new genres or by new authors that deviate from traditional reading trends.

[0682] 7. "Recommendation generation means" is a function that generates a book recommendation list by inputting a prompt sentence into the generative AI model.

[0683] 8. "Transaction storage means" is a function that stores transaction details and logs when a book transaction is concluded between users.

[0684] The present invention provides a system that combines a generative AI model with various functions to improve the user's reading experience. Specific embodiments of the system are described below.

[0685] System Configuration

[0686] This system functions mainly through the exchange of data between the server, terminals, and users. The following hardware and software are used:

[0687] Hardware: High-performance cloud server (e.g. AWS, Google Cloud), smartphone with GPS function

[0688] Software: generative AI models (e.g., GPT-4), databases (e.g., MySQL), mapping applications (e.g., Google Maps), event management systems (e.g., Eventbrite), notification systems (e.g., Firebase), translation APIs (e.g., Google Translate API)

[0689] Recommendation method

[0690] The server uses a generative AI model to recommend books based on the user's reading history and interests. When a user logs in to the app and enters their genre and author information, the server collects that data and inputs prompts into the generative AI model to generate a list of book recommendations.

[0691] Example prompt sentence:

[0692] A user's subscription history and genre interest is mystery novels. Generate a list of the latest mystery novels.

[0693] This recommendation list is sent to the user's terminal, and the recommended book list is displayed on the screen.

[0694] Location information means

[0695] The device acquires the user's location information and provides inventory information for the nearest bookstore or library. When the user enters search keywords and location information, the server uses that information to gather inventory information for the nearest bookstore or library and sends it to the user's device. A pin is displayed on the map, and the user can access that location to obtain the book.

[0696] Example prompt sentence:

[0697] A user searches for Harry Potter and is located in Tokyo. Please provide availability information for the nearest bookstore or library.

[0698] Trading Instruments

[0699] Users register unwanted books on the online marketplace and wish to trade with other users. The server stores the registered book information in a database, and when other users search, matching book information is provided. When a trade is made, the server stores the transaction details and logs.

[0700] Event Management Methods

[0701] The server manages schedules for famous authors and related events and notifies users. When a user applies to participate in an event, the information is sent to the server and the registration is completed. For example, if a "reading group of famous authors" is being held, the information is notified to users, and users who wish to participate can apply.

[0702] Example prompt sentence:

[0703] A reading group for a famous author is being held. Please explain the process for notifying users and accepting applications to participate.

[0704] Translation tools

[0705] The server uses a generative AI model to translate a foreign language book selected by the user and provide it in the user's native language. For example, if the user selects "Le Petit Prince" (French), the server translates the entire text and sends it to the user's device.

[0706] Example prompt sentence:

[0707] Please translate Le Petit Prince (French) into the user's native language (Japanese).

[0708] Surprise recommendation method

[0709] The server will suggest books from different genres and authors based on the user's past reading habits. For example, if a user primarily reads business books, the server might suggest new books such as poetry collections or science fiction novels, providing an opportunity for a new reading experience.

[0710] Example prompt sentence:

[0711] For a user who likes business books, suggest books in different genres, such as poetry collections and science fiction novels.

[0712] Recommendation generator and transaction store

[0713] The server inputs prompts into the generative AI model to generate a list of recommended books. When a book transaction is completed between users, the server stores the transaction details and logs in a database.

[0714] This allows users to satisfy a wide range of reading-related needs in one system, providing accurate book recommendations, convenient book search and purchase, event participation, exploring new genres, and foreign language book translation.

[0715] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0716] Recommendation method

[0717] Step 1:

[0718] A user logs in to the app and enters the genres and author information they are interested in. This information is sent from the app to the server. The input data includes the genres and author information selected by the user.

[0719] Step 2:

[0720] The server collects the user's subscription history and input data. It retrieves past subscription history from the database and integrates it with newly entered genre and author information.

[0721] Specific behavior:

[0722] The server queries the database to retrieve the subscription history associated with the user ID, and then adds the newly entered genre and author information to it.

[0723] Step 3:

[0724] The server inputs the following prompt sentence into the generative AI model to generate a list of book recommendations:

[0725] A user's subscription history and genre interest is mystery novels. Generate a list of the latest mystery novels.

[0726] A generative AI model creates a recommendation list based on this prompt.

[0727] Step 4:

[0728] The server sends the generated recommendation list to the user's device via an API, and the output data includes the recommended book list.

[0729] Step 5:

[0730] The device displays a list of recommended books to the user, who can then view the recommended books on the screen and click on links for more information or to purchase.

[0731] Location information means

[0732] Step 1:

[0733] The user enters a search term and location information. The user turns on location services and enters the search term into the app.

[0734] Step 2:

[0735] The device uses the GPS function to obtain the user's current location. This location information is sent to the server. The GPS information of the current location is included as input data.

[0736] Specific behavior:

[0737] The device activates the GPS sensor, acquires the current location, and sends it to the server.

[0738] Step 3:

[0739] The server collects inventory information from the nearest bookstore or library based on the location information and search keywords, and sends a query to the inventory management system to obtain the inventory information for the corresponding book.

[0740] Specific behavior:

[0741] The server queries a database or API to retrieve inventory information that matches the location and search keywords.

[0742] Step 4:

[0743] The server sends the collected inventory information and its location to the terminal. The output data includes the inventory information and location data.

[0744] Step 5:

[0745] The device will use a map app to display the location of the bookstore or library you retrieved, and a pin will appear on the map, allowing you to visit the location and get the book.

[0746] Trading Instruments

[0747] Step 1:

[0748] The user registers unwanted books in the app and requests a transaction. The user enters the book information and sends the data to the server.

[0749] Step 2:

[0750] The server stores the registered book information in a database. Input data includes the book title, condition, price, contact information, etc.

[0751] Specific behavior:

[0752] The server registers the book information in the database using an INSERT query.

[0753] Step 3:

[0754] Other users search for specific books by entering a search query and sending it to the server.

[0755] Step 4:

[0756] The server retrieves book information that matches the search query from the database, and sends the retrieved book information to the user via API.

[0757] Specific behavior:

[0758] The server sends a SELECT query to the database and retrieves the search results.

[0759] Step 5:

[0760] The server notifies the user of the matching book information, and the search results are displayed on the user's device.

[0761] Step 6:

[0762] When a transaction negotiation begins between users and the transaction is concluded, the server stores the transaction details and log in the database. The output data includes the transaction details and log data.

[0763] Specific behavior:

[0764] The server records the transaction information in the database using an INSERT query.

[0765] Event Management Methods

[0766] Step 1:

[0767] The server registers the schedule of famous authors and related events in a database and notifies the user. The event information is stored in the database.

[0768] Specific behavior:

[0769] The server registers the event information in the database with an INSERT query and sends the information to the notification system.

[0770] Step 2:

[0771] A user applies to participate in an event. The application information is sent to the server and stored in a database.

[0772] Step 3:

[0773] The server completes the registration and sends a confirmation message to the user. The output data includes the registration confirmation message.

[0774] Step 4:

[0775] The server sends a reminder before the event, and the event reminder message is displayed on the user's device.

[0776] Translation tools

[0777] Step 1:

[0778] A user selects a particular foreign language book and submits that information to a server, including the book's foreign language text as input data.

[0779] Step 2:

[0780] The server translates the foreign language book using a generative AI model. The generative AI model is given the following prompt:

[0781] Please translate Le Petit Prince (French) into the user's native language (Japanese).

[0782] The model generates the translation result.

[0783] Step 3:

[0784] The server sends the translated book information to the user's device, and the output data includes the translated text.

[0785] Step 4:

[0786] The device displays the translated text to the user, who can comfortably read the text in their native language.

[0787] Surprise recommendation method

[0788] Step 1:

[0789] The server analyzes the user's past reading habits, retrieves past subscription history from the database, and performs the analysis.

[0790] Specific behavior:

[0791] The server queries the database to analyze the subscription history associated with the user ID.

[0792] Step 2:

[0793] The server inputs the following prompt sentence into the generative AI model to recommend new books that deviate from the user's preferences.

[0794] For a user who likes business books, suggest books in different genres, such as poetry collections and science fiction novels.

[0795] The model creates a new recommendation list.

[0796] Step 3:

[0797] The server sends the generated new recommendation list to the user's device, and the output data includes the new genre and author recommendation list.

[0798] Step 4:

[0799] The device displays a list of newly recommended books to the user, giving the user the opportunity to discover books in a new genre.

[0800] (Application example 1)

[0801] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0802] Conventional reading support systems are limited to recommending books based on a user's interests and reading history, and providing book inventory information. They lack comprehensive support for efficient book searches within bookstores and for improving the user experience. Furthermore, they lack the integration of a wide range of services, such as trading books owned by users, managing event information, and translating foreign language books. As a result, users are forced to use various services individually, resulting in a lack of a consistent reading experience.

[0803] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0804] In this invention, the server includes a recommendation unit using a generative AI model to present recommended books based on the user's reading history and interests, a location information unit that acquires the user's location information and provides inventory information for the nearest bookstore or library, a trading unit that provides an online marketplace for the user to trade books owned by the user with other users, an event management unit that manages event and workshop schedules and notifies the user, a translation unit that translates books in foreign languages ​​and provides them in the user's native language, a surprise recommendation unit that recommends books in new genres or by new authors outside of traditional reading habits, and a smartphone application provision unit that assists navigation within the bookstore and suggests books based on the user's preferences. This allows the user to have a consistent and comprehensive reading experience.

[0805] A "generative AI model" is an algorithm or program that automatically suggests books based on a user's reading history and interests.

[0806] A "recommendation tool" is a mechanism or method for using a generative AI model to suggest suitable books to a user.

[0807] "Location information means" refers to a system or device that acquires the user's current location and provides inventory information for the nearest bookstore or library based on that information.

[0808] "Exchange vehicle" means a method or system that provides an online marketplace for users to trade or sell their books with other users.

[0809] An "event management means" is a method or system for managing the schedule of events or workshops and notifying users.

[0810] A "translation means" is a method or system for automatically translating a book written in a foreign language into a user's native language and providing the results to the user.

[0811] A "surprise recommendation method" is a mechanism or method for recommending books in new genres or by new authors that are outside the user's usual reading habits.

[0812] "Means for providing smartphone applications" refers to a method or system for providing smartphone applications that support efficient book searches and navigation within a bookstore and suggest books based on the user's preferences.

[0813] This invention is a system for improving users' reading experience by providing customized book suggestions and related information using a generative AI model. The main components of this system include data exchange between a server, a terminal, and a user.

[0814] The system has the following main functions:

[0815] 1. Recommendation method:

[0816] The server uses a generative AI model to suggest books based on the user's reading history and interests. This allows users to easily find books that match their interests. An example of a prompt is, "Please suggest books based on mystery novels I've read in the past."

[0817] 2. Location information means:

[0818] The server uses the location information obtained from the device to provide inventory information for the nearest bookstore or library. This function allows users to quickly check where the book they are looking for is in stock, making book searches in bookstores more efficient.

[0819] 3. Trading instruments:

[0820] It provides an online marketplace for users to trade their books with other users, allowing them to give away or sell books they have finished reading to other users. This information is sent to a server and registered in a database.

[0821] 4. Event Management Measures:

[0822] The server manages schedules for events and workshops and notifies users. This function allows users to find out about book clubs by famous authors and related events, and to apply to participate.

[0823] 5. Translation Methods:

[0824] The server translates books in a foreign language into the user's native language and provides the results to the user. This allows the user to enjoy a variety of books across language barriers. As a specific example, the server may translate "Le Petit Prince" (French) into the user's native language and provide it to the user.

[0825] 6. Surprise Recommendation:

[0826] The server suggests books from new genres or authors that deviate from the user's usual reading habits. This allows the user to discover new reading pleasures. An example of a prompt sentence is "Please suggest books from new genres or authors that deviate from the user's usual reading habits."

[0827] 7. Means of providing smartphone applications:

[0828] We provide a smartphone application that assists users in navigating bookstores and guides them to the location of desired books. This application allows users to quickly find specific books in bookstores. Specifically, it provides navigation on a store map based on the user's location information.

[0829] To implement this system, a smartphone is required as hardware. On the software side, Python programming, an API for generative AI models, and Geopy for handling geographic information are used. The server processes this data, generates the information to be provided to the user, and sends it to the device. Specific examples of prompts used include those that suggest books based on the user's interests and reading history, such as the following:

[0830] "Please suggest books based on mystery novels you've read in the past."

[0831] "Generate new book lists based on your genre interests."

[0832] "I'm looking for a work by a particular author. Please list all related works."

[0833] This system allows users to have a consistent and comprehensive reading experience.

[0834] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0835] Step 1: Receive the user request

[0836] A user opens the application and requests book recommendations, including input about the user's reading history, genre interests, and specific author information. This information is then sent to the server.

[0837] Step 2: Data processing by generative AI models

[0838] The server inputs the received user data into a generative AI model and generates a list of recommended books using prompts. The input data includes past reading history and genre interests, and the output data is a list of recommended books. An example prompt is: "Please suggest books based on mystery novels I've read in the past."

[0839] Step 3: Generate and submit a recommendation list

[0840] The server sends the list of recommended books output by the generative AI model to the device. In this process, the recommended book list is sent to the user's smartphone and displayed on the device.

[0841] Step 4: Obtaining location information

[0842] The user turns on location information to search for a specific book. This input includes the user's current location and the book they are looking for. The device sends the location information to the server.

[0843] Step 5: Retrieve and display inventory information

[0844] The server retrieves inventory information from the database of the nearest bookstore or library using the user's location information and search keywords. Based on this information, the server sends the inventory information to the terminal, which displays it to the user. The output includes the book's location and inventory status.

[0845] Step 6: Performing Navigation

[0846] A user enters a bookstore and uses an application to locate a particular book. The input includes the book's ID and the user's current location. The device navigates the book's location within the store and provides the user with specific directions. The output includes the book's location (aisle number and shelf number).

[0847] Step 7: Request a translation

[0848] A user selects a foreign language book and requests that it be translated into their native language. The server uses a generative AI model to translate the foreign language book into the user's native language and sends the text to the user. The input data is the original text, and the output data is the translated text.

[0849] Step 8: Event Information Notification

[0850] The server uses an event management mechanism to notify users of related events. When a user registers to participate in an event, the information is sent to the server, which manages the schedule. Input data includes details of the event and the user's response, and output data includes confirmation of participation in the event.

[0851] These steps will enable us to provide a comprehensive reading experience and improve user convenience and satisfaction.

[0852] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0853] The system of the present invention combines a generative AI model and an emotion engine to provide customized book suggestions and related information to enhance the user's reading experience. This system is realized through the exchange of data between a server, terminals, and users.

[0854] System Programming and Processing

[0855] 1. Book Search Navigator

[0856] The server uses generative AI to suggest recommended books based on data obtained from the user, such as their subscription history and their favorite genres and authors. For example, if a user likes "mystery novels," the server uses that data to generate a list of the latest mystery novels and presents it to the user. When a user enters a specific author using the search function, all works related to that author are returned as search results.

[0857] 2. Book Destination Guide

[0858] The device obtains the user's location information and provides inventory information for the nearest bookstore or library. For example, if a user searches for "Harry Potter" and has location information turned on, the server uses the user's location information and search keywords to obtain inventory information for the nearest bookstore or library and displays it on the device. A pin is displayed on a map, allowing the user to access that location and obtain the book.

[0859] 3. Book Hunting Market

[0860] Users register books they own that they no longer need in the app and wish to trade with other users. This information is sent to the server and registered in the database. When other users search for the book, the server retrieves matching information and notifies the user who wishes to trade. Once negotiations for a trade begin between users and the transaction is concluded, the server saves a log of the transaction.

[0861] 4. Exciting Book Classes

[0862] The server registers event information from famous authors and related parties in a database and notifies users. When a user registers to participate in an event, the information is sent to the server and registration is completed. For example, if a "reading group of famous authors" is being held, the information is notified to users, and users who wish to participate can register. The server then sends reminders.

[0863] 5. Word Adventurer

[0864] When a user selects a book written in a specific foreign language, the server uses generative AI to translate the book into the user's native language. For example, if the user selects "Le Petit Prince" (French), the server uses generative AI to translate the entire text and sends it to the user. The user's device displays the translated text, providing a comfortable reading experience.

[0865] 6. Reading Surprise

[0866] The server retrieves the user's past reading habits from a database and runs an algorithm to suggest books in different genres or by different authors. For example, if a user primarily reads business books, the server might suggest new books such as poetry collections or science fiction novels. This gives users the opportunity to discover new genres.

[0867] 7. Emotion Engine

[0868] The server uses an emotion engine to recognize the user's emotions and recommends books based on those emotions. As a specific example, when a user expresses their emotions using a camera or microphone, the device analyzes the user's facial expressions and tone of voice and sends the data to the server. The server then uses an emotion engine to analyze the emotion data and recommends books that best suit the user's mood and emotions.

[0869] Example of emotion engine

[0870] For example, if a user feels tired, the emotion engine will recommend books in relaxing genres, such as essays or short stories. Conversely, if the user feels excited, it will recommend books in genres such as suspense or action. In this way, book suggestions tailored to the user's current emotional state provide a more personalized reading experience.

[0871] By combining each of the above functions with an emotion engine, the system of the present invention can meet the diverse needs of users and provide a more highly customized reading experience.

[0872] The processing flow will be explained below.

[0873] 1. Book Search Navigator

[0874] Subject: Server

[0875] Step 1:

[0876] When a user logs in, the device sends the user ID and token to the server.

[0877] Step 2:

[0878] The server retrieves the user's subscription history, genres of interest, and author information from the database.

[0879] Step 3:

[0880] The server uses generative AI to calculate book recommendations based on user data.

[0881] Step 4:

[0882] Based on the calculation results, the server creates a list of recommended books and sends it to the device.

[0883] Step 5:

[0884] The terminal displays the received list to the user.

[0885] 2. Book Destination Guide

[0886] Subject: Terminal

[0887] Step 1:

[0888] The user allows the use of location information.

[0889] Step 2:

[0890] The device obtains the user's current location and sends it to the server.

[0891] Step 3:

[0892] The user enters a search keyword and sends it to the server.

[0893] Step 4:

[0894] The server uses the location information and search keywords to retrieve inventory information from the database of the nearest bookstore or library.

[0895] Step 5:

[0896] The server transmits the acquired inventory information to the terminal.

[0897] Step 6:

[0898] The device displays a pin on a map and shows the user the inventory information of the nearest bookstore or library.

[0899] 3. Book Hunting Market

[0900] Subject: User and Server

[0901] Step 1:

[0902] The user inputs information about the books he owns and sends it to the server.

[0903] Step 2:

[0904] The server registers the book information received from the user in a database.

[0905] Step 3:

[0906] Other users search for specific books and send requests to the server.

[0907] Step 4:

[0908] The server searches the database, retrieves the matching book information, and returns it to the requesting user.

[0909] Step 5:

[0910] When a user expresses interest in a particular book and requests a transaction, the server notifies both users.

[0911] Step 6:

[0912] The users negotiate a transaction and the transaction is concluded.

[0913] 4. Exciting Book Classes

[0914] Subject: Server

[0915] Step 1:

[0916] The server receives event information from authors and related parties and registers it in a database.

[0917] Step 2:

[0918] The server notifies the user of the registered event information.

[0919] Step 3:

[0920] When a user wishes to participate, they send a request to the server.

[0921] Step 4:

[0922] The server approves the request and registers the user.

[0923] Step 5:

[0924] The server sends a reminder to the user the day before the event.

[0925] 5. Word Adventurer

[0926] Subject: Server

[0927] Step 1:

[0928] The user selects the book they wish to translate and sends a request to the server.

[0929] Step 2:

[0930] The server launches a generative AI to translate the book text into the user's native language.

[0931] Step 3:

[0932] The server generates the translated text data and sends it to the user.

[0933] Step 4:

[0934] The user's device displays the translated text.

[0935] 6. Reading Surprise

[0936] Subject: Server

[0937] Step 1:

[0938] The server retrieves the user's reading history from the database.

[0939] Step 2:

[0940] The server uses generative AI to run algorithms that recommend new genres and authors outside of the user's interests.

[0941] Step 3:

[0942] The server creates a surprise recommendation list based on the calculation results and sends it to the user.

[0943] Step 4:

[0944] The user's device will display suggestions, encouraging a new reading experience.

[0945] 7. Emotion Engine

[0946] Subject: Server and terminal

[0947] Step 1:

[0948] The user turns on the camera and microphone and allows emotional data capture.

[0949] Step 2:

[0950] The device captures the user's facial expressions and tone of voice in real time and sends the data to a server.

[0951] Step 3:

[0952] The server uses an emotion engine to analyze the received emotion data.

[0953] Step 4:

[0954] Based on the emotional data, the server uses a generative AI model to recommend books that best fit the user's current emotional state.

[0955] Step 5:

[0956] The server creates a recommendation list and sends it to the terminal.

[0957] Step 6:

[0958] The terminal displays a recommendation list based on the received emotions to the user.

[0959] The above specific processing steps enable highly customized reading suggestions tailored to the user's emotional state, allowing users to easily find the book that best suits their mood and emotions at any given time.

[0960] Example 2

[0961] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0962] Conventional book recommendation systems are limited to recommending books based on users' reading history and interests, and lack recommendations based on new genres or emotions. Furthermore, they lack an integrated system that allows users to conveniently check inventory by location information when searching for specific books, trade books with other users, and manage events and workshops, making it difficult to meet the diverse needs of users.

[0963] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0964] In this invention, the server includes a recommendation means using a generative AI model to present recommended books based on the user's reading history and interests, a location information means for acquiring the user's location information and providing inventory information of the nearest retailer or library facility, a trading means for providing an online trading venue for the user to trade books owned by the user with other users, an event management means for managing event and workshop schedules and notifying the user, a translation means for translating books in foreign languages ​​and providing them in the user's native language, a surprise recommendation means for recommending books in new genres or by new authors that are outside of traditional reading trends, and an emotion evaluation means for recognizing the user's emotions based on an emotion engine and recommending books that match those emotions. This makes it possible to meet the diverse needs of users and provide a personalized reading experience.

[0965] A "generative AI model" is an artificial intelligence model that generates a list of recommended books based on a user's reading history and interests.

[0966] A "recommendation method" is a method for recommending books suitable for a user using a generative AI model.

[0967] The "location information means" is a means for acquiring the user's location information and providing inventory information of the nearest retail store or library facility.

[0968] A "trading means" is a means for providing an online trading space for a user to trade books with other users.

[0969] The "event management means" is a means for managing schedules of events and workshops and notifying users of the schedules.

[0970] "Translation means" refers to a means for translating a book in a foreign language into the user's native language and providing it to the user.

[0971] A "surprise recommendation method" is a method for recommending to a user books in new genres or by new authors that deviate from traditional reading habits.

[0972] The "emotion evaluation means" is a means for recognizing the user's emotions using an emotion engine and recommending books that match those emotions.

[0973] The system of the present invention combines a generative AI model, a location information acquisition system, an online trading platform, an event management system, a translation function, a surprise recommendation function, and an emotion evaluation system to meet the diverse reading needs of users. Each component of the system is described in detail below.

[0974] 1. Recommendation method

[0975] The server uses a generative AI model to generate a book list based on the user's reading history and interests. The user inputs their genres and authors of interest through the app interface, and their device sends that information to the server. The server then generates prompts and inputs them into the generative AI model.

[0976] As a concrete example, if the user is interested in "mystery novels," the following prompt sentence may be used:

[0977] Based on your users' reading history and interests, create a list of the latest mystery novels, along with related information and reviews.

[0978] The generated book list is sent to the user's terminal along with additional information and displayed on the screen.

[0979] 2. Location information means

[0980] The device acquires the user's current location and provides inventory information for the nearest retailer or library. When the user searches for a specific book with location information turned on, the device sends that information to the server. The server then consults a database to collect inventory and location information and displays it to the user.

[0981] For example, if you searched for "Harry Potter," you would use the following prompt:

[0982] The user's current location is a specific location. Please provide information about retailers and libraries that stock Harry Potter. Please also display map information.

[0983] As a result, facilities with inventory are displayed on the map and the user can access the location.

[0984] 3. Trading Instruments

[0985] We provide an online trading platform where users can trade unwanted books with other users. Users input information about the book they wish to trade, and their terminals send it to the server. The server registers the information in a database, and when other users search for the same book, it matches them.

[0986] 4. Event Management Tools

[0987] The server has the function of registering event information from authors and event organizers in a database and notifying users. When a user registers to participate in an event, the information is sent to the server and registration is completed. Before the event begins, the server sends a reminder to the user.

[0988] 5. Translation Methods

[0989] When a user selects a book written in a specific foreign language, the server uses a generative AI model to translate the book. After the user selects a book, the device sends the information to the server. The server generates a translation prompt, inputs it into the AI ​​model, and obtains the translation result.

[0990] For example, if the user selects "Le Petit Prince (French)", use the following prompt text:

[0991] Please translate this French book into your native language.

[0992] The translated text is sent to the terminal and displayed to the user.

[0993] 6. Surprise Recommendations

[0994] The server suggests books from new genres and authors that are outside the user's past reading habits, giving the user the opportunity to be exposed to new genres.

[0995] 7. Emotional Assessment Instrument

[0996] The server uses an emotion engine to recognize the user's emotions and recommends books based on those emotions. When the user expresses their emotions using the camera or microphone, the device sends the data to the server for emotion analysis.

[0997] As a concrete example, if it is determined that the user is tired, the following prompt sentence is used:

[0998] List books in genres that users find relaxing.

[0999] The server provides the list obtained from the generative AI model to the user.

[1000] As described above, the system of the present invention can provide users with a personalized and sophisticated reading experience by flexibly combining each function.

[1001] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1002] "Book Search Navigator" program processing flow

[1003] Step 1: Accepting User Input

[1004] Users input genres and authors of interest through the app's interface. The input data is temporarily stored on the user's device (input). Specifically, the user enters "mystery novels" or "a specific author's name" into the app's search bar.

[1005] Step 2: Send data

[1006] The terminal sends the user's input data to the server (output). Specifically, when the send button on the terminal is pressed, the input data is sent to the back-end server.

[1007] Step 3: Data analysis

[1008] The server receives the transmitted data and generates a prompt based on the user's interests (input). The data is analyzed using text mining and natural language processing to communicate with the generative AI model. For example, a prompt might be created that reads, "Generate a list of the latest mystery novels based on the user's reading history and interests."

[1009] Step 4: Query the AI ​​model

[1010] The server inputs the generated prompt sentence into the generative AI model and obtains a list of appropriate books from the generative AI model (output). Specifically, the generative AI model generates a book list based on the prompt sentence and returns it to the server.

[1011] Step 5: List Generation

[1012] The server compares the book list returned by the generative AI model with its internal database and adds additional information such as reviews and ratings for each book (input and output). Specifically, it retrieves review information from the database and merges it with the retrieved book list.

[1013] Step 6: Sending the results

[1014] The server then sends the final book list and additional information to the user's device (output). Specifically, it converts the list into the required data format and sends it to the device using an API.

[1015] Step 7: Display to the user

[1016] The device displays the received book list and additional information in response to the user's request (input). Specifically, the book list is displayed in the app's UI, and when the user clicks, detailed information is displayed in a pop-up.

[1017] "Book Whereabouts Guide" program processing flow

[1018] Step 1: Accepting User Input

[1019] The user enters search keywords for a specific book within the app and turns on location services. The entered data is temporarily stored on the device (input). For example, the user enters "Harry Potter" as a search keyword.

[1020] Step 2: Send data

[1021] The device sends the search keywords and location information to the server (output). Specifically, when the user presses the send button, the device sends the GPS information and search keywords to the server.

[1022] Step 3: Data analysis

[1023] The server receives the location information and search keywords and searches the database for inventory information of the nearest retail store or library facility (input and output).Specifically, it queries facilities within a certain radius from the user's current location and retrieves inventory information that matches the search keywords from the database.

[1024] Step 4: Result generation

[1025] The server generates a list of the nearest retail stores and library facilities based on the location information and search keywords, and adds inventory information to the list (input and output).The server attaches inventory status to the acquired facility information and generates a list.

[1026] Step 5: Sending the results

[1027] The server sends the generated list and inventory information to the user's device (output). Specifically, it uses an API to convert the data into a display format and sends it to the device.

[1028] Step 6: Display to the user

[1029] The device displays the received list and inventory information along with a map to the user (input). Specifically, it uses the app's map function to map the facility's location and displays an icon indicating whether the item is in stock.

[1030] "Book Search Market" program processing flow

[1031] Step 1: Accepting User Input

[1032] The user enters information about the book they wish to trade into the app, including the book's title, author, condition, price, etc. (Input). The entered data is temporarily stored on the device.

[1033] Step 2: Send data

[1034] The terminal sends the entered book information to the server (output). When the user presses the "Register" button, the terminal sends the book information to the server.

[1035] Step 3: Database registration

[1036] The server receives the book information and registers it in the database (input and output). Specifically, it adds a new book record to the database.

[1037] Step 4: Search by other users

[1038] When other users search for a particular book, the keywords are sent to the server (input). Specifically, the user enters the keywords into the search bar and executes the search.

[1039] Step 5: Database Matching

[1040] The server matches the search keywords with the desired book information in the database and presents the matching books to the user who searches (input and output). Specifically, it queries the book records that match the keywords and generates the results.

[1041] Step 6: Submit matches

[1042] The server notifies the searched user and the user who wishes to trade of the matching book information (output). Specifically, it sends a notification email or an in-app notification to the user who wishes to trade.

[1043] Step 7: Negotiate the deal

[1044] A deal is negotiated between two users, who use the in-app chat feature to discuss the price and details of the deal (input and output).

[1045] Step 8: Keep a transaction log

[1046] When a transaction is completed, the server saves the transaction log in the database (input and output) and records the transaction details in the transaction log table.

[1047] (Application example 2)

[1048] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1049] In recent years, electronic payment services and online book sales platforms have become popular, but simply purchasing a book is not enough to enrich the user's reading experience. There is a need for a system that can recommend books and support purchases in an appropriate and timely manner based on the user's individual interests and emotional state. However, existing systems lack advanced customization based on the user's emotions and reading habits, and are therefore unable to provide the optimal reading experience for the user. To solve this problem, a system is needed that can recommend books based on the user's reading history, interests, and emotional state, and then allow for electronic payment.

[1050] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a recommendation unit using a generative AI model to present recommended books based on the user's reading history and interests; a location information unit that acquires the user's location information and provides inventory information for the nearest bookstore or library; a transaction unit that provides an online marketplace for the user to trade books owned by the user with other users; an event management unit that manages event and workshop schedules and notifies the user; a translation unit that translates books in foreign languages ​​and provides them in the user's native language; a surprise recommendation unit that recommends books in new genres or by authors that deviate from traditional reading habits; an emotion recommendation unit that analyzes the user's emotional state and recommends books based on the user's emotions; and an electronic payment unit that can automatically purchase books based on the user's reading history and emotions. This enables customized book recommendations based on the user's individual interests and emotional state and a one-stop purchasing experience.

[1051] A "generative AI model" is an artificial intelligence algorithm that recommends books based on a user's reading history and interests.

[1052] "Recommendation method" refers to a function that uses a generative AI model to suggest the most suitable books to the user.

[1053] "Location information means" is a function that acquires the user's current location and provides inventory information for the nearest bookstore or library.

[1054] "Transaction means" is a function that provides an online marketplace for users to trade books they own with other users.

[1055] The "event management means" is a function for managing schedules for events and workshops and notifying users of them.

[1056] "Translation means" is a function that translates books in foreign languages ​​and provides them in the user's native language.

[1057] The "surprise recommendation means" is a function that recommends books by new genres or authors that deviate from the user's usual reading habits.

[1058] The "emotion recommendation means" is a function that analyzes the user's emotional state and recommends books based on that emotion.

[1059] "Electronic payment methods" is a feature that allows users to automatically purchase books based on their subscription history and sentiment.

[1060] The system of the present invention combines a generative AI model and an emotion engine to provide customized book suggestions and related information to enhance the user's reading experience. This system is realized through the exchange of data between a server, terminals, and users.

[1061] System Programming and Processing

[1062] 1. Recommendation methods using generative AI models

[1063] The server uses generative AI to suggest recommended books based on data obtained from the user, such as their subscription history and their favorite genres and authors. For example, if a user likes "mystery novels," the server uses that data to generate a list of the latest mystery novels and presents it to the user. When a user enters a specific author using the search function, all works related to that author are returned as search results.

[1064] 2. Location information means

[1065] The device acquires the user's location information and provides inventory information for the nearest bookstore or library. For example, if a user searches for a specific book and has location information turned on, the server uses the user's location information and search keywords to acquire inventory information for the nearest bookstore or library and displays it on the device. A pin is displayed on a map, allowing the user to access that location and obtain the book.

[1066] 3. Trading instruments that provide online marketplaces

[1067] Users register books they own that they no longer need in the app and wish to trade with other users. This information is sent to the server and registered in the database. When other users search for the book, the server retrieves matching information and notifies the user who wishes to trade. Once negotiations for a trade begin between users and the transaction is concluded, the server saves a log of the transaction.

[1068] 4. Event Management Tools

[1069] The server registers event information from famous authors and related parties in a database and notifies users. When a user registers to participate in an event, the information is sent to the server and registration is completed. For example, if a "reading group of famous authors" is being held, the information is notified to users, and users who wish to participate can register. The server then sends reminders.

[1070] 5. Translation Methods

[1071] When a user selects a book written in a specific foreign language, the server uses generative AI to translate the book into the user's native language. When a user selects a specific book, the server uses generative AI to translate the entire text and sends it to the user. The user's device displays the translated text, providing a comfortable reading experience. For example, if a user selects "Le Petit Prince" (French), the server uses generative AI to translate the entire text and sends it to the user.

[1072] 6. Surprise Recommendations

[1073] The server retrieves the user's past reading habits from a database and runs an algorithm to suggest books in different genres or by different authors. For example, if a user primarily reads business books, the server might suggest new books such as poetry collections or science fiction novels. This gives users the opportunity to discover new genres.

[1074] 7. Emotion recommendation means

[1075] The server uses an emotion engine to recognize the user's emotions and recommends books based on those emotions. As a specific example, when a user expresses emotions using a camera or microphone, the device analyzes the user's facial expressions and tone of voice and sends the data to the server. The server then analyzes the emotion data using an emotion engine and recommends books that best suit the user's mood and emotions. For example, if the user feels tired, the emotion engine will recommend books in relaxing genres, such as essays and short stories. Conversely, if the user is excited, it will recommend books in genres such as suspense and action.

[1076] 8. Electronic Payment Methods

[1077] This system allows users to automatically purchase books based on their subscription history and emotions. The server processes payment information and completes the purchase process for the book selected by the user. For example, when a user selects a suggested book and presses the purchase button, the server completes the payment through a payment gateway and provides the book to the user in digital format.

[1078] This allows users to enjoy a one-stop shopping experience with customized book recommendations based on their individual interests and emotional state.

[1079] Specific usage examples and prompts

[1080] For example, suppose user "A" likes mystery novels and has been feeling stressed lately. In this case, the emotion engine can detect this stressful state and recommend relaxing essays or short stories. An example prompt for the generative AI model is as follows:

[1081] "The user's reading history primarily includes mystery novels. They're also currently feeling stressed. Suggest five relaxing essays and short stories to them."

[1082] Based on these prompts, the generative AI model suggests the best books for the user, who can then easily purchase them using electronic payment methods.

[1083] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1084] Step 1:

[1085] A user logs in to the app and enters data such as their subscription history, genres of interest, authors, etc. This data is sent from the device to the server and saved as a user profile.

[1086] Input: User subscription history, genre interests, author data

[1087] Output: User profile stored on the server

[1088] What happens: A user enters data into a form in your app, and that data is sent to your server.

[1089] Step 2:

[1090] The server generates book recommendations by sending prompts to the generative AI model based on the user's profile information. The prompts include information about the user's interests and past reading habits.

[1091] Input: User profile, prompt

[1092] Output: Book recommendation list

[1093] How it works: The generative AI model generates a list of book recommendations based on the prompt.

[1094] Step 3:

[1095] The generated book recommendation list is sent from the server to the terminal and presented to the user, who then selects the books they want to read from the presented list.

[1096] Input: Book recommendation list

[1097] Output: A list of recommended books displayed on the user's device

[1098] Specific operation: Data is sent from the server to the device, and a list is displayed on the app screen.

[1099] Step 4:

[1100] When a user selects a particular book, the server uses an emotion engine to recognize the user's emotions and re-adjusts book recommendations based on that data.

[1101] Input: User book selection, emotion data

[1102] Output: A refined list of book recommendations based on emotions

[1103] How it works: The device analyzes the user's facial expressions and tone of voice, and sends the data to the server, which then analyzes the data using an emotion engine.

[1104] Step 5:

[1105] The server generates a book recommendation list based on the emotions and sends it back to the user's device, where the user can select books they want to purchase from the new recommendation list.

[1106] Input: A list of book recommendations based on sentiment

[1107] Output: The re-adjusted recommended book list displayed on the user's device.

[1108] Specific operation: The server sends the recommendation list to the device, which is then displayed in the app.

[1109] Step 6:

[1110] When a user selects a book to purchase and presses the purchase button, the server processes the payment information and completes the electronic transaction. Once the payment is complete, the book is provided to the user.

[1111] Input: User purchase selection, payment information

[1112] Output: Purchase completion notification, book provided to user

[1113] What happens: The server processes the payment through the payment gateway and adds the book data to the user's account.

[1114] 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.

[1115] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1116] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1117] [Third embodiment]

[1118] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1119] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1121] 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.

[1122] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1123] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1124] 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.

[1125] 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.

[1126] 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 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.

[1127] 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.

[1128] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1129] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1130] The system of the present invention combines a generative AI model with various functions to provide customized book suggestions and related information to improve the user's reading experience. The main components of this system include data exchange between a server, terminals, and users.

[1131] System Programming and Processing

[1132] 1. Book Search Navigator

[1133] The server uses generative AI to suggest recommended books based on data acquired from the user, such as their subscription history, genres of interest, and authors. For example, if a user likes "mystery novels," the server uses that data to generate a list of the latest mystery novels and presents it to the user. When a user enters a specific author using the search function, all works related to that author are returned as search results.

[1134] 2. Book Destination Guide

[1135] The device obtains the user's location information and provides inventory information for the nearest bookstore or library. For example, if a user searches for "Harry Potter" and has location information turned on, the server uses the user's location information and search keywords to obtain inventory information for the nearest bookstore or library, and the device displays it to the user. A pin is displayed on a map, and the user can access that location to obtain the book.

[1136] 3. Book Hunting Market

[1137] Users register books they own that they no longer need in the app and wish to trade with other users. This information is sent to the server and registered in the database. When other users search for the book, the server retrieves matching information and notifies the user who wishes to trade. Once negotiations for a trade begin between users and the transaction is concluded, the server saves a log of the transaction.

[1138] 4. Exciting Book Classes

[1139] The server registers information about famous authors and related events in a database and notifies users. When a user registers to participate in an event, the information is sent to the server, and registration is completed. For example, if a "reading group by a famous author" is being held, users are notified of the information and those who wish to participate can register. The server then sends reminders.

[1140] 5. Word Adventurer

[1141] When a user selects a book written in a specific foreign language, the server uses generative AI to translate the book into the user's native language. For example, if the user selects "Le Petit Prince" (French), the server uses generative AI to translate the entire text and sends it to the user. The user's device then displays the translated text, providing a comfortable reading experience.

[1142] 6. Reading Surprise

[1143] The server acquires the user's past reading habits and runs an algorithm to suggest books in different genres or by different authors. For example, for a user who mainly reads "business books," the server might suggest new books such as "poetry collections" or "science fiction novels." This gives the user the opportunity to be exposed to new genres.

[1144] Through these functions, the system of the present invention can provide a diverse and customized reading experience, enhancing users' interest in books. This allows users to easily find and conveniently obtain books they want to read, while also encouraging them to explore new reading areas.

[1145] The processing flow will be explained below.

[1146] 1. Book Search Navigator

[1147] Subject: Server

[1148] Step 1:

[1149] When a user logs in, the device sends the user ID and token to the server.

[1150] Step 2:

[1151] The server retrieves the user's subscription history, genres of interest, and author information from the database.

[1152] Step 3:

[1153] The server uses generative AI to calculate book recommendations based on user data.

[1154] Step 4:

[1155] Based on the calculation results, the server creates a list of recommended books and sends it to the device.

[1156] Step 5:

[1157] The terminal displays the received list to the user.

[1158] 2. Book Destination Guide

[1159] Subject: Terminal

[1160] Step 1:

[1161] The user allows the use of location information.

[1162] Step 2:

[1163] The device obtains the user's current location and sends it to the server.

[1164] Step 3:

[1165] The user enters a search keyword and sends it to the server.

[1166] Step 4:

[1167] The server uses the location information and search keywords to retrieve inventory information from the database of the nearest bookstore or library.

[1168] Step 5:

[1169] The server transmits the acquired inventory information to the terminal.

[1170] Step 6:

[1171] The device displays a pin on a map and shows the user the inventory information of the nearest bookstore or library.

[1172] 3. Book Hunting Market

[1173] Subject: User and Server

[1174] Step 1:

[1175] The user inputs information about the books he owns and sends it to the server.

[1176] Step 2:

[1177] The server registers the book information received from the user in a database.

[1178] Step 3:

[1179] Other users search for specific books and send requests to the server.

[1180] Step 4:

[1181] The server searches the database, retrieves the matching book information, and returns it to the requesting user.

[1182] Step 5:

[1183] When a user expresses interest in a particular book and requests a transaction, the server notifies both users.

[1184] Step 6:

[1185] The users negotiate a transaction and the transaction is concluded.

[1186] 4. Exciting Book Classes

[1187] Subject: Server

[1188] Step 1:

[1189] The server receives event information from authors and related parties and registers it in a database.

[1190] Step 2:

[1191] The server notifies the user of the registered event information.

[1192] Step 3:

[1193] When a user wishes to participate, they send a request to the server.

[1194] Step 4:

[1195] The server approves the request and registers the user.

[1196] Step 5:

[1197] The server sends a reminder to the user the day before the event.

[1198] 5. Word Adventurer

[1199] Subject: Server

[1200] Step 1:

[1201] The user selects the book they wish to translate and sends a request to the server.

[1202] Step 2:

[1203] The server launches a generative AI to translate the book text into the user's native language.

[1204] Step 3:

[1205] The server generates the translated text data and sends it to the user.

[1206] Step 4:

[1207] The user's device displays the translated text.

[1208] 6. Reading Surprise

[1209] Subject: Server

[1210] Step 1:

[1211] The server retrieves the user's reading history from the database.

[1212] Step 2:

[1213] The server uses generative AI to run algorithms that recommend new genres and authors outside of the user's interests.

[1214] Step 3:

[1215] The server creates a surprise recommendation list based on the calculation results and sends it to the user.

[1216] Step 4:

[1217] The user's device will display suggestions, encouraging a new reading experience.

[1218] The above processing steps realize a system that can provide a customized reading experience to the user.

[1219] Example 1

[1220] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1221] For many modern book lovers, selecting and acquiring the right books to enhance their reading experience is a key challenge. In addition, they have diverse needs, such as attending events of interest, trading unwanted books, exploring new genres, and translating foreign language books. Conventional systems must address these needs individually, requiring users to use multiple applications and services, which is inconvenient. Furthermore, recommendations based on a user's reading history and interests often lack accuracy. It is desirable to solve these issues with a single system.

[1222] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1223] In this invention, the server includes a recommendation unit using a generative AI model to present recommended books based on the user's reading history and interests, a location information unit that acquires the user's location information and provides inventory information for the nearest bookstore or library, a transaction unit that provides an online marketplace for users to trade books they own with other users, an event management unit that manages event and workshop schedules and notifies users, a translation unit that translates books in foreign languages ​​and provides them in the user's native language, a surprise recommendation unit that recommends books in new genres or by authors outside of traditional reading trends, a recommendation generation unit that inputs prompts into the generative AI model to generate a list of recommended books, and a transaction storage unit that stores transaction details and logs when a book transaction between users is completed. This allows users to satisfy a wide range of reading-related needs with a single system, enabling highly accurate book recommendations, convenient book search and purchase, event participation, exploration of new genres, and translation of foreign language books.

[1224] 1. "Recommendation" is a feature that uses a generative AI model to suggest books based on a user's reading history and interests.

[1225] 2. "Location information means" is a function that obtains the user's location information and provides inventory information of the nearest bookstore or library.

[1226] 3. "Transaction Means" means a feature that provides an online marketplace for users to trade books they own with other users.

[1227] 4. "Event management means" is a function that manages the schedule of events and workshops and notifies users.

[1228] 5. "Translation means" refers to the function of translating books in foreign languages ​​and providing them in the user's native language.

[1229] 6. "Surprise recommendation" is a function that recommends books in new genres or by new authors that deviate from traditional reading trends.

[1230] 7. "Recommendation generation means" is a function that generates a book recommendation list by inputting a prompt sentence into the generative AI model.

[1231] 8. "Transaction storage means" is a function that stores transaction details and logs when a book transaction is concluded between users.

[1232] The present invention provides a system that combines a generative AI model with various functions to improve the user's reading experience. Specific embodiments of the system are described below.

[1233] System Configuration

[1234] This system functions mainly through the exchange of data between the server, terminals, and users. The following hardware and software are used:

[1235] Hardware: High-performance cloud server (e.g. AWS, Google Cloud), smartphone with GPS function

[1236] Software: generative AI models (e.g., GPT-4), databases (e.g., MySQL), mapping applications (e.g., Google Maps), event management systems (e.g., Eventbrite), notification systems (e.g., Firebase), translation APIs (e.g., Google Translate API)

[1237] Recommendation method

[1238] The server uses a generative AI model to recommend books based on the user's reading history and interests. When a user logs in to the app and enters their genre and author information, the server collects that data and inputs prompts into the generative AI model to generate a list of book recommendations.

[1239] Example prompt sentence:

[1240] A user's subscription history and genre interest is mystery novels. Generate a list of the latest mystery novels.

[1241] This recommendation list is sent to the user's terminal, and the recommended book list is displayed on the screen.

[1242] Location information means

[1243] The device acquires the user's location information and provides inventory information for the nearest bookstore or library. When the user enters search keywords and location information, the server uses that information to gather inventory information for the nearest bookstore or library and sends it to the user's device. A pin is displayed on the map, and the user can access that location to obtain the book.

[1244] Example prompt sentence:

[1245] A user searches for Harry Potter and is located in Tokyo. Please provide availability information for the nearest bookstore or library.

[1246] Trading Instruments

[1247] Users register unwanted books on the online marketplace and wish to trade with other users. The server stores the registered book information in a database, and when other users search, matching book information is provided. When a trade is made, the server stores the transaction details and logs.

[1248] Event Management Methods

[1249] The server manages schedules for famous authors and related events and notifies users. When a user applies to participate in an event, the information is sent to the server and the registration is completed. For example, if a "reading group of famous authors" is being held, the information is notified to users, and users who wish to participate can apply.

[1250] Example prompt sentence:

[1251] A reading group for a famous author is being held. Please explain the process for notifying users and accepting applications to participate.

[1252] Translation tools

[1253] The server uses a generative AI model to translate a foreign language book selected by the user and provide it in the user's native language. For example, if the user selects "Le Petit Prince" (French), the server translates the entire text and sends it to the user's device.

[1254] Example prompt sentence:

[1255] Please translate Le Petit Prince (French) into the user's native language (Japanese).

[1256] Surprise recommendation method

[1257] The server will suggest books from different genres and authors based on the user's past reading habits. For example, if a user primarily reads business books, the server might suggest new books such as poetry collections or science fiction novels, providing an opportunity for a new reading experience.

[1258] Example prompt sentence:

[1259] For a user who likes business books, suggest books in different genres, such as poetry collections and science fiction novels.

[1260] Recommendation generator and transaction store

[1261] The server inputs prompts into the generative AI model to generate a list of recommended books. When a book transaction is completed between users, the server stores the transaction details and logs in a database.

[1262] This allows users to satisfy a wide range of reading-related needs in one system, providing accurate book recommendations, convenient book search and purchase, event participation, exploring new genres, and foreign language book translation.

[1263] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1264] Recommendation method

[1265] Step 1:

[1266] A user logs in to the app and enters the genres and author information they are interested in. This information is sent from the app to the server. The input data includes the genres and author information selected by the user.

[1267] Step 2:

[1268] The server collects the user's subscription history and input data. It retrieves past subscription history from the database and integrates it with newly entered genre and author information.

[1269] Specific behavior:

[1270] The server queries the database to retrieve the subscription history associated with the user ID, and then adds the newly entered genre and author information to it.

[1271] Step 3:

[1272] The server inputs the following prompt sentence into the generative AI model to generate a list of book recommendations:

[1273] A user's subscription history and genre interest is mystery novels. Generate a list of the latest mystery novels.

[1274] A generative AI model creates a recommendation list based on this prompt.

[1275] Step 4:

[1276] The server sends the generated recommendation list to the user's device via an API, and the output data includes the recommended book list.

[1277] Step 5:

[1278] The device displays a list of recommended books to the user, who can then view the recommended books on the screen and click on links for more information or to purchase.

[1279] Location information means

[1280] Step 1:

[1281] The user enters a search term and location information. The user turns on location services and enters the search term into the app.

[1282] Step 2:

[1283] The device uses the GPS function to obtain the user's current location. This location information is sent to the server. The GPS information of the current location is included as input data.

[1284] Specific behavior:

[1285] The device activates the GPS sensor, acquires the current location, and sends it to the server.

[1286] Step 3:

[1287] The server collects inventory information from the nearest bookstore or library based on the location information and search keywords, and sends a query to the inventory management system to obtain the inventory information for the corresponding book.

[1288] Specific behavior:

[1289] The server queries a database or API to retrieve inventory information that matches the location and search keywords.

[1290] Step 4:

[1291] The server sends the collected inventory information and its location to the terminal. The output data includes the inventory information and location data.

[1292] Step 5:

[1293] The device will use a map app to display the location of the bookstore or library you retrieved, and a pin will appear on the map, allowing you to visit the location and get the book.

[1294] Trading Instruments

[1295] Step 1:

[1296] The user registers unwanted books in the app and requests a transaction. The user enters the book information and sends the data to the server.

[1297] Step 2:

[1298] The server stores the registered book information in a database. Input data includes the book title, condition, price, contact information, etc.

[1299] Specific behavior:

[1300] The server registers the book information in the database using an INSERT query.

[1301] Step 3:

[1302] Other users search for specific books by entering a search query and sending it to the server.

[1303] Step 4:

[1304] The server retrieves book information that matches the search query from the database, and sends the retrieved book information to the user via API.

[1305] Specific behavior:

[1306] The server sends a SELECT query to the database and retrieves the search results.

[1307] Step 5:

[1308] The server notifies the user of the matching book information, and the search results are displayed on the user's device.

[1309] Step 6:

[1310] When a transaction negotiation begins between users and the transaction is concluded, the server stores the transaction details and log in the database. The output data includes the transaction details and log data.

[1311] Specific behavior:

[1312] The server records the transaction information in the database using an INSERT query.

[1313] Event Management Methods

[1314] Step 1:

[1315] The server registers the schedule of famous authors and related events in a database and notifies the user. The event information is stored in the database.

[1316] Specific behavior:

[1317] The server registers the event information in the database with an INSERT query and sends the information to the notification system.

[1318] Step 2:

[1319] A user applies to participate in an event. The application information is sent to the server and stored in a database.

[1320] Step 3:

[1321] The server completes the registration and sends a confirmation message to the user. The output data includes the registration confirmation message.

[1322] Step 4:

[1323] The server sends a reminder before the event, and the event reminder message is displayed on the user's device.

[1324] Translation tools

[1325] Step 1:

[1326] A user selects a particular foreign language book and submits that information to a server, including the book's foreign language text as input data.

[1327] Step 2:

[1328] The server translates the foreign language book using a generative AI model. The generative AI model is given the following prompt:

[1329] Please translate Le Petit Prince (French) into the user's native language (Japanese).

[1330] The model generates the translation result.

[1331] Step 3:

[1332] The server sends the translated book information to the user's device, and the output data includes the translated text.

[1333] Step 4:

[1334] The device displays the translated text to the user, who can comfortably read the text in their native language.

[1335] Surprise recommendation method

[1336] Step 1:

[1337] The server analyzes the user's past reading habits, retrieves past subscription history from the database, and performs the analysis.

[1338] Specific behavior:

[1339] The server queries the database to analyze the subscription history associated with the user ID.

[1340] Step 2:

[1341] The server inputs the following prompt sentence into the generative AI model to recommend new books that deviate from the user's preferences.

[1342] For a user who likes business books, suggest books in different genres, such as poetry collections and science fiction novels.

[1343] The model creates a new recommendation list.

[1344] Step 3:

[1345] The server sends the generated new recommendation list to the user's device, and the output data includes the new genre and author recommendation list.

[1346] Step 4:

[1347] The device displays a list of newly recommended books to the user, giving the user the opportunity to discover books in a new genre.

[1348] (Application example 1)

[1349] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1350] Conventional reading support systems are limited to recommending books based on a user's interests and reading history, and providing book inventory information. They lack comprehensive support for efficient book searches within bookstores and for improving the user experience. Furthermore, they lack the integration of a wide range of services, such as trading books owned by users, managing event information, and translating foreign language books. As a result, users are forced to use various services individually, resulting in a lack of a consistent reading experience.

[1351] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1352] In this invention, the server includes a recommendation unit using a generative AI model to present recommended books based on the user's reading history and interests, a location information unit that acquires the user's location information and provides inventory information for the nearest bookstore or library, a trading unit that provides an online marketplace for the user to trade books owned by the user with other users, an event management unit that manages event and workshop schedules and notifies the user, a translation unit that translates books in foreign languages ​​and provides them in the user's native language, a surprise recommendation unit that recommends books in new genres or by new authors outside of traditional reading habits, and a smartphone application provision unit that assists navigation within the bookstore and suggests books based on the user's preferences. This allows the user to have a consistent and comprehensive reading experience.

[1353] A "generative AI model" is an algorithm or program that automatically suggests books based on a user's reading history and interests.

[1354] A "recommendation tool" is a mechanism or method for using a generative AI model to suggest suitable books to a user.

[1355] "Location information means" refers to a system or device that acquires the user's current location and provides inventory information for the nearest bookstore or library based on that information.

[1356] "Exchange vehicle" means a method or system that provides an online marketplace for users to trade or sell their books with other users.

[1357] An "event management means" is a method or system for managing the schedule of events or workshops and notifying users.

[1358] A "translation means" is a method or system for automatically translating a book written in a foreign language into a user's native language and providing the results to the user.

[1359] A "surprise recommendation method" is a mechanism or method for recommending books in new genres or by new authors that are outside the user's usual reading habits.

[1360] "Means for providing smartphone applications" refers to a method or system for providing smartphone applications that support efficient book searches and navigation within a bookstore and suggest books based on the user's preferences.

[1361] This invention is a system for improving users' reading experience by providing customized book suggestions and related information using a generative AI model. The main components of this system include data exchange between a server, a terminal, and a user.

[1362] The system has the following main functions:

[1363] 1. Recommendation method:

[1364] The server uses a generative AI model to suggest books based on the user's reading history and interests. This allows users to easily find books that match their interests. An example of a prompt is, "Please suggest books based on mystery novels I've read in the past."

[1365] 2. Location information means:

[1366] The server uses the location information obtained from the device to provide inventory information for the nearest bookstore or library. This function allows users to quickly check where the book they are looking for is in stock, making book searches in bookstores more efficient.

[1367] 3. Trading instruments:

[1368] It provides an online marketplace for users to trade their books with other users, allowing them to give away or sell books they have finished reading to other users. This information is sent to a server and registered in a database.

[1369] 4. Event Management Measures:

[1370] The server manages schedules for events and workshops and notifies users. This function allows users to find out about book clubs by famous authors and related events, and to apply to participate.

[1371] 5. Translation Methods:

[1372] The server translates books in a foreign language into the user's native language and provides the results to the user. This allows the user to enjoy a variety of books across language barriers. As a specific example, the server may translate "Le Petit Prince" (French) into the user's native language and provide it to the user.

[1373] 6. Surprise Recommendation:

[1374] The server suggests books from new genres or authors that deviate from the user's usual reading habits. This allows the user to discover new reading pleasures. An example of a prompt sentence is "Please suggest books from new genres or authors that deviate from the user's usual reading habits."

[1375] 7. Means of providing smartphone applications:

[1376] We provide a smartphone application that assists users in navigating bookstores and guides them to the location of desired books. This application allows users to quickly find specific books in bookstores. Specifically, it provides navigation on a store map based on the user's location information.

[1377] To implement this system, a smartphone is required as hardware. On the software side, Python programming, an API for generative AI models, and Geopy for handling geographic information are used. The server processes this data, generates the information to be provided to the user, and sends it to the device. Specific examples of prompts used include those that suggest books based on the user's interests and reading history, such as the following:

[1378] "Please suggest books based on mystery novels you've read in the past."

[1379] "Generate new book lists based on your genre interests."

[1380] "I'm looking for a work by a particular author. Please list all related works."

[1381] This system allows users to have a consistent and comprehensive reading experience.

[1382] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1383] Step 1: Receive the user request

[1384] A user opens the application and requests book recommendations, including input about the user's reading history, genre interests, and specific author information. This information is then sent to the server.

[1385] Step 2: Data processing by generative AI models

[1386] The server inputs the received user data into a generative AI model and generates a list of recommended books using prompts. The input data includes past reading history and genre interests, and the output data is a list of recommended books. An example prompt is: "Please suggest books based on mystery novels I've read in the past."

[1387] Step 3: Generate and submit a recommendation list

[1388] The server sends the list of recommended books output by the generative AI model to the device. In this process, the recommended book list is sent to the user's smartphone and displayed on the device.

[1389] Step 4: Obtaining location information

[1390] The user turns on location information to search for a specific book. This input includes the user's current location and the book they are looking for. The device sends the location information to the server.

[1391] Step 5: Retrieve and display inventory information

[1392] The server retrieves inventory information from the database of the nearest bookstore or library using the user's location information and search keywords. Based on this information, the server sends the inventory information to the terminal, which displays it to the user. The output includes the book's location and inventory status.

[1393] Step 6: Performing Navigation

[1394] A user enters a bookstore and uses an application to locate a particular book. The input includes the book's ID and the user's current location. The device navigates the book's location within the store and provides the user with specific directions. The output includes the book's location (aisle number and shelf number).

[1395] Step 7: Request a translation

[1396] A user selects a foreign language book and requests that it be translated into their native language. The server uses a generative AI model to translate the foreign language book into the user's native language and sends the text to the user. The input data is the original text, and the output data is the translated text.

[1397] Step 8: Event Information Notification

[1398] The server uses an event management mechanism to notify users of related events. When a user registers to participate in an event, the information is sent to the server, which manages the schedule. Input data includes details of the event and the user's response, and output data includes confirmation of participation in the event.

[1399] These steps will enable us to provide a comprehensive reading experience and improve user convenience and satisfaction.

[1400] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1401] The system of the present invention combines a generative AI model and an emotion engine to provide customized book suggestions and related information to enhance the user's reading experience. This system is realized through the exchange of data between a server, terminals, and users.

[1402] System Programming and Processing

[1403] 1. Book Search Navigator

[1404] The server uses generative AI to suggest recommended books based on data obtained from the user, such as their subscription history and their favorite genres and authors. For example, if a user likes "mystery novels," the server uses that data to generate a list of the latest mystery novels and presents it to the user. When a user enters a specific author using the search function, all works related to that author are returned as search results.

[1405] 2. Book Destination Guide

[1406] The device obtains the user's location information and provides inventory information for the nearest bookstore or library. For example, if a user searches for "Harry Potter" and has location information turned on, the server uses the user's location information and search keywords to obtain inventory information for the nearest bookstore or library and displays it on the device. A pin is displayed on a map, allowing the user to access that location and obtain the book.

[1407] 3. Book Hunting Market

[1408] Users register books they own that they no longer need in the app and wish to trade with other users. This information is sent to the server and registered in the database. When other users search for the book, the server retrieves matching information and notifies the user who wishes to trade. Once negotiations for a trade begin between users and the transaction is concluded, the server saves a log of the transaction.

[1409] 4. Exciting Book Classes

[1410] The server registers event information from famous authors and related parties in a database and notifies users. When a user registers to participate in an event, the information is sent to the server and registration is completed. For example, if a "reading group of famous authors" is being held, the information is notified to users, and users who wish to participate can register. The server then sends reminders.

[1411] 5. Word Adventurer

[1412] When a user selects a book written in a specific foreign language, the server uses generative AI to translate the book into the user's native language. For example, if the user selects "Le Petit Prince" (French), the server uses generative AI to translate the entire text and sends it to the user. The user's device displays the translated text, providing a comfortable reading experience.

[1413] 6. Reading Surprise

[1414] The server retrieves the user's past reading habits from a database and runs an algorithm to suggest books in different genres or by different authors. For example, if a user primarily reads business books, the server might suggest new books such as poetry collections or science fiction novels. This gives users the opportunity to discover new genres.

[1415] 7. Emotion Engine

[1416] The server uses an emotion engine to recognize the user's emotions and recommends books based on those emotions. As a specific example, when a user expresses their emotions using a camera or microphone, the device analyzes the user's facial expressions and tone of voice and sends the data to the server. The server then uses an emotion engine to analyze the emotion data and recommends books that best suit the user's mood and emotions.

[1417] Example of emotion engine

[1418] For example, if a user feels tired, the emotion engine will recommend books in relaxing genres, such as essays or short stories. Conversely, if the user feels excited, it will recommend books in genres such as suspense or action. In this way, book suggestions tailored to the user's current emotional state provide a more personalized reading experience.

[1419] By combining each of the above functions with an emotion engine, the system of the present invention can meet the diverse needs of users and provide a more highly customized reading experience.

[1420] The processing flow will be explained below.

[1421] 1. Book Search Navigator

[1422] Subject: Server

[1423] Step 1:

[1424] When a user logs in, the device sends the user ID and token to the server.

[1425] Step 2:

[1426] The server retrieves the user's subscription history, genres of interest, and author information from the database.

[1427] Step 3:

[1428] The server uses generative AI to calculate book recommendations based on user data.

[1429] Step 4:

[1430] Based on the calculation results, the server creates a list of recommended books and sends it to the device.

[1431] Step 5:

[1432] The terminal displays the received list to the user.

[1433] 2. Book Destination Guide

[1434] Subject: Terminal

[1435] Step 1:

[1436] The user allows the use of location information.

[1437] Step 2:

[1438] The device obtains the user's current location and sends it to the server.

[1439] Step 3:

[1440] The user enters a search keyword and sends it to the server.

[1441] Step 4:

[1442] The server uses the location information and search keywords to retrieve inventory information from the database of the nearest bookstore or library.

[1443] Step 5:

[1444] The server transmits the acquired inventory information to the terminal.

[1445] Step 6:

[1446] The device displays a pin on a map and shows the user the inventory information of the nearest bookstore or library.

[1447] 3. Book Hunting Market

[1448] Subject: User and Server

[1449] Step 1:

[1450] The user inputs information about the books he owns and sends it to the server.

[1451] Step 2:

[1452] The server registers the book information received from the user in a database.

[1453] Step 3:

[1454] Other users search for specific books and send requests to the server.

[1455] Step 4:

[1456] The server searches the database, retrieves the matching book information, and returns it to the requesting user.

[1457] Step 5:

[1458] When a user expresses interest in a particular book and requests a transaction, the server notifies both users.

[1459] Step 6:

[1460] The users negotiate a transaction and the transaction is concluded.

[1461] 4. Exciting Book Classes

[1462] Subject: Server

[1463] Step 1:

[1464] The server receives event information from authors and related parties and registers it in a database.

[1465] Step 2:

[1466] The server notifies the user of the registered event information.

[1467] Step 3:

[1468] When a user wishes to participate, they send a request to the server.

[1469] Step 4:

[1470] The server approves the request and registers the user.

[1471] Step 5:

[1472] The server sends a reminder to the user the day before the event.

[1473] 5. Word Adventurer

[1474] Subject: Server

[1475] Step 1:

[1476] The user selects the book they wish to translate and sends a request to the server.

[1477] Step 2:

[1478] The server launches a generative AI to translate the book text into the user's native language.

[1479] Step 3:

[1480] The server generates the translated text data and sends it to the user.

[1481] Step 4:

[1482] The user's device displays the translated text.

[1483] 6. Reading Surprise

[1484] Subject: Server

[1485] Step 1:

[1486] The server retrieves the user's reading history from the database.

[1487] Step 2:

[1488] The server uses generative AI to run algorithms that recommend new genres and authors outside of the user's interests.

[1489] Step 3:

[1490] The server creates a surprise recommendation list based on the calculation results and sends it to the user.

[1491] Step 4:

[1492] The user's device will display suggestions, encouraging a new reading experience.

[1493] 7. Emotion Engine

[1494] Subject: Server and terminal

[1495] Step 1:

[1496] The user turns on the camera and microphone and allows emotional data capture.

[1497] Step 2:

[1498] The device captures the user's facial expressions and tone of voice in real time and sends the data to a server.

[1499] Step 3:

[1500] The server uses an emotion engine to analyze the received emotion data.

[1501] Step 4:

[1502] Based on the emotional data, the server uses a generative AI model to recommend books that best fit the user's current emotional state.

[1503] Step 5:

[1504] The server creates a recommendation list and sends it to the terminal.

[1505] Step 6:

[1506] The terminal displays a recommendation list based on the received emotions to the user.

[1507] The above specific processing steps enable highly customized reading suggestions tailored to the user's emotional state, allowing users to easily find the book that best suits their mood and emotions at any given time.

[1508] Example 2

[1509] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1510] Conventional book recommendation systems are limited to recommending books based on users' reading history and interests, and lack recommendations based on new genres or emotions. Furthermore, they lack an integrated system that allows users to conveniently check inventory by location information when searching for specific books, trade books with other users, and manage events and workshops, making it difficult to meet the diverse needs of users.

[1511] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1512] In this invention, the server includes a recommendation means using a generative AI model to present recommended books based on the user's reading history and interests, a location information means for acquiring the user's location information and providing inventory information of the nearest retailer or library facility, a trading means for providing an online trading venue for the user to trade books owned by the user with other users, an event management means for managing event and workshop schedules and notifying the user, a translation means for translating books in foreign languages ​​and providing them in the user's native language, a surprise recommendation means for recommending books in new genres or by new authors that are outside of traditional reading trends, and an emotion evaluation means for recognizing the user's emotions based on an emotion engine and recommending books that match those emotions. This makes it possible to meet the diverse needs of users and provide a personalized reading experience.

[1513] A "generative AI model" is an artificial intelligence model that generates a list of recommended books based on a user's reading history and interests.

[1514] A "recommendation method" is a method for recommending books suitable for a user using a generative AI model.

[1515] The "location information means" is a means for acquiring the user's location information and providing inventory information of the nearest retail store or library facility.

[1516] A "trading means" is a means for providing an online trading space for a user to trade books with other users.

[1517] The "event management means" is a means for managing schedules of events and workshops and notifying users of the schedules.

[1518] "Translation means" refers to a means for translating a book in a foreign language into the user's native language and providing it to the user.

[1519] A "surprise recommendation method" is a method for recommending to a user books in new genres or by new authors that deviate from traditional reading habits.

[1520] The "emotion evaluation means" is a means for recognizing the user's emotions using an emotion engine and recommending books that match those emotions.

[1521] The system of the present invention combines a generative AI model, a location information acquisition system, an online trading platform, an event management system, a translation function, a surprise recommendation function, and an emotion evaluation system to meet the diverse reading needs of users. Each component of the system is described in detail below.

[1522] 1. Recommendation method

[1523] The server uses a generative AI model to generate a book list based on the user's reading history and interests. The user inputs their genres and authors of interest through the app interface, and their device sends that information to the server. The server then generates prompts and inputs them into the generative AI model.

[1524] As a concrete example, if the user is interested in "mystery novels," the following prompt sentence may be used:

[1525] Based on your users' reading history and interests, create a list of the latest mystery novels, along with related information and reviews.

[1526] The generated book list is sent to the user's terminal along with additional information and displayed on the screen.

[1527] 2. Location information means

[1528] The device acquires the user's current location and provides inventory information for the nearest retailer or library. When the user searches for a specific book with location information turned on, the device sends that information to the server. The server then consults a database to collect inventory and location information and displays it to the user.

[1529] For example, if you searched for "Harry Potter," you would use the following prompt:

[1530] The user's current location is a specific location. Please provide information about retailers and libraries that stock Harry Potter. Please also display map information.

[1531] As a result, facilities with inventory are displayed on the map and the user can access the location.

[1532] 3. Trading Instruments

[1533] We provide an online trading platform where users can trade unwanted books with other users. Users input information about the book they wish to trade, and their terminals send it to the server. The server registers the information in a database, and when other users search for the same book, it matches them.

[1534] 4. Event Management Tools

[1535] The server has the function of registering event information from authors and event organizers in a database and notifying users. When a user registers to participate in an event, the information is sent to the server and registration is completed. Before the event begins, the server sends a reminder to the user.

[1536] 5. Translation Methods

[1537] When a user selects a book written in a specific foreign language, the server uses a generative AI model to translate the book. After the user selects a book, the device sends the information to the server. The server generates a translation prompt, inputs it into the AI ​​model, and obtains the translation result.

[1538] For example, if the user selects "Le Petit Prince (French)", use the following prompt text:

[1539] Please translate this French book into your native language.

[1540] The translated text is sent to the terminal and displayed to the user.

[1541] 6. Surprise Recommendations

[1542] The server suggests books from new genres and authors that are outside the user's past reading habits, giving the user the opportunity to be exposed to new genres.

[1543] 7. Emotional Assessment Instrument

[1544] The server uses an emotion engine to recognize the user's emotions and recommends books based on those emotions. When the user expresses their emotions using the camera or microphone, the device sends the data to the server for emotion analysis.

[1545] As a concrete example, if it is determined that the user is tired, the following prompt sentence is used:

[1546] List books in genres that users find relaxing.

[1547] The server provides the list obtained from the generative AI model to the user.

[1548] As described above, the system of the present invention can provide users with a personalized and sophisticated reading experience by flexibly combining each function.

[1549] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1550] "Book Search Navigator" program processing flow

[1551] Step 1: Accepting User Input

[1552] Users input genres and authors of interest through the app's interface. The input data is temporarily stored on the user's device (input). Specifically, the user enters "mystery novels" or "a specific author's name" into the app's search bar.

[1553] Step 2: Send data

[1554] The terminal sends the user's input data to the server (output). Specifically, when the send button on the terminal is pressed, the input data is sent to the back-end server.

[1555] Step 3: Data analysis

[1556] The server receives the transmitted data and generates a prompt based on the user's interests (input). The data is analyzed using text mining and natural language processing to communicate with the generative AI model. For example, a prompt might be created that reads, "Generate a list of the latest mystery novels based on the user's reading history and interests."

[1557] Step 4: Query the AI ​​model

[1558] The server inputs the generated prompt sentence into the generative AI model and obtains a list of appropriate books from the generative AI model (output). Specifically, the generative AI model generates a book list based on the prompt sentence and returns it to the server.

[1559] Step 5: List Generation

[1560] The server compares the book list returned by the generative AI model with its internal database and adds additional information such as reviews and ratings for each book (input and output). Specifically, it retrieves review information from the database and merges it with the retrieved book list.

[1561] Step 6: Sending the results

[1562] The server then sends the final book list and additional information to the user's device (output). Specifically, it converts the list into the required data format and sends it to the device using an API.

[1563] Step 7: Display to the user

[1564] The device displays the received book list and additional information in response to the user's request (input). Specifically, the book list is displayed in the app's UI, and when the user clicks, detailed information is displayed in a pop-up.

[1565] "Book Whereabouts Guide" program processing flow

[1566] Step 1: Accepting User Input

[1567] The user enters search keywords for a specific book within the app and turns on location services. The entered data is temporarily stored on the device (input). For example, the user enters "Harry Potter" as a search keyword.

[1568] Step 2: Send data

[1569] The device sends the search keywords and location information to the server (output). Specifically, when the user presses the send button, the device sends the GPS information and search keywords to the server.

[1570] Step 3: Data analysis

[1571] The server receives the location information and search keywords and searches the database for inventory information of the nearest retail store or library facility (input and output).Specifically, it queries facilities within a certain radius from the user's current location and retrieves inventory information that matches the search keywords from the database.

[1572] Step 4: Result generation

[1573] The server generates a list of the nearest retail stores and library facilities based on the location information and search keywords, and adds inventory information to the list (input and output).The server attaches inventory status to the acquired facility information and generates a list.

[1574] Step 5: Sending the results

[1575] The server sends the generated list and inventory information to the user's device (output). Specifically, it uses an API to convert the data into a display format and sends it to the device.

[1576] Step 6: Display to the user

[1577] The device displays the received list and inventory information along with a map to the user (input). Specifically, it uses the app's map function to map the facility's location and displays an icon indicating whether the item is in stock.

[1578] "Book Search Market" program processing flow

[1579] Step 1: Accepting User Input

[1580] The user enters information about the book they wish to trade into the app, including the book's title, author, condition, price, etc. (Input). The entered data is temporarily stored on the device.

[1581] Step 2: Send data

[1582] The terminal sends the entered book information to the server (output). When the user presses the "Register" button, the terminal sends the book information to the server.

[1583] Step 3: Database registration

[1584] The server receives the book information and registers it in the database (input and output). Specifically, it adds a new book record to the database.

[1585] Step 4: Search by other users

[1586] When other users search for a particular book, the keywords are sent to the server (input). Specifically, the user enters the keywords into the search bar and executes the search.

[1587] Step 5: Database Matching

[1588] The server matches the search keywords with the desired book information in the database and presents the matching books to the user who searches (input and output). Specifically, it queries the book records that match the keywords and generates the results.

[1589] Step 6: Submit matches

[1590] The server notifies the searched user and the user who wishes to trade of the matching book information (output). Specifically, it sends a notification email or an in-app notification to the user who wishes to trade.

[1591] Step 7: Negotiate the deal

[1592] A deal is negotiated between two users, who use the in-app chat feature to discuss the price and details of the deal (input and output).

[1593] Step 8: Keep a transaction log

[1594] When a transaction is completed, the server saves the transaction log in the database (input and output) and records the transaction details in the transaction log table.

[1595] (Application example 2)

[1596] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1597] In recent years, electronic payment services and online book sales platforms have become popular, but simply purchasing a book is not enough to enrich the user's reading experience. There is a need for a system that can recommend books and support purchases in an appropriate and timely manner based on the user's individual interests and emotional state. However, existing systems lack advanced customization based on the user's emotions and reading habits, and are therefore unable to provide the optimal reading experience for the user. To solve this problem, a system is needed that can recommend books based on the user's reading history, interests, and emotional state, and then allow for electronic payment.

[1598] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a recommendation unit using a generative AI model to present recommended books based on the user's reading history and interests; a location information unit that acquires the user's location information and provides inventory information for the nearest bookstore or library; a transaction unit that provides an online marketplace for the user to trade books owned by the user with other users; an event management unit that manages event and workshop schedules and notifies the user; a translation unit that translates books in foreign languages ​​and provides them in the user's native language; a surprise recommendation unit that recommends books in new genres or by authors that deviate from traditional reading habits; an emotion recommendation unit that analyzes the user's emotional state and recommends books based on the user's emotions; and an electronic payment unit that can automatically purchase books based on the user's reading history and emotions. This enables customized book recommendations based on the user's individual interests and emotional state and a one-stop purchasing experience.

[1599] A "generative AI model" is an artificial intelligence algorithm that recommends books based on a user's reading history and interests.

[1600] "Recommendation method" refers to a function that uses a generative AI model to suggest the most suitable books to the user.

[1601] "Location information means" is a function that acquires the user's current location and provides inventory information for the nearest bookstore or library.

[1602] "Transaction means" is a function that provides an online marketplace for users to trade books they own with other users.

[1603] The "event management means" is a function for managing schedules for events and workshops and notifying users of them.

[1604] "Translation means" is a function that translates books in foreign languages ​​and provides them in the user's native language.

[1605] The "surprise recommendation means" is a function that recommends books by new genres or authors that deviate from the user's usual reading habits.

[1606] The "emotion recommendation means" is a function that analyzes the user's emotional state and recommends books based on that emotion.

[1607] "Electronic payment methods" is a feature that allows users to automatically purchase books based on their subscription history and sentiment.

[1608] The system of the present invention combines a generative AI model and an emotion engine to provide customized book suggestions and related information to enhance the user's reading experience. This system is realized through the exchange of data between a server, terminals, and users.

[1609] System Programming and Processing

[1610] 1. Recommendation methods using generative AI models

[1611] The server uses generative AI to suggest recommended books based on data obtained from the user, such as their subscription history and their favorite genres and authors. For example, if a user likes "mystery novels," the server uses that data to generate a list of the latest mystery novels and presents it to the user. When a user enters a specific author using the search function, all works related to that author are returned as search results.

[1612] 2. Location information means

[1613] The device acquires the user's location information and provides inventory information for the nearest bookstore or library. For example, if a user searches for a specific book and has location information turned on, the server uses the user's location information and search keywords to acquire inventory information for the nearest bookstore or library and displays it on the device. A pin is displayed on a map, allowing the user to access that location and obtain the book.

[1614] 3. Trading instruments that provide online marketplaces

[1615] Users register books they own that they no longer need in the app and wish to trade with other users. This information is sent to the server and registered in the database. When other users search for the book, the server retrieves matching information and notifies the user who wishes to trade. Once negotiations for a trade begin between users and the transaction is concluded, the server saves a log of the transaction.

[1616] 4. Event Management Tools

[1617] The server registers event information from famous authors and related parties in a database and notifies users. When a user registers to participate in an event, the information is sent to the server and registration is completed. For example, if a "reading group of famous authors" is being held, the information is notified to users, and users who wish to participate can register. The server then sends reminders.

[1618] 5. Translation Methods

[1619] When a user selects a book written in a specific foreign language, the server uses generative AI to translate the book into the user's native language. When a user selects a specific book, the server uses generative AI to translate the entire text and sends it to the user. The user's device displays the translated text, providing a comfortable reading experience. For example, if a user selects "Le Petit Prince" (French), the server uses generative AI to translate the entire text and sends it to the user.

[1620] 6. Surprise Recommendations

[1621] The server retrieves the user's past reading habits from a database and runs an algorithm to suggest books in different genres or by different authors. For example, if a user primarily reads business books, the server might suggest new books such as poetry collections or science fiction novels. This gives users the opportunity to discover new genres.

[1622] 7. Emotion recommendation means

[1623] The server uses an emotion engine to recognize the user's emotions and recommends books based on those emotions. As a specific example, when a user expresses emotions using a camera or microphone, the device analyzes the user's facial expressions and tone of voice and sends the data to the server. The server then analyzes the emotion data using an emotion engine and recommends books that best suit the user's mood and emotions. For example, if the user feels tired, the emotion engine will recommend books in relaxing genres, such as essays and short stories. Conversely, if the user is excited, it will recommend books in genres such as suspense and action.

[1624] 8. Electronic Payment Methods

[1625] This system allows users to automatically purchase books based on their subscription history and emotions. The server processes payment information and completes the purchase process for the book selected by the user. For example, when a user selects a suggested book and presses the purchase button, the server completes the payment through a payment gateway and provides the book to the user in digital format.

[1626] This allows users to enjoy a one-stop shopping experience with customized book recommendations based on their individual interests and emotional state.

[1627] Specific usage examples and prompts

[1628] For example, suppose user "A" likes mystery novels and has been feeling stressed lately. In this case, the emotion engine can detect this stressful state and recommend relaxing essays or short stories. An example prompt for the generative AI model is as follows:

[1629] "The user's reading history primarily includes mystery novels. They're also currently feeling stressed. Suggest five relaxing essays and short stories to them."

[1630] Based on these prompts, the generative AI model suggests the best books for the user, who can then easily purchase them using electronic payment methods.

[1631] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1632] Step 1:

[1633] A user logs in to the app and enters data such as their subscription history, genres of interest, authors, etc. This data is sent from the device to the server and saved as a user profile.

[1634] Input: User subscription history, genre interests, author data

[1635] Output: User profile stored on the server

[1636] What happens: A user enters data into a form in your app, and that data is sent to your server.

[1637] Step 2:

[1638] The server generates book recommendations by sending prompts to the generative AI model based on the user's profile information. The prompts include information about the user's interests and past reading habits.

[1639] Input: User profile, prompt

[1640] Output: Book recommendation list

[1641] How it works: The generative AI model generates a list of book recommendations based on the prompt.

[1642] Step 3:

[1643] The generated book recommendation list is sent from the server to the terminal and presented to the user, who then selects the books they want to read from the presented list.

[1644] Input: Book recommendation list

[1645] Output: A list of recommended books displayed on the user's device

[1646] Specific operation: Data is sent from the server to the device, and a list is displayed on the app screen.

[1647] Step 4:

[1648] When a user selects a particular book, the server uses an emotion engine to recognize the user's emotions and re-adjusts book recommendations based on that data.

[1649] Input: User book selection, emotion data

[1650] Output: A refined list of book recommendations based on emotions

[1651] How it works: The device analyzes the user's facial expressions and tone of voice, and sends the data to the server, which then analyzes the data using an emotion engine.

[1652] Step 5:

[1653] The server generates a book recommendation list based on the emotions and sends it back to the user's device, where the user can select books they want to purchase from the new recommendation list.

[1654] Input: A list of book recommendations based on sentiment

[1655] Output: The re-adjusted recommended book list displayed on the user's device.

[1656] Specific operation: The server sends the recommendation list to the device, which is then displayed in the app.

[1657] Step 6:

[1658] When a user selects a book to purchase and presses the purchase button, the server processes the payment information and completes the electronic transaction. Once the payment is complete, the book is provided to the user.

[1659] Input: User purchase selection, payment information

[1660] Output: Purchase completion notification, book provided to user

[1661] What happens: The server processes the payment through the payment gateway and adds the book data to the user's account.

[1662] 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.

[1663] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1664] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1665] [Fourth embodiment]

[1666] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1667] 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.

[1668] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1669] 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.

[1670] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1671] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1672] 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.

[1673] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1674] 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.

[1675] 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 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.

[1676] 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.

[1677] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1678] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1679] The system of the present invention combines a generative AI model with various functions to provide customized book suggestions and related information to improve the user's reading experience. The main components of this system include data exchange between a server, terminals, and users.

[1680] System Programming and Processing

[1681] 1. Book Search Navigator

[1682] The server uses generative AI to suggest recommended books based on data acquired from the user, such as their subscription history, genres of interest, and authors. For example, if a user likes "mystery novels," the server uses that data to generate a list of the latest mystery novels and presents it to the user. When a user enters a specific author using the search function, all works related to that author are returned as search results.

[1683] 2. Book Destination Guide

[1684] The device obtains the user's location information and provides inventory information for the nearest bookstore or library. For example, if a user searches for "Harry Potter" and has location information turned on, the server uses the user's location information and search keywords to obtain inventory information for the nearest bookstore or library, and the device displays it to the user. A pin is displayed on a map, and the user can access that location to obtain the book.

[1685] 3. Book Hunting Market

[1686] Users register books they own that they no longer need in the app and wish to trade with other users. This information is sent to the server and registered in the database. When other users search for the book, the server retrieves matching information and notifies the user who wishes to trade. Once negotiations for a trade begin between users and the transaction is concluded, the server saves a log of the transaction.

[1687] 4. Exciting Book Classes

[1688] The server registers information about famous authors and related events in a database and notifies users. When a user registers to participate in an event, the information is sent to the server, and registration is completed. For example, if a "reading group by a famous author" is being held, users are notified of the information and those who wish to participate can register. The server then sends reminders.

[1689] 5. Word Adventurer

[1690] When a user selects a book written in a specific foreign language, the server uses generative AI to translate the book into the user's native language. For example, if the user selects "Le Petit Prince" (French), the server uses generative AI to translate the entire text and sends it to the user. The user's device then displays the translated text, providing a comfortable reading experience.

[1691] 6. Reading Surprise

[1692] The server acquires the user's past reading habits and runs an algorithm to suggest books in different genres or by different authors. For example, for a user who mainly reads "business books," the server might suggest new books such as "poetry collections" or "science fiction novels." This gives the user the opportunity to be exposed to new genres.

[1693] Through these functions, the system of the present invention can provide a diverse and customized reading experience, enhancing users' interest in books. This allows users to easily find and conveniently obtain books they want to read, while also encouraging them to explore new reading areas.

[1694] The processing flow will be explained below.

[1695] 1. Book Search Navigator

[1696] Subject: Server

[1697] Step 1:

[1698] When a user logs in, the device sends the user ID and token to the server.

[1699] Step 2:

[1700] The server retrieves the user's subscription history, genres of interest, and author information from the database.

[1701] Step 3:

[1702] The server uses generative AI to calculate book recommendations based on user data.

[1703] Step 4:

[1704] Based on the calculation results, the server creates a list of recommended books and sends it to the device.

[1705] Step 5:

[1706] The terminal displays the received list to the user.

[1707] 2. Book Destination Guide

[1708] Subject: Terminal

[1709] Step 1:

[1710] The user allows the use of location information.

[1711] Step 2:

[1712] The device obtains the user's current location and sends it to the server.

[1713] Step 3:

[1714] The user enters a search keyword and sends it to the server.

[1715] Step 4:

[1716] The server uses the location information and search keywords to retrieve inventory information from the database of the nearest bookstore or library.

[1717] Step 5:

[1718] The server transmits the acquired inventory information to the terminal.

[1719] Step 6:

[1720] The device displays a pin on a map and shows the user the inventory information of the nearest bookstore or library.

[1721] 3. Book Hunting Market

[1722] Subject: User and Server

[1723] Step 1:

[1724] The user inputs information about the books he owns and sends it to the server.

[1725] Step 2:

[1726] The server registers the book information received from the user in a database.

[1727] Step 3:

[1728] Other users search for specific books and send requests to the server.

[1729] Step 4:

[1730] The server searches the database, retrieves the matching book information, and returns it to the requesting user.

[1731] Step 5:

[1732] When a user expresses interest in a particular book and requests a transaction, the server notifies both users.

[1733] Step 6:

[1734] The users negotiate a transaction and the transaction is concluded.

[1735] 4. Exciting Book Classes

[1736] Subject: Server

[1737] Step 1:

[1738] The server receives event information from authors and related parties and registers it in a database.

[1739] Step 2:

[1740] The server notifies the user of the registered event information.

[1741] Step 3:

[1742] When a user wishes to participate, they send a request to the server.

[1743] Step 4:

[1744] The server approves the request and registers the user.

[1745] Step 5:

[1746] The server sends a reminder to the user the day before the event.

[1747] 5. Word Adventurer

[1748] Subject: Server

[1749] Step 1:

[1750] The user selects the book they wish to translate and sends a request to the server.

[1751] Step 2:

[1752] The server launches a generative AI to translate the book text into the user's native language.

[1753] Step 3:

[1754] The server generates the translated text data and sends it to the user.

[1755] Step 4:

[1756] The user's device displays the translated text.

[1757] 6. Reading Surprise

[1758] Subject: Server

[1759] Step 1:

[1760] The server retrieves the user's reading history from the database.

[1761] Step 2:

[1762] The server uses generative AI to run algorithms that recommend new genres and authors outside of the user's interests.

[1763] Step 3:

[1764] The server creates a surprise recommendation list based on the calculation results and sends it to the user.

[1765] Step 4:

[1766] The user's device will display suggestions, encouraging a new reading experience.

[1767] The above processing steps realize a system that can provide a customized reading experience to the user.

[1768] Example 1

[1769] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1770] For many modern book lovers, selecting and acquiring the right books to enhance their reading experience is a key challenge. In addition, they have diverse needs, such as attending events of interest, trading unwanted books, exploring new genres, and translating foreign language books. Conventional systems must address these needs individually, requiring users to use multiple applications and services, which is inconvenient. Furthermore, recommendations based on a user's reading history and interests often lack accuracy. It is desirable to solve these issues with a single system.

[1771] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1772] In this invention, the server includes a recommendation unit using a generative AI model to present recommended books based on the user's reading history and interests, a location information unit that acquires the user's location information and provides inventory information for the nearest bookstore or library, a transaction unit that provides an online marketplace for users to trade books they own with other users, an event management unit that manages event and workshop schedules and notifies users, a translation unit that translates books in foreign languages ​​and provides them in the user's native language, a surprise recommendation unit that recommends books in new genres or by authors outside of traditional reading trends, a recommendation generation unit that inputs prompts into the generative AI model to generate a list of recommended books, and a transaction storage unit that stores transaction details and logs when a book transaction between users is completed. This allows users to satisfy a wide range of reading-related needs with a single system, enabling highly accurate book recommendations, convenient book search and purchase, event participation, exploration of new genres, and translation of foreign language books.

[1773] 1. "Recommendation" is a feature that uses a generative AI model to suggest books based on a user's reading history and interests.

[1774] 2. "Location information means" is a function that obtains the user's location information and provides inventory information of the nearest bookstore or library.

[1775] 3. "Transaction Means" means a feature that provides an online marketplace for users to trade books they own with other users.

[1776] 4. "Event management means" is a function that manages the schedule of events and workshops and notifies users.

[1777] 5. "Translation means" refers to the function of translating books in foreign languages ​​and providing them in the user's native language.

[1778] 6. "Surprise recommendation" is a function that recommends books in new genres or by new authors that deviate from traditional reading trends.

[1779] 7. "Recommendation generation means" is a function that generates a book recommendation list by inputting a prompt sentence into the generative AI model.

[1780] 8. "Transaction storage means" is a function that stores transaction details and logs when a book transaction is concluded between users.

[1781] The present invention provides a system that combines a generative AI model with various functions to improve the user's reading experience. Specific embodiments of the system are described below.

[1782] System Configuration

[1783] This system functions mainly through the exchange of data between the server, terminals, and users. The following hardware and software are used:

[1784] Hardware: High-performance cloud server (e.g. AWS, Google Cloud), smartphone with GPS function

[1785] Software: generative AI models (e.g., GPT-4), databases (e.g., MySQL), mapping applications (e.g., Google Maps), event management systems (e.g., Eventbrite), notification systems (e.g., Firebase), translation APIs (e.g., Google Translate API)

[1786] Recommendation method

[1787] The server uses a generative AI model to recommend books based on the user's reading history and interests. When a user logs in to the app and enters their genre and author information, the server collects that data and inputs prompts into the generative AI model to generate a list of book recommendations.

[1788] Example prompt sentence:

[1789] A user's subscription history and genre interest is mystery novels. Generate a list of the latest mystery novels.

[1790] This recommendation list is sent to the user's terminal, and the recommended book list is displayed on the screen.

[1791] Location information means

[1792] The device acquires the user's location information and provides inventory information for the nearest bookstore or library. When the user enters search keywords and location information, the server uses that information to gather inventory information for the nearest bookstore or library and sends it to the user's device. A pin is displayed on the map, and the user can access that location to obtain the book.

[1793] Example prompt sentence:

[1794] A user searches for Harry Potter and is located in Tokyo. Please provide availability information for the nearest bookstore or library.

[1795] Trading Instruments

[1796] Users register unwanted books on the online marketplace and wish to trade with other users. The server stores the registered book information in a database, and when other users search, matching book information is provided. When a trade is made, the server stores the transaction details and logs.

[1797] Event Management Methods

[1798] The server manages schedules for famous authors and related events and notifies users. When a user applies to participate in an event, the information is sent to the server and the registration is completed. For example, if a "reading group of famous authors" is being held, the information is notified to users, and users who wish to participate can apply.

[1799] Example prompt sentence:

[1800] A reading group for a famous author is being held. Please explain the process for notifying users and accepting applications to participate.

[1801] Translation tools

[1802] The server uses a generative AI model to translate a foreign language book selected by the user and provide it in the user's native language. For example, if the user selects "Le Petit Prince" (French), the server translates the entire text and sends it to the user's device.

[1803] Example prompt sentence:

[1804] Please translate Le Petit Prince (French) into the user's native language (Japanese).

[1805] Surprise recommendation method

[1806] The server will suggest books from different genres and authors based on the user's past reading habits. For example, if a user primarily reads business books, the server might suggest new books such as poetry collections or science fiction novels, providing an opportunity for a new reading experience.

[1807] Example prompt sentence:

[1808] For a user who likes business books, suggest books in different genres, such as poetry collections and science fiction novels.

[1809] Recommendation generator and transaction store

[1810] The server inputs prompts into the generative AI model to generate a list of recommended books. When a book transaction is completed between users, the server stores the transaction details and logs in a database.

[1811] This allows users to satisfy a wide range of reading-related needs in one system, providing accurate book recommendations, convenient book search and purchase, event participation, exploring new genres, and foreign language book translation.

[1812] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1813] Recommendation method

[1814] Step 1:

[1815] A user logs in to the app and enters the genres and author information they are interested in. This information is sent from the app to the server. The input data includes the genres and author information selected by the user.

[1816] Step 2:

[1817] The server collects the user's subscription history and input data. It retrieves past subscription history from the database and integrates it with newly entered genre and author information.

[1818] Specific behavior:

[1819] The server queries the database to retrieve the subscription history associated with the user ID, and then adds the newly entered genre and author information to it.

[1820] Step 3:

[1821] The server inputs the following prompt sentence into the generative AI model to generate a list of book recommendations:

[1822] A user's subscription history and genre interest is mystery novels. Generate a list of the latest mystery novels.

[1823] A generative AI model creates a recommendation list based on this prompt.

[1824] Step 4:

[1825] The server sends the generated recommendation list to the user's device via an API, and the output data includes the recommended book list.

[1826] Step 5:

[1827] The device displays a list of recommended books to the user, who can then view the recommended books on the screen and click on links for more information or to purchase.

[1828] Location information means

[1829] Step 1:

[1830] The user enters a search term and location information. The user turns on location services and enters the search term into the app.

[1831] Step 2:

[1832] The device uses the GPS function to obtain the user's current location. This location information is sent to the server. The GPS information of the current location is included as input data.

[1833] Specific behavior:

[1834] The device activates the GPS sensor, acquires the current location, and sends it to the server.

[1835] Step 3:

[1836] The server collects inventory information from the nearest bookstore or library based on the location information and search keywords, and sends a query to the inventory management system to obtain the inventory information for the corresponding book.

[1837] Specific behavior:

[1838] The server queries a database or API to retrieve inventory information that matches the location and search keywords.

[1839] Step 4:

[1840] The server sends the collected inventory information and its location to the terminal. The output data includes the inventory information and location data.

[1841] Step 5:

[1842] The device will use a map app to display the location of the bookstore or library you retrieved, and a pin will appear on the map, allowing you to visit the location and get the book.

[1843] Trading Instruments

[1844] Step 1:

[1845] The user registers unwanted books in the app and requests a transaction. The user enters the book information and sends the data to the server.

[1846] Step 2:

[1847] The server stores the registered book information in a database. Input data includes the book title, condition, price, contact information, etc.

[1848] Specific behavior:

[1849] The server registers the book information in the database using an INSERT query.

[1850] Step 3:

[1851] Other users search for specific books by entering a search query and sending it to the server.

[1852] Step 4:

[1853] The server retrieves book information that matches the search query from the database, and sends the retrieved book information to the user via API.

[1854] Specific behavior:

[1855] The server sends a SELECT query to the database and retrieves the search results.

[1856] Step 5:

[1857] The server notifies the user of the matching book information, and the search results are displayed on the user's device.

[1858] Step 6:

[1859] When a transaction negotiation begins between users and the transaction is concluded, the server stores the transaction details and log in the database. The output data includes the transaction details and log data.

[1860] Specific behavior:

[1861] The server records the transaction information in the database using an INSERT query.

[1862] Event Management Methods

[1863] Step 1:

[1864] The server registers the schedule of famous authors and related events in a database and notifies the user. The event information is stored in the database.

[1865] Specific behavior:

[1866] The server registers the event information in the database with an INSERT query and sends the information to the notification system.

[1867] Step 2:

[1868] A user applies to participate in an event. The application information is sent to the server and stored in a database.

[1869] Step 3:

[1870] The server completes the registration and sends a confirmation message to the user. The output data includes the registration confirmation message.

[1871] Step 4:

[1872] The server sends a reminder before the event, and the event reminder message is displayed on the user's device.

[1873] Translation tools

[1874] Step 1:

[1875] A user selects a particular foreign language book and submits that information to a server, including the book's foreign language text as input data.

[1876] Step 2:

[1877] The server translates the foreign language book using a generative AI model. The generative AI model is given the following prompt:

[1878] Please translate Le Petit Prince (French) into the user's native language (Japanese).

[1879] The model generates the translation result.

[1880] Step 3:

[1881] The server sends the translated book information to the user's device, and the output data includes the translated text.

[1882] Step 4:

[1883] The device displays the translated text to the user, who can comfortably read the text in their native language.

[1884] Surprise recommendation method

[1885] Step 1:

[1886] The server analyzes the user's past reading habits, retrieves past subscription history from the database, and performs the analysis.

[1887] Specific behavior:

[1888] The server queries the database to analyze the subscription history associated with the user ID.

[1889] Step 2:

[1890] The server inputs the following prompt sentence into the generative AI model to recommend new books that deviate from the user's preferences.

[1891] For a user who likes business books, suggest books in different genres, such as poetry collections and science fiction novels.

[1892] The model creates a new recommendation list.

[1893] Step 3:

[1894] The server sends the generated new recommendation list to the user's device, and the output data includes the new genre and author recommendation list.

[1895] Step 4:

[1896] The device displays a list of newly recommended books to the user, giving the user the opportunity to discover books in a new genre.

[1897] (Application example 1)

[1898] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1899] Conventional reading support systems are limited to recommending books based on a user's interests and reading history, and providing book inventory information. They lack comprehensive support for efficient book searches within bookstores and for improving the user experience. Furthermore, they lack the integration of a wide range of services, such as trading books owned by users, managing event information, and translating foreign language books. As a result, users are forced to use various services individually, resulting in a lack of a consistent reading experience.

[1900] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1901] In this invention, the server includes a recommendation unit using a generative AI model to present recommended books based on the user's reading history and interests, a location information unit that acquires the user's location information and provides inventory information for the nearest bookstore or library, a trading unit that provides an online marketplace for the user to trade books owned by the user with other users, an event management unit that manages event and workshop schedules and notifies the user, a translation unit that translates books in foreign languages ​​and provides them in the user's native language, a surprise recommendation unit that recommends books in new genres or by new authors outside of traditional reading habits, and a smartphone application provision unit that assists navigation within the bookstore and suggests books based on the user's preferences. This allows the user to have a consistent and comprehensive reading experience.

[1902] A "generative AI model" is an algorithm or program that automatically suggests books based on a user's reading history and interests.

[1903] A "recommendation tool" is a mechanism or method for using a generative AI model to suggest suitable books to a user.

[1904] "Location information means" refers to a system or device that acquires the user's current location and provides inventory information for the nearest bookstore or library based on that information.

[1905] "Exchange vehicle" means a method or system that provides an online marketplace for users to trade or sell their books with other users.

[1906] An "event management means" is a method or system for managing the schedule of events or workshops and notifying users.

[1907] A "translation means" is a method or system for automatically translating a book written in a foreign language into a user's native language and providing the results to the user.

[1908] A "surprise recommendation method" is a mechanism or method for recommending books in new genres or by new authors that are outside the user's usual reading habits.

[1909] "Means for providing smartphone applications" refers to a method or system for providing smartphone applications that support efficient book searches and navigation within a bookstore and suggest books based on the user's preferences.

[1910] This invention is a system for improving users' reading experience by providing customized book suggestions and related information using a generative AI model. The main components of this system include data exchange between a server, a terminal, and a user.

[1911] The system has the following main functions:

[1912] 1. Recommendation method:

[1913] The server uses a generative AI model to suggest books based on the user's reading history and interests. This allows users to easily find books that match their interests. An example of a prompt is, "Please suggest books based on mystery novels I've read in the past."

[1914] 2. Location information means:

[1915] The server uses the location information obtained from the device to provide inventory information for the nearest bookstore or library. This function allows users to quickly check where the book they are looking for is in stock, making book searches in bookstores more efficient.

[1916] 3. Trading instruments:

[1917] It provides an online marketplace for users to trade their books with other users, allowing them to give away or sell books they have finished reading to other users. This information is sent to a server and registered in a database.

[1918] 4. Event Management Measures:

[1919] The server manages schedules for events and workshops and notifies users. This function allows users to find out about book clubs by famous authors and related events, and to apply to participate.

[1920] 5. Translation Methods:

[1921] The server translates books in a foreign language into the user's native language and provides the results to the user. This allows the user to enjoy a variety of books across language barriers. As a specific example, the server may translate "Le Petit Prince" (French) into the user's native language and provide it to the user.

[1922] 6. Surprise Recommendation:

[1923] The server suggests books from new genres or authors that deviate from the user's usual reading habits. This allows the user to discover new reading pleasures. An example of a prompt sentence is "Please suggest books from new genres or authors that deviate from the user's usual reading habits."

[1924] 7. Means of providing smartphone applications:

[1925] We provide a smartphone application that assists users in navigating bookstores and guides them to the location of desired books. This application allows users to quickly find specific books in bookstores. Specifically, it provides navigation on a store map based on the user's location information.

[1926] To implement this system, a smartphone is required as hardware. On the software side, Python programming, an API for generative AI models, and Geopy for handling geographic information are used. The server processes this data, generates the information to be provided to the user, and sends it to the device. Specific examples of prompts used include those that suggest books based on the user's interests and reading history, such as the following:

[1927] "Please suggest books based on mystery novels you've read in the past."

[1928] "Generate new book lists based on your genre interests."

[1929] "I'm looking for a work by a particular author. Please list all related works."

[1930] This system allows users to have a consistent and comprehensive reading experience.

[1931] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1932] Step 1: Receive the user request

[1933] A user opens the application and requests book recommendations, including input about the user's reading history, genre interests, and specific author information. This information is then sent to the server.

[1934] Step 2: Data processing by generative AI models

[1935] The server inputs the received user data into a generative AI model and generates a list of recommended books using prompts. The input data includes past reading history and genre interests, and the output data is a list of recommended books. An example prompt is: "Please suggest books based on mystery novels I've read in the past."

[1936] Step 3: Generate and submit a recommendation list

[1937] The server sends the list of recommended books output by the generative AI model to the device. In this process, the recommended book list is sent to the user's smartphone and displayed on the device.

[1938] Step 4: Obtaining location information

[1939] The user turns on location information to search for a specific book. This input includes the user's current location and the book they are looking for. The device sends the location information to the server.

[1940] Step 5: Retrieve and display inventory information

[1941] The server retrieves inventory information from the database of the nearest bookstore or library using the user's location information and search keywords. Based on this information, the server sends the inventory information to the terminal, which displays it to the user. The output includes the book's location and inventory status.

[1942] Step 6: Performing Navigation

[1943] A user enters a bookstore and uses an application to locate a particular book. The input includes the book's ID and the user's current location. The device navigates the book's location within the store and provides the user with specific directions. The output includes the book's location (aisle number and shelf number).

[1944] Step 7: Request a translation

[1945] A user selects a foreign language book and requests that it be translated into their native language. The server uses a generative AI model to translate the foreign language book into the user's native language and sends the text to the user. The input data is the original text, and the output data is the translated text.

[1946] Step 8: Event Information Notification

[1947] The server uses an event management mechanism to notify users of related events. When a user registers to participate in an event, the information is sent to the server, which manages the schedule. Input data includes details of the event and the user's response, and output data includes confirmation of participation in the event.

[1948] These steps will enable us to provide a comprehensive reading experience and improve user convenience and satisfaction.

[1949] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1950] The system of the present invention combines a generative AI model and an emotion engine to provide customized book suggestions and related information to enhance the user's reading experience. This system is realized through the exchange of data between a server, terminals, and users.

[1951] System Programming and Processing

[1952] 1. Book Search Navigator

[1953] The server uses generative AI to suggest recommended books based on data obtained from the user, such as their subscription history and their favorite genres and authors. For example, if a user likes "mystery novels," the server uses that data to generate a list of the latest mystery novels and presents it to the user. When a user enters a specific author using the search function, all works related to that author are returned as search results.

[1954] 2. Book Destination Guide

[1955] The device obtains the user's location information and provides inventory information for the nearest bookstore or library. For example, if a user searches for "Harry Potter" and has location information turned on, the server uses the user's location information and search keywords to obtain inventory information for the nearest bookstore or library and displays it on the device. A pin is displayed on a map, allowing the user to access that location and obtain the book.

[1956] 3. Book Hunting Market

[1957] Users register books they own that they no longer need in the app and wish to trade with other users. This information is sent to the server and registered in the database. When other users search for the book, the server retrieves matching information and notifies the user who wishes to trade. Once negotiations for a trade begin between users and the transaction is concluded, the server saves a log of the transaction.

[1958] 4. Exciting Book Classes

[1959] The server registers event information from famous authors and related parties in a database and notifies users. When a user registers to participate in an event, the information is sent to the server and registration is completed. For example, if a "reading group of famous authors" is being held, the information is notified to users, and users who wish to participate can register. The server then sends reminders.

[1960] 5. Word Adventurer

[1961] When a user selects a book written in a specific foreign language, the server uses generative AI to translate the book into the user's native language. For example, if the user selects "Le Petit Prince" (French), the server uses generative AI to translate the entire text and sends it to the user. The user's device displays the translated text, providing a comfortable reading experience.

[1962] 6. Reading Surprise

[1963] The server retrieves the user's past reading habits from a database and runs an algorithm to suggest books in different genres or by different authors. For example, if a user primarily reads business books, the server might suggest new books such as poetry collections or science fiction novels. This gives users the opportunity to discover new genres.

[1964] 7. Emotion Engine

[1965] The server uses an emotion engine to recognize the user's emotions and recommends books based on those emotions. As a specific example, when a user expresses their emotions using a camera or microphone, the device analyzes the user's facial expressions and tone of voice and sends the data to the server. The server then uses an emotion engine to analyze the emotion data and recommends books that best suit the user's mood and emotions.

[1966] Example of emotion engine

[1967] For example, if a user feels tired, the emotion engine will recommend books in relaxing genres, such as essays or short stories. Conversely, if the user feels excited, it will recommend books in genres such as suspense or action. In this way, book suggestions tailored to the user's current emotional state provide a more personalized reading experience.

[1968] By combining each of the above functions with an emotion engine, the system of the present invention can meet the diverse needs of users and provide a more highly customized reading experience.

[1969] The processing flow will be explained below.

[1970] 1. Book Search Navigator

[1971] Subject: Server

[1972] Step 1:

[1973] When a user logs in, the device sends the user ID and token to the server.

[1974] Step 2:

[1975] The server retrieves the user's subscription history, genres of interest, and author information from the database.

[1976] Step 3:

[1977] The server uses generative AI to calculate book recommendations based on user data.

[1978] Step 4:

[1979] Based on the calculation results, the server creates a list of recommended books and sends it to the device.

[1980] Step 5:

[1981] The terminal displays the received list to the user.

[1982] 2. Book Destination Guide

[1983] Subject: Terminal

[1984] Step 1:

[1985] The user allows the use of location information.

[1986] Step 2:

[1987] The device obtains the user's current location and sends it to the server.

[1988] Step 3:

[1989] The user enters a search keyword and sends it to the server.

[1990] Step 4:

[1991] The server uses the location information and search keywords to retrieve inventory information from the database of the nearest bookstore or library.

[1992] Step 5:

[1993] The server transmits the acquired inventory information to the terminal.

[1994] Step 6:

[1995] The device displays a pin on a map and shows the user the inventory information of the nearest bookstore or library.

[1996] 3. Book Hunting Market

[1997] Subject: User and Server

[1998] Step 1:

[1999] The user inputs information about the books he owns and sends it to the server.

[2000] Step 2:

[2001] The server registers the book information received from the user in a database.

[2002] Step 3:

[2003] Other users search for specific books and send requests to the server.

[2004] Step 4:

[2005] The server searches the database, retrieves the matching book information, and returns it to the requesting user.

[2006] Step 5:

[2007] When a user expresses interest in a particular book and requests a transaction, the server notifies both users.

[2008] Step 6:

[2009] The users negotiate a transaction and the transaction is concluded.

[2010] 4. Exciting Book Classes

[2011] Subject: Server

[2012] Step 1:

[2013] The server receives event information from authors and related parties and registers it in a database.

[2014] Step 2:

[2015] The server notifies the user of the registered event information.

[2016] Step 3:

[2017] When a user wishes to participate, they send a request to the server.

[2018] Step 4:

[2019] The server approves the request and registers the user.

[2020] Step 5:

[2021] The server sends a reminder to the user the day before the event.

[2022] 5. Word Adventurer

[2023] Subject: Server

[2024] Step 1:

[2025] The user selects the book they wish to translate and sends a request to the server.

[2026] Step 2:

[2027] The server launches a generative AI to translate the book text into the user's native language.

[2028] Step 3:

[2029] The server generates the translated text data and sends it to the user.

[2030] Step 4:

[2031] The user's device displays the translated text.

[2032] 6. Reading Surprise

[2033] Subject: Server

[2034] Step 1:

[2035] The server retrieves the user's reading history from the database.

[2036] Step 2:

[2037] The server uses generative AI to run algorithms that recommend new genres and authors outside of the user's interests.

[2038] Step 3:

[2039] The server creates a surprise recommendation list based on the calculation results and sends it to the user.

[2040] Step 4:

[2041] The user's device will display suggestions, encouraging a new reading experience.

[2042] 7. Emotion Engine

[2043] Subject: Server and terminal

[2044] Step 1:

[2045] The user turns on the camera and microphone and allows emotional data capture.

[2046] Step 2:

[2047] The device captures the user's facial expressions and tone of voice in real time and sends the data to a server.

[2048] Step 3:

[2049] The server uses an emotion engine to analyze the received emotion data.

[2050] Step 4:

[2051] Based on the emotional data, the server uses a generative AI model to recommend books that best fit the user's current emotional state.

[2052] Step 5:

[2053] The server creates a recommendation list and sends it to the terminal.

[2054] Step 6:

[2055] The terminal displays a recommendation list based on the received emotions to the user.

[2056] The above specific processing steps enable highly customized reading suggestions tailored to the user's emotional state, allowing users to easily find the book that best suits their mood and emotions at any given time.

[2057] Example 2

[2058] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2059] Conventional book recommendation systems are limited to recommending books based on users' reading history and interests, and lack recommendations based on new genres or emotions. Furthermore, they lack an integrated system that allows users to conveniently check inventory by location information when searching for specific books, trade books with other users, and manage events and workshops, making it difficult to meet the diverse needs of users.

[2060] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2061] In this invention, the server includes a recommendation means using a generative AI model to present recommended books based on the user's reading history and interests, a location information means for acquiring the user's location information and providing inventory information of the nearest retailer or library facility, a trading means for providing an online trading venue for the user to trade books owned by the user with other users, an event management means for managing event and workshop schedules and notifying the user, a translation means for translating books in foreign languages ​​and providing them in the user's native language, a surprise recommendation means for recommending books in new genres or by new authors that are outside of traditional reading trends, and an emotion evaluation means for recognizing the user's emotions based on an emotion engine and recommending books that match those emotions. This makes it possible to meet the diverse needs of users and provide a personalized reading experience.

[2062] A "generative AI model" is an artificial intelligence model that generates a list of recommended books based on a user's reading history and interests.

[2063] A "recommendation method" is a method for recommending books suitable for a user using a generative AI model.

[2064] The "location information means" is a means for acquiring the user's location information and providing inventory information of the nearest retail store or library facility.

[2065] A "trading means" is a means for providing an online trading space for a user to trade books with other users.

[2066] The "event management means" is a means for managing schedules of events and workshops and notifying users of the schedules.

[2067] "Translation means" refers to a means for translating a book in a foreign language into the user's native language and providing it to the user.

[2068] A "surprise recommendation method" is a method for recommending to a user books in new genres or by new authors that deviate from traditional reading habits.

[2069] The "emotion evaluation means" is a means for recognizing the user's emotions using an emotion engine and recommending books that match those emotions.

[2070] The system of the present invention combines a generative AI model, a location information acquisition system, an online trading platform, an event management system, a translation function, a surprise recommendation function, and an emotion evaluation system to meet the diverse reading needs of users. Each component of the system is described in detail below.

[2071] 1. Recommendation method

[2072] The server uses a generative AI model to generate a book list based on the user's reading history and interests. The user inputs their genres and authors of interest through the app interface, and their device sends that information to the server. The server then generates prompts and inputs them into the generative AI model.

[2073] As a concrete example, if the user is interested in "mystery novels," the following prompt sentence may be used:

[2074] Based on your users' reading history and interests, create a list of the latest mystery novels, along with related information and reviews.

[2075] The generated book list is sent to the user's terminal along with additional information and displayed on the screen.

[2076] 2. Location information means

[2077] The device acquires the user's current location and provides inventory information for the nearest retailer or library. When the user searches for a specific book with location information turned on, the device sends that information to the server. The server then consults a database to collect inventory and location information and displays it to the user.

[2078] For example, if you searched for "Harry Potter," you would use the following prompt:

[2079] The user's current location is a specific location. Please provide information about retailers and libraries that stock Harry Potter. Please also display map information.

[2080] As a result, facilities with inventory are displayed on the map and the user can access the location.

[2081] 3. Trading Instruments

[2082] We provide an online trading platform where users can trade unwanted books with other users. Users input information about the book they wish to trade, and their terminals send it to the server. The server registers the information in a database, and when other users search for the same book, it matches them.

[2083] 4. Event Management Tools

[2084] The server has the function of registering event information from authors and event organizers in a database and notifying users. When a user registers to participate in an event, the information is sent to the server and registration is completed. Before the event begins, the server sends a reminder to the user.

[2085] 5. Translation Methods

[2086] When a user selects a book written in a specific foreign language, the server uses a generative AI model to translate the book. After the user selects a book, the device sends the information to the server. The server generates a translation prompt, inputs it into the AI ​​model, and obtains the translation result.

[2087] For example, if the user selects "Le Petit Prince (French)", use the following prompt text:

[2088] Please translate this French book into your native language.

[2089] The translated text is sent to the terminal and displayed to the user.

[2090] 6. Surprise Recommendations

[2091] The server suggests books from new genres and authors that are outside the user's past reading habits, giving the user the opportunity to be exposed to new genres.

[2092] 7. Emotional Assessment Instrument

[2093] The server uses an emotion engine to recognize the user's emotions and recommends books based on those emotions. When the user expresses their emotions using the camera or microphone, the device sends the data to the server for emotion analysis.

[2094] As a concrete example, if it is determined that the user is tired, the following prompt sentence is used:

[2095] List books in genres that users find relaxing.

[2096] The server provides the list obtained from the generative AI model to the user.

[2097] As described above, the system of the present invention can provide users with a personalized and sophisticated reading experience by flexibly combining each function.

[2098] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2099] "Book Search Navigator" program processing flow

[2100] Step 1: Accepting User Input

[2101] Users input genres and authors of interest through the app's interface. The input data is temporarily stored on the user's device (input). Specifically, the user enters "mystery novels" or "a specific author's name" into the app's search bar.

[2102] Step 2: Send data

[2103] The terminal sends the user's input data to the server (output). Specifically, when the send button on the terminal is pressed, the input data is sent to the back-end server.

[2104] Step 3: Data analysis

[2105] The server receives the transmitted data and generates a prompt based on the user's interests (input). The data is analyzed using text mining and natural language processing to communicate with the generative AI model. For example, a prompt might be created that reads, "Generate a list of the latest mystery novels based on the user's reading history and interests."

[2106] Step 4: Query the AI ​​model

[2107] The server inputs the generated prompt sentence into the generative AI model and obtains a list of appropriate books from the generative AI model (output). Specifically, the generative AI model generates a book list based on the prompt sentence and returns it to the server.

[2108] Step 5: List Generation

[2109] The server compares the book list returned by the generative AI model with its internal database and adds additional information such as reviews and ratings for each book (input and output). Specifically, it retrieves review information from the database and merges it with the retrieved book list.

[2110] Step 6: Sending the results

[2111] The server then sends the final book list and additional information to the user's device (output). Specifically, it converts the list into the required data format and sends it to the device using an API.

[2112] Step 7: Display to the user

[2113] The device displays the received book list and additional information in response to the user's request (input). Specifically, the book list is displayed in the app's UI, and when the user clicks, detailed information is displayed in a pop-up.

[2114] "Book Whereabouts Guide" program processing flow

[2115] Step 1: Accepting User Input

[2116] The user enters search keywords for a specific book within the app and turns on location services. The entered data is temporarily stored on the device (input). For example, the user enters "Harry Potter" as a search keyword.

[2117] Step 2: Send data

[2118] The device sends the search keywords and location information to the server (output). Specifically, when the user presses the send button, the device sends the GPS information and search keywords to the server.

[2119] Step 3: Data analysis

[2120] The server receives the location information and search keywords and searches the database for inventory information of the nearest retail store or library facility (input and output).Specifically, it queries facilities within a certain radius from the user's current location and retrieves inventory information that matches the search keywords from the database.

[2121] Step 4: Result generation

[2122] The server generates a list of the nearest retail stores and library facilities based on the location information and search keywords, and adds inventory information to the list (input and output).The server attaches inventory status to the acquired facility information and generates a list.

[2123] Step 5: Sending the results

[2124] The server sends the generated list and inventory information to the user's device (output). Specifically, it uses an API to convert the data into a display format and sends it to the device.

[2125] Step 6: Display to the user

[2126] The device displays the received list and inventory information along with a map to the user (input). Specifically, it uses the app's map function to map the facility's location and displays an icon indicating whether the item is in stock.

[2127] "Book Search Market" program processing flow

[2128] Step 1: Accepting User Input

[2129] The user enters information about the book they wish to trade into the app, including the book's title, author, condition, price, etc. (Input). The entered data is temporarily stored on the device.

[2130] Step 2: Send data

[2131] The terminal sends the entered book information to the server (output). When the user presses the "Register" button, the terminal sends the book information to the server.

[2132] Step 3: Database registration

[2133] The server receives the book information and registers it in the database (input and output). Specifically, it adds a new book record to the database.

[2134] Step 4: Search by other users

[2135] When other users search for a particular book, the keywords are sent to the server (input). Specifically, the user enters the keywords into the search bar and executes the search.

[2136] Step 5: Database Matching

[2137] The server matches the search keywords with the desired book information in the database and presents the matching books to the user who searches (input and output). Specifically, it queries the book records that match the keywords and generates the results.

[2138] Step 6: Submit matches

[2139] The server notifies the searched user and the user who wishes to trade of the matching book information (output). Specifically, it sends a notification email or an in-app notification to the user who wishes to trade.

[2140] Step 7: Negotiate the deal

[2141] A deal is negotiated between two users, who use the in-app chat feature to discuss the price and details of the deal (input and output).

[2142] Step 8: Keep a transaction log

[2143] When a transaction is completed, the server saves the transaction log in the database (input and output) and records the transaction details in the transaction log table.

[2144] (Application example 2)

[2145] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2146] In recent years, electronic payment services and online book sales platforms have become popular, but simply purchasing a book is not enough to enrich the user's reading experience. There is a need for a system that can recommend books and support purchases in an appropriate and timely manner based on the user's individual interests and emotional state. However, existing systems lack advanced customization based on the user's emotions and reading habits, and are therefore unable to provide the optimal reading experience for the user. To solve this problem, a system is needed that can recommend books based on the user's reading history, interests, and emotional state, and then allow for electronic payment.

[2147] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a recommendation unit using a generative AI model to present recommended books based on the user's reading history and interests; a location information unit that acquires the user's location information and provides inventory information for the nearest bookstore or library; a transaction unit that provides an online marketplace for the user to trade books owned by the user with other users; an event management unit that manages event and workshop schedules and notifies the user; a translation unit that translates books in foreign languages ​​and provides them in the user's native language; a surprise recommendation unit that recommends books in new genres or by authors that deviate from traditional reading habits; an emotion recommendation unit that analyzes the user's emotional state and recommends books based on the user's emotions; and an electronic payment unit that can automatically purchase books based on the user's reading history and emotions. This enables customized book recommendations based on the user's individual interests and emotional state and a one-stop purchasing experience.

[2148] A "generative AI model" is an artificial intelligence algorithm that recommends books based on a user's reading history and interests.

[2149] "Recommendation method" refers to a function that uses a generative AI model to suggest the most suitable books to the user.

[2150] "Location information means" is a function that acquires the user's current location and provides inventory information for the nearest bookstore or library.

[2151] "Transaction means" is a function that provides an online marketplace for users to trade books they own with other users.

[2152] The "event management means" is a function for managing schedules for events and workshops and notifying users of them.

[2153] "Translation means" is a function that translates books in foreign languages ​​and provides them in the user's native language.

[2154] The "surprise recommendation means" is a function that recommends books by new genres or authors that deviate from the user's usual reading habits.

[2155] The "emotion recommendation means" is a function that analyzes the user's emotional state and recommends books based on that emotion.

[2156] "Electronic payment methods" is a feature that allows users to automatically purchase books based on their subscription history and sentiment.

[2157] The system of the present invention combines a generative AI model and an emotion engine to provide customized book suggestions and related information to enhance the user's reading experience. This system is realized through the exchange of data between a server, terminals, and users.

[2158] System Programming and Processing

[2159] 1. Recommendation methods using generative AI models

[2160] The server uses generative AI to suggest recommended books based on data obtained from the user, such as their subscription history and their favorite genres and authors. For example, if a user likes "mystery novels," the server uses that data to generate a list of the latest mystery novels and presents it to the user. When a user enters a specific author using the search function, all works related to that author are returned as search results.

[2161] 2. Location information means

[2162] The device acquires the user's location information and provides inventory information for the nearest bookstore or library. For example, if a user searches for a specific book and has location information turned on, the server uses the user's location information and search keywords to acquire inventory information for the nearest bookstore or library and displays it on the device. A pin is displayed on a map, allowing the user to access that location and obtain the book.

[2163] 3. Trading instruments that provide online marketplaces

[2164] Users register books they own that they no longer need in the app and wish to trade with other users. This information is sent to the server and registered in the database. When other users search for the book, the server retrieves matching information and notifies the user who wishes to trade. Once negotiations for a trade begin between users and the transaction is concluded, the server saves a log of the transaction.

[2165] 4. Event Management Tools

[2166] The server registers event information from famous authors and related parties in a database and notifies users. When a user registers to participate in an event, the information is sent to the server and registration is completed. For example, if a "reading group of famous authors" is being held, the information is notified to users, and users who wish to participate can r...

Claims

1. A recommendation method using a generative AI model that suggests books based on a user's reading history and interests. location information means for acquiring location information of a user and providing inventory information of the nearest bookstore or library; a trading means for providing an online marketplace for users to trade their books with other users; an event management means for managing schedules of events and workshops and notifying users; a translation means for translating books in a foreign language and providing them in the user's native language; A surprise recommendation method that recommends books from new genres and authors that deviate from traditional reading trends, A system including:

2. The system of claim 1 , wherein the generative AI model includes means for creating a recommendation list based on the user's past subscription history and interests, and transmitting the list to the user's terminal.

3. 2. The system according to claim 1, wherein said location information means includes means for acquiring the user's current location, acquiring inventory information of the nearest bookstore or library from a database, and transmitting the information to the user's terminal.

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