system

Abstract

The system according to this embodiment aims to automatically determine the physical condition of a book and suggest books that meet the user's needs. [Solution] The system according to the embodiment comprises a scanning unit, a determination unit, a notification unit, and a suggestion unit. The scanning unit scans the physical condition of the book. The determination unit analyzes the data scanned by the scanning unit and determines the physical condition of the book. The notification unit notifies the administrator of books that require repair or replacement based on the results determined by the determination unit. The suggestion unit analyzes the user's lending history and collection data and suggests books and themes tailored to individual needs.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, since the physical state of books is manually checked and managed, it requires a great deal of time and labor, and there is a problem that it is difficult to propose individualized books that meet the needs of users.

[0005] The system according to the embodiment aims to automatically determine the physical state of books and propose books according to the needs of users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a scanning unit, a determination unit, a notification unit, and a suggestion unit. The scanning unit scans the physical condition of books. The determination unit analyzes the data scanned by the scanning unit and determines the physical condition of the books. The notification unit notifies the administrator of books that require repair or replacement based on the results determined by the determination unit. The suggestion unit analyzes the user's lending history and collection data and suggests books and themes tailored to individual needs. [Effects of the Invention]

[0007] The system according to this embodiment can automatically determine the physical condition of a book and suggest books that meet the user's needs. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The librarian AI agent system according to an embodiment of the present invention is a system aimed at improving the efficiency of library operations and enhancing user satisfaction. This system utilizes a generating AI and a dedicated book scanner to automatically determine the physical condition of books and suggest books tailored to the user's needs. The librarian AI agent system scans the physical condition of books, and the generating AI analyzes the data to notify administrators of books that require repair or replacement. It also analyzes the user's borrowing history and holdings data to suggest books and themes tailored to individual needs. For example, if a user requests to read an adventure story, the system selects suitable books from past borrowing history and notifies the user of related new releases and event information. This allows users to easily find books that match their interests, improving their satisfaction. Furthermore, the librarian AI agent system significantly improves the operational efficiency of the library. By automating book management and user services, the burden on library staff is reduced, allowing them to dedicate more time to other tasks. In addition, book maintenance becomes easier, ensuring the quality of the collection is maintained, and personalized services for users are realized, improving satisfaction. Furthermore, decision-making based on data provided by the librarian AI agent strengthens the role of libraries in local communities. This system can be used in various types of libraries, including public libraries, university libraries, and specialized libraries, and is expected to contribute to the expansion of the library services market. In today's world, where digital technology is required to improve operational efficiency and provide personalized services, this invention improves the efficiency and value of libraries and strengthens their role as knowledge centers in local communities. As a result, the librarian AI agent system can achieve increased efficiency in library operations and improved user satisfaction.

[0029] The librarian AI agent system according to this embodiment comprises a scanning unit, a determination unit, a notification unit, and a suggestion unit. The scanning unit scans the physical condition of a book. The scanning unit scans the condition of a book using, for example, a dedicated book scanner. The scanning unit can scan the condition of a book if its pages are torn or its cover is damaged. For example, if a page is torn, the scanning unit scans the torn portion and saves it as digital data. For example, if the cover of a book is damaged, the scanning unit scans the damaged portion and saves it as digital data. For example, if a page is dirty, the scanning unit scans the dirty portion and saves it as digital data. The determination unit analyzes the data scanned by the scanning unit and determines the physical condition of the book. For example, the determination unit analyzes the scanned data using a generative AI and automatically determines whether the book is damaged or broken. For example, the determination unit determines whether a page of the book is torn based on the scanned data. The judgment unit determines, for example, whether the cover of a book is damaged based on scanned data. The judgment unit determines, for example, whether the pages of a book are dirty based on scanned data. The notification unit notifies the administrator of books that need repair or replacement based on the results determined by the judgment unit. For example, if the judgment unit determines that the pages of a book are torn, the notification unit notifies the administrator that the book needs to be repaired. For example, if the judgment unit determines that the cover of a book is damaged, the notification unit notifies the administrator that the book needs to be repaired. For example, if the judgment unit determines that the pages of a book are dirty, the notification unit notifies the administrator that the book needs to be repaired. The suggestion unit analyzes the user's borrowing history and holding data and proposes books and themes tailored to individual needs. For example, the suggestion unit uses generative AI to analyze the user's borrowing history and proposes books that match the user's interests. For example, the suggestion unit proposes related books based on the genres of books the user has borrowed in the past. The suggestion department, for example, suggests related books based on the authors of books the user has borrowed in the past.The suggestion function, for example, suggests related books based on the themes of books the user has borrowed in the past. This allows the librarian AI agent system, according to the embodiment, to automate book management and user services, thereby improving the operational efficiency of the library.

[0030] The scanning unit scans the physical condition of the book. For example, the scanning unit uses a dedicated book scanner to scan the book's condition. The scanning unit can scan for torn pages or damaged covers. Specifically, the scanning unit uses a scanner combining a high-resolution camera and optical sensor to accurately scan even the smallest details of the book's pages and cover. The scanner automatically turns each page of the book and scans them one by one, detecting tears, stains, and cover damage with high precision. The scanned data is saved as a digital image and used for subsequent processing. For example, if a page is torn, the scanning unit scans the torn portion and saves it as digital data. For example, if the book's cover is damaged, the scanning unit scans the damaged portion and saves it as digital data. For example, if a page is dirty, the scanning unit scans the dirty portion and saves it as digital data. The scanning unit meticulously records the book's condition during scanning, enabling subsequent judgment and notification units to process the data efficiently. The scanning unit features an automatic adjustment function that adjusts scanner settings according to the size and shape of the book to improve scanning accuracy. This allows the scanning unit to handle various types of books and acquire accurate scan data. Furthermore, the scanning unit can save the scanned data to a cloud-based database and share it with other systems and departments. This enables the scanning unit to efficiently and effectively scan the physical condition of books, improving the overall system performance.

[0031] The judgment unit analyzes the data scanned by the scanning unit to determine the physical condition of the book. For example, the judgment unit uses generative AI to analyze the scanned data and automatically determine the condition of the book. Specifically, the generative AI uses image recognition technology to analyze the scanned data and detect tears and stains on the pages of the book, damage to the cover, etc. Based on past data and trained models, the generative AI can determine the condition of the book with high accuracy. For example, the judgment unit determines whether the pages of the book are torn based on the scanned data. For example, the judgment unit determines whether the cover of the book is damaged based on the scanned data. For example, the judgment unit determines whether the pages of the book are stained based on the scanned data. When analyzing the scanned data, the judgment unit evaluates the condition of the book in detail and identifies books that require repair or replacement. The judgment unit saves the analysis results as digital data so that the notification unit and proposal unit can process them efficiently. Furthermore, the judgment unit saves the analysis results to a database on the cloud and can share the data in cooperation with other systems and departments. This allows the determination unit to efficiently and effectively determine the physical condition of the books, thereby improving the overall performance of the system.

[0032] The notification unit notifies the administrator of books requiring repair or replacement based on the results determined by the assessment unit. For example, if the assessment unit determines that a book's pages are torn, the notification unit will notify the administrator that the book needs repair. For example, if the assessment unit determines that a book's cover is damaged, the notification unit will notify the administrator that the book needs repair. For example, if the assessment unit determines that a book's pages are dirty, the notification unit will notify the administrator that the book needs repair. When notifying, the notification unit will provide a detailed explanation of the book's condition and the need for repair, enabling the administrator to respond quickly. The notification unit can send notifications via email, SMS, or a dedicated management system. The notification unit automatically generates notification content and provides appropriate information to the administrator. Furthermore, the notification unit can save the notification history as digital data for subsequent processing and analysis. This allows the notification unit to efficiently and effectively notify administrators of books requiring repair or replacement, improving the overall system performance.

[0033] The recommendation department analyzes users' borrowing history and holding data to suggest books and themes tailored to individual needs. For example, the recommendation department uses generative AI to analyze users' borrowing history and suggest books that match the user's interests. Specifically, the generative AI analyzes users' past borrowing history and holding data to understand the user's interests and preferences. Based on the genres, authors, and themes of books the user has borrowed in the past, the generative AI can suggest related books with high accuracy. For example, the recommendation department suggests related books based on the genres of books the user has borrowed in the past. For example, the recommendation department suggests related books based on the authors of books the user has borrowed in the past. For example, the recommendation department suggests related books based on the themes of books the user has borrowed in the past. When making suggestions, the recommendation department provides detailed explanations of books tailored to the user's interests and preferences to pique the user's interest. The recommendation department can make suggestions, for example, via email or a dedicated application. The recommendation department automatically generates the content of the suggestions and provides appropriate information to the user. Furthermore, the proposal department can save the history of proposals as digital data and use it for subsequent processing and analysis. This allows the proposal department to efficiently and effectively propose books and themes that meet user needs, thereby improving the overall performance of the system.

[0034] The scanning unit can scan the condition of a book using a dedicated book scanner. For example, the scanning unit can use the dedicated book scanner to scan whether the pages of the book are torn. For example, the scanning unit can use the dedicated book scanner to scan whether the cover of the book is damaged. For example, the scanning unit can use the dedicated book scanner to scan whether the pages of the book are dirty. This allows for accurate scanning of the book's condition using a dedicated book scanner. The dedicated book scanner has, for example, a high-resolution scanning function, allowing for accurate scanning of even the finest details of the book. The dedicated book scanner has, for example, a high-speed scanning function, allowing for scanning many books in a short time. The dedicated book scanner has, for example, a function to adjust the scanning range according to the size of the book, allowing for appropriate scanning of large and small books. Some or all of the above-described processes in the scanning unit may be performed using AI, for example, or without AI. For example, the scanning unit can input the data scanned by the dedicated book scanner into a generating AI, which can analyze the scanned data to determine the condition of the book.

[0035] The judgment unit can analyze scanned data and automatically determine whether a book is damaged or broken. For example, the judgment unit can use a generating AI to analyze the scanned data and determine whether the pages of the book are torn. For example, the judgment unit can use a generating AI to analyze the scanned data and determine whether the cover of the book is damaged. For example, the judgment unit can use a generating AI to analyze the scanned data and determine whether the pages of the book are dirty. This allows for the automatic determination of whether a book is damaged or broken, enabling early detection of maintenance needs. Damage and breakage include, but are not limited to, torn pages, damaged covers, and dirty pages. Some or all of the above-described processes in the judgment unit may be performed using a generating AI, for example, or without a generating AI. For example, the judgment unit can input scanned data into a generating AI, which can then analyze the data to determine the condition of the book.

[0036] The notification unit can notify the administrator of books that require repair or replacement based on the judgment results. For example, if the judgment unit determines that a book's pages are torn, the notification unit will notify the administrator that the book needs to be repaired. For example, if the judgment unit determines that a book's cover is damaged, the notification unit will notify the administrator that the book needs to be repaired. For example, if the judgment unit determines that a book's pages are dirty, the notification unit will notify the administrator that the book needs to be repaired. This enables a quick response by notifying the administrator of books that require repair or replacement. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the results determined by the judgment unit into a generating AI, which can then generate notification content and notify the administrator.

[0037] The suggestion unit can analyze a user's borrowing history and collection data to propose books and themes tailored to their individual needs. For example, the suggestion unit can use generative AI to analyze a user's borrowing history and propose books that match the user's interests. For example, the suggestion unit can propose related books based on the genres of books the user has borrowed in the past. For example, the suggestion unit can propose related books based on the authors of books the user has borrowed in the past. For example, the suggestion unit can propose related books based on the themes of books the user has borrowed in the past. This improves user satisfaction by proposing books and themes tailored to the user's needs. Individual needs include, but are not limited to, the user's interests and preferences, and past borrowing history. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or without generative AI. For example, the suggestion unit can input the user's borrowing history and collection data into generative AI, which can then analyze the data and propose books and themes.

[0038] The suggestion unit can notify users of relevant new releases and event information based on their preferences. For example, if a user requests to read an adventure story, the suggestion unit will select suitable books from the user's past borrowing history and notify them of relevant new releases and event information. For example, if a user requests to read a history book, the suggestion unit will select suitable books from the user's past borrowing history and notify them of relevant new releases and event information. For example, if a user requests to read a mystery novel, the suggestion unit will select suitable books from the user's past borrowing history and notify them of relevant new releases and event information. By notifying users of relevant new releases and event information, the system can attract their interest and improve their satisfaction. Relevant new releases and event information includes, but is not limited to, information on new book releases and information on events held at the library. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's preferences into a generative AI, which can then select and notify the user of relevant new releases and event information.

[0039] The scanning unit can determine scanning priorities based on the genre and frequency of use of books. For example, the scanning unit can prioritize scanning frequently used books to identify maintenance needs early. For example, the scanning unit can prioritize scanning books of a specific genre to understand the status of each genre. For example, the scanning unit can prioritize scanning newly published books to record their initial state. This enables efficient scanning by determining scanning priorities based on the genre and frequency of use of books. The genre and frequency of use of books include, but are not limited to, methods for classifying genres and methods for measuring frequency of use. Some or all of the above processing in the scanning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the scanning unit can input book genre and frequency of use data into a generative AI, which can then determine scanning priorities.

[0040] The scanning unit can automatically adjust the scanning method according to the size and shape of the book. For example, in the case of a large book, the scanning unit widens the scanning range to scan the entire book. For example, in the case of a small book, the scanning unit narrows the scanning range to scan in detail. For example, in the case of a book with a special shape, the scanning unit customizes the scanning method to scan appropriately. This makes it possible to perform appropriate scanning by adjusting the scanning method according to the size and shape of the book. The size and shape of the book include, but are not limited to, the method of measuring the size and the method of classifying the shape. Some or all of the above processing in the scanning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scanning unit can input the size and shape data of the book into a generative AI, and the generative AI can automatically adjust the scanning method.

[0041] The scanning unit can adjust the level of detail of the scan, taking into account the publication year and author information of the book. For example, in the case of an older book, the scanning unit performs a detailed scan to understand its state of deterioration. For example, in the case of a book by a well-known author, the scanning unit performs a detailed scan to record its state of preservation. For example, in the case of a new book, the scanning unit performs a simplified scan to record its initial state. This allows for appropriate scanning by adjusting the level of detail of the scan, taking into account the publication year and author information of the book. The publication year and author information of the book include, but are not limited to, the range of publication years and the type of author information. Some or all of the above processing in the scanning unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the scanning unit can input the book's publication year and author information data into the generating AI, which can then adjust the level of detail of the scan.

[0042] The scanning unit can adjust the scanning frequency by referring to the book's lending history. For example, the scanning unit may frequently scan books that are frequently borrowed to keep track of their status. For example, the scanning unit may periodically scan books that are not frequently borrowed to check their status. For example, the scanning unit may scan books that have no lending history and record their initial state. This allows for efficient scanning by adjusting the scanning frequency by referring to the book's lending history. The book's lending history includes, but is not limited to, the number of times a book has been borrowed and the loan period. Some or all of the above processing in the scanning unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the scanning unit may input the book's lending history data into a generating AI, which can then adjust the scanning frequency.

[0043] The judgment unit can improve the accuracy of its judgments based on the frequency of book use and lending history. For example, the judgment unit can improve the accuracy of judgments for frequently used books and identify the need for maintenance early. For example, the judgment unit can improve the accuracy of judgments for books with a high lending history and accurately understand their condition. For example, the judgment unit can improve the accuracy of judgments for books with a low usage frequency and check their storage condition. By improving the accuracy of judgments based on the frequency of book use and lending history, accurate judgments become possible. The accuracy of the judgment includes, but is not limited to, the type of data used and the analysis algorithm. Some or all of the above processing in the judgment unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the judgment unit can input book use frequency and lending history data into a generating AI, which can then improve the accuracy of its judgments.

[0044] The judgment unit can apply different judgment algorithms depending on the genre and content of the book. For example, for a novel, the judgment unit applies a judgment algorithm that emphasizes the deterioration of the text and the tearing of pages. For example, for a photo book, the judgment unit applies a judgment algorithm that emphasizes the deterioration of the images and the damage to the cover. For example, for an academic book, the judgment unit applies a judgment algorithm that emphasizes the tearing of pages and the writing on the pages. By applying different judgment algorithms depending on the genre and content of the book, appropriate judgment becomes possible. Different judgment algorithms include, but are not limited to, differences in algorithms for each genre. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the judgment unit can input book genre and content data into a generative AI, and the generative AI can apply different judgment algorithms.

[0045] The judgment unit can adjust the level of detail in its judgment by considering the publication year and author information of the book. For example, in the case of an older book, the judgment unit performs a detailed judgment to understand its state of deterioration. For example, in the case of a book by a well-known author, the judgment unit performs a detailed judgment and records its state of preservation. For example, in the case of a new book, the judgment unit performs a simplified judgment and records its initial state. By adjusting the level of detail in the judgment by considering the publication year and author information of the book, an appropriate judgment becomes possible. The level of detail in the judgment includes, but is not limited to, differences in detail based on the publication year and author information. Some or all of the above processing in the judgment unit may be performed using, for example, a generating AI, or without a generating AI. For example, the judgment unit can input the book's publication year and author information data into the generating AI, and the generating AI can adjust the level of detail in the judgment.

[0046] The judgment unit can improve the accuracy of its judgment by referring to related literature for the book. For example, the judgment unit makes a detailed judgment of the book's condition based on the information in the related literature. For example, the judgment unit accurately grasps the deterioration state of the book based on the information in the related literature. For example, the judgment unit checks the preservation state of the book based on the information in the related literature. By improving the accuracy of the judgment by referring to related literature for the book, accurate judgment becomes possible. Related literature includes, but is not limited to, cited literature and related research papers. Some or all of the above processing in the judgment unit may be performed using, for example, a generating AI, or without a generating AI. For example, the judgment unit can input the book's related literature data into a generating AI, which can then improve the accuracy of its judgment.

[0047] The notification unit can determine the priority of notifications based on the urgency of book repair or replacement. For example, the notification unit may prioritize notifications for repairs or replacements of books with high urgency. For example, the notification unit may periodically notify users of repairs or replacements of books with low urgency. For example, the notification unit may adjust the priority of repairs or replacements according to the condition of the books. This enables a quick response by determining the priority of notifications based on the urgency of book repair or replacement. The priority of notifications may include, but is not limited to, the urgency of the repair or the need for replacement. Some or all of the above processing in the notification unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the notification unit may input data on the urgency of book repair or replacement into a generating AI, and the generating AI may determine the priority of notifications.

[0048] The notification unit can customize notification content according to the book's genre and usage frequency. For example, the notification unit can make notifications for frequently used books more detailed to identify the need for maintenance early. For example, the notification unit can make notifications for books of a specific genre more detailed to understand the status of each genre. For example, the notification unit can simplify notifications for newly published books and record their initial state. This allows for efficient notifications by customizing notification content according to the book's genre and usage frequency. Customization of notification content includes, but is not limited to, differences in notification content for each genre. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can input book genre and usage frequency data into a generative AI, which can then customize the notification content.

[0049] The notification unit can adjust the level of detail of a notification, taking into account the publication year and author information of the book. For example, in the case of an older book, the notification unit provides a detailed notification to understand its deterioration status. For example, in the case of a book by a well-known author, the notification unit provides a detailed notification to record its preservation status. For example, in the case of a new book, the notification unit provides a simplified notification to record its initial state. This allows for appropriate notifications by adjusting the level of detail of the notification, taking into account the publication year and author information of the book. The level of detail of a notification includes, but is not limited to, differences in detail based on the publication year and author information. Some or all of the above processing in the notification unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the notification unit can input the book's publication year and author information data into the generating AI, which can then adjust the level of detail of the notification.

[0050] The notification unit can enrich the notification content by including information about events related to the book. For example, the notification unit may include event information related to book repair or replacement in the notification content. For example, the notification unit may include event information related to the genre of the book in the notification content. For example, the notification unit may include event information related to the frequency of book use in the notification content. By enriching the notification content by including information about events related to the book, the system can attract user interest and improve satisfaction. Information about events related to the book may include, but is not limited to, the type of event and the timing of the notification. Some or all of the processing described above in the notification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the notification unit may input information about events related to the book into a generative AI, which can then enrich the notification content.

[0051] The suggestion unit can improve the accuracy of its suggestions based on the user's past borrowing history and preferences. For example, the suggestion unit can analyze the genres of books the user has borrowed in the past and suggest new books of the same genre. For example, the suggestion unit can suggest books on related themes based on the user's preferences. For example, the suggestion unit can suggest books by authors the user has not read before, based on the user's borrowing history. This improves the accuracy of suggestions based on the user's past borrowing history and preferences, enabling more appropriate suggestions. The accuracy of suggestions includes, but is not limited to, the type of data used and the analysis algorithm. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the user's past borrowing history and preference data into a generative AI, which can then improve the accuracy of its suggestions.

[0052] The suggestion unit can apply different suggestion algorithms depending on the user's current areas of interest and reading habits. For example, the suggestion unit may prioritize suggesting books in areas the user is currently interested in. For example, the suggestion unit may analyze the user's reading habits and suggest books that might interest them. For example, the suggestion unit may include event information related to the user's areas of interest in its suggestions. This allows for appropriate suggestions by applying different suggestion algorithms depending on the user's current areas of interest and reading habits. Different suggestion algorithms include, but are not limited to, differences in algorithms for each area of ​​interest. Some or all of the processing described above in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input data on the user's current areas of interest and reading habits into a generative AI, which can then apply different suggestion algorithms.

[0053] The suggestion unit can suggest relevant books and event information, taking into account the user's geographical location. For example, the suggestion unit can suggest event information held at libraries near the user's current location. For example, the suggestion unit can suggest books related to the region based on the user's geographical location. For example, the suggestion unit can suggest books available at nearby libraries based on the user's location information. This makes it possible to make appropriate suggestions by suggesting relevant books and event information, taking into account the user's geographical location. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the processing described above in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's geographical location data into a generative AI, which can then suggest relevant books and event information.

[0054] The suggestion unit can analyze a user's social media activity and suggest relevant books and themes. For example, the suggestion unit can suggest books related to themes the user has shown interest in on social media. For example, the suggestion unit can suggest books that the user's social media followers are reading. For example, the suggestion unit can analyze the content of a user's social media posts and suggest relevant books. This allows for appropriate suggestions by analyzing the user's social media activity and suggesting relevant books and themes. Social media activity includes, but is not limited to, posts and followed accounts. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the user's social media activity data into a generative AI, which can then suggest relevant books and themes.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The scanning unit can not only scan the physical state of a book but also extract its contents as text data. For example, the scanning unit can digitize the text of a book using OCR technology and store the content in a database. For example, the scanning unit can extract keywords from the book's content and build a searchable database. For example, the scanning unit can summarize the book's content and provide it to the user. This allows users to easily search the book's content and quickly obtain the necessary information by digitizing its contents.

[0057] The judgment unit can determine not only the physical condition of the book but also the quality of its content. For example, the judgment unit can determine whether the content of the book contains up-to-date information. For example, the judgment unit can determine whether the content of the book is accurate. For example, the judgment unit can determine whether the content of the book meets the user's needs. By determining the quality of the book's content, it is possible to provide users with high-quality books.

[0058] The notification unit can notify users not only when a book needs repair or replacement, but also of important information regarding the book's content. For example, the notification unit can notify users of the latest information regarding the book's content. For example, the notification unit can notify users of important changes to the book's content. For example, the notification unit can notify users of new research findings regarding the book's content. This ensures that users are always up-to-date by notifying them of important information regarding the book's content.

[0059] The recommendation department can not only analyze users' borrowing history and holding data, but also learn users' reading habits to make more accurate recommendations. For example, the recommendation department can analyze the content of books users have read in the past and suggest books with similar content. For example, the recommendation department can suggest highly-rated books based on the ratings users have given to books they have read in the past. For example, the recommendation department can suggest books with an appropriate reading time based on the time users have spent reading books they have read in the past. In this way, by learning users' reading habits, more accurate recommendations become possible.

[0060] The recommendation department can not only analyze users' borrowing history and holding data, but also analyze users' social media activity and suggest relevant books and themes. For example, the recommendation department can suggest books related to themes that users have shown interest in on social media. For example, the recommendation department can suggest books that the user's social media followers are reading. For example, the recommendation department can analyze the content of users' social media posts and suggest relevant books. In this way, by analyzing users' social media activity and suggesting relevant books and themes, appropriate suggestions become possible.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The scanning unit scans the physical condition of the book. The scanning unit uses a dedicated book scanner to scan the condition of the book and saves any torn pages, damaged covers, or stains on the pages as digital data. Step 2: The judgment unit analyzes the data scanned by the scanning unit to determine the physical condition of the book. The judgment unit uses generated AI to analyze the scanned data and automatically determines any damage or wear to the book. Step 3: The notification unit notifies the administrator of books that require repair or replacement based on the results determined by the assessment unit. The notification unit notifies the administrator if a book has torn pages or a damaged cover, indicating that the book needs repair. Step 4: The Proposal Department analyzes the user's borrowing history and collection data to suggest books and themes tailored to individual needs. The Proposal Department uses generative AI to analyze the user's borrowing history and suggest books that match the user's interests.

[0063] (Example of form 2) The librarian AI agent system according to an embodiment of the present invention is a system aimed at improving the efficiency of library operations and enhancing user satisfaction. This system utilizes a generating AI and a dedicated book scanner to automatically determine the physical condition of books and suggest books tailored to the user's needs. The librarian AI agent system scans the physical condition of books, and the generating AI analyzes the data to notify administrators of books that require repair or replacement. It also analyzes the user's borrowing history and holdings data to suggest books and themes tailored to individual needs. For example, if a user requests to read an adventure story, the system selects suitable books from past borrowing history and notifies the user of related new releases and event information. This allows users to easily find books that match their interests, improving their satisfaction. Furthermore, the librarian AI agent system significantly improves the operational efficiency of the library. By automating book management and user services, the burden on library staff is reduced, allowing them to dedicate more time to other tasks. In addition, book maintenance becomes easier, ensuring the quality of the collection is maintained, and personalized services for users are realized, improving satisfaction. Furthermore, decision-making based on data provided by the librarian AI agent strengthens the role of libraries in local communities. This system can be used in various types of libraries, including public libraries, university libraries, and specialized libraries, and is expected to contribute to the expansion of the library services market. In today's world, where digital technology is required to improve operational efficiency and provide personalized services, this invention improves the efficiency and value of libraries and strengthens their role as knowledge centers in local communities. As a result, the librarian AI agent system can achieve increased efficiency in library operations and improved user satisfaction.

[0064] The librarian AI agent system according to this embodiment comprises a scanning unit, a determination unit, a notification unit, and a suggestion unit. The scanning unit scans the physical condition of a book. The scanning unit scans the condition of a book using, for example, a dedicated book scanner. The scanning unit can scan the condition of a book if its pages are torn or its cover is damaged. For example, if a page is torn, the scanning unit scans the torn portion and saves it as digital data. For example, if the cover of a book is damaged, the scanning unit scans the damaged portion and saves it as digital data. For example, if a page is dirty, the scanning unit scans the dirty portion and saves it as digital data. The determination unit analyzes the data scanned by the scanning unit and determines the physical condition of the book. For example, the determination unit analyzes the scanned data using a generative AI and automatically determines whether the book is damaged or broken. For example, the determination unit determines whether a page of the book is torn based on the scanned data. The judgment unit determines, for example, whether the cover of a book is damaged based on scanned data. The judgment unit determines, for example, whether the pages of a book are dirty based on scanned data. The notification unit notifies the administrator of books that need repair or replacement based on the results determined by the judgment unit. For example, if the judgment unit determines that the pages of a book are torn, the notification unit notifies the administrator that the book needs to be repaired. For example, if the judgment unit determines that the cover of a book is damaged, the notification unit notifies the administrator that the book needs to be repaired. For example, if the judgment unit determines that the pages of a book are dirty, the notification unit notifies the administrator that the book needs to be repaired. The suggestion unit analyzes the user's borrowing history and holding data and proposes books and themes tailored to individual needs. For example, the suggestion unit uses generative AI to analyze the user's borrowing history and proposes books that match the user's interests. For example, the suggestion unit proposes related books based on the genres of books the user has borrowed in the past. The suggestion department, for example, suggests related books based on the authors of books the user has borrowed in the past.The suggestion function, for example, suggests related books based on the themes of books the user has borrowed in the past. This allows the librarian AI agent system, according to the embodiment, to automate book management and user services, thereby improving the operational efficiency of the library.

[0065] The scanning unit scans the physical condition of the book. For example, the scanning unit uses a dedicated book scanner to scan the book's condition. The scanning unit can scan for torn pages or damaged covers. Specifically, the scanning unit uses a scanner combining a high-resolution camera and optical sensor to accurately scan even the smallest details of the book's pages and cover. The scanner automatically turns each page of the book and scans them one by one, detecting tears, stains, and cover damage with high precision. The scanned data is saved as a digital image and used for subsequent processing. For example, if a page is torn, the scanning unit scans the torn portion and saves it as digital data. For example, if the book's cover is damaged, the scanning unit scans the damaged portion and saves it as digital data. For example, if a page is dirty, the scanning unit scans the dirty portion and saves it as digital data. The scanning unit meticulously records the book's condition during scanning, enabling subsequent judgment and notification units to process the data efficiently. The scanning unit features an automatic adjustment function that adjusts scanner settings according to the size and shape of the book to improve scanning accuracy. This allows the scanning unit to handle various types of books and acquire accurate scan data. Furthermore, the scanning unit can save the scanned data to a cloud-based database and share it with other systems and departments. This enables the scanning unit to efficiently and effectively scan the physical condition of books, improving the overall system performance.

[0066] The judgment unit analyzes the data scanned by the scanning unit to determine the physical condition of the book. For example, the judgment unit uses generative AI to analyze the scanned data and automatically determine the condition of the book. Specifically, the generative AI uses image recognition technology to analyze the scanned data and detect tears and stains on the pages of the book, damage to the cover, etc. Based on past data and trained models, the generative AI can determine the condition of the book with high accuracy. For example, the judgment unit determines whether the pages of the book are torn based on the scanned data. For example, the judgment unit determines whether the cover of the book is damaged based on the scanned data. For example, the judgment unit determines whether the pages of the book are stained based on the scanned data. When analyzing the scanned data, the judgment unit evaluates the condition of the book in detail and identifies books that require repair or replacement. The judgment unit saves the analysis results as digital data so that the notification unit and proposal unit can process them efficiently. Furthermore, the judgment unit saves the analysis results to a database on the cloud and can share the data in cooperation with other systems and departments. This allows the determination unit to efficiently and effectively determine the physical condition of the books, thereby improving the overall performance of the system.

[0067] The notification unit notifies the administrator of books requiring repair or replacement based on the results determined by the assessment unit. For example, if the assessment unit determines that a book's pages are torn, the notification unit will notify the administrator that the book needs repair. For example, if the assessment unit determines that a book's cover is damaged, the notification unit will notify the administrator that the book needs repair. For example, if the assessment unit determines that a book's pages are dirty, the notification unit will notify the administrator that the book needs repair. When notifying, the notification unit will provide a detailed explanation of the book's condition and the need for repair, enabling the administrator to respond quickly. The notification unit can send notifications via email, SMS, or a dedicated management system. The notification unit automatically generates notification content and provides appropriate information to the administrator. Furthermore, the notification unit can save the notification history as digital data for subsequent processing and analysis. This allows the notification unit to efficiently and effectively notify administrators of books requiring repair or replacement, improving the overall system performance.

[0068] The recommendation department analyzes users' borrowing history and holding data to suggest books and themes tailored to individual needs. For example, the recommendation department uses generative AI to analyze users' borrowing history and suggest books that match the user's interests. Specifically, the generative AI analyzes users' past borrowing history and holding data to understand the user's interests and preferences. Based on the genres, authors, and themes of books the user has borrowed in the past, the generative AI can suggest related books with high accuracy. For example, the recommendation department suggests related books based on the genres of books the user has borrowed in the past. For example, the recommendation department suggests related books based on the authors of books the user has borrowed in the past. For example, the recommendation department suggests related books based on the themes of books the user has borrowed in the past. When making suggestions, the recommendation department provides detailed explanations of books tailored to the user's interests and preferences to pique the user's interest. The recommendation department can make suggestions, for example, via email or a dedicated application. The recommendation department automatically generates the content of the suggestions and provides appropriate information to the user. Furthermore, the proposal department can save the history of proposals as digital data and use it for subsequent processing and analysis. This allows the proposal department to efficiently and effectively propose books and themes that meet user needs, thereby improving the overall performance of the system.

[0069] The scanning unit can scan the condition of a book using a dedicated book scanner. For example, the scanning unit can use the dedicated book scanner to scan whether the pages of the book are torn. For example, the scanning unit can use the dedicated book scanner to scan whether the cover of the book is damaged. For example, the scanning unit can use the dedicated book scanner to scan whether the pages of the book are dirty. This allows for accurate scanning of the book's condition using a dedicated book scanner. The dedicated book scanner has, for example, a high-resolution scanning function, allowing for accurate scanning of even the finest details of the book. The dedicated book scanner has, for example, a high-speed scanning function, allowing for scanning many books in a short time. The dedicated book scanner has, for example, a function to adjust the scanning range according to the size of the book, allowing for appropriate scanning of large and small books. Some or all of the above-described processes in the scanning unit may be performed using AI, for example, or without AI. For example, the scanning unit can input the data scanned by the dedicated book scanner into a generating AI, which can analyze the scanned data to determine the condition of the book.

[0070] The judgment unit can analyze scanned data and automatically determine whether a book is damaged or broken. For example, the judgment unit can use a generating AI to analyze the scanned data and determine whether the pages of the book are torn. For example, the judgment unit can use a generating AI to analyze the scanned data and determine whether the cover of the book is damaged. For example, the judgment unit can use a generating AI to analyze the scanned data and determine whether the pages of the book are dirty. This allows for the automatic determination of whether a book is damaged or broken, enabling early detection of maintenance needs. Damage and breakage include, but are not limited to, torn pages, damaged covers, and dirty pages. Some or all of the above-described processes in the judgment unit may be performed using a generating AI, for example, or without a generating AI. For example, the judgment unit can input scanned data into a generating AI, which can then analyze the data to determine the condition of the book.

[0071] The notification unit can notify the administrator of books that require repair or replacement based on the judgment results. For example, if the judgment unit determines that a book's pages are torn, the notification unit will notify the administrator that the book needs to be repaired. For example, if the judgment unit determines that a book's cover is damaged, the notification unit will notify the administrator that the book needs to be repaired. For example, if the judgment unit determines that a book's pages are dirty, the notification unit will notify the administrator that the book needs to be repaired. This enables a quick response by notifying the administrator of books that require repair or replacement. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the results determined by the judgment unit into a generating AI, which can then generate notification content and notify the administrator.

[0072] The suggestion unit can analyze a user's borrowing history and collection data to propose books and themes tailored to their individual needs. For example, the suggestion unit can use generative AI to analyze a user's borrowing history and propose books that match the user's interests. For example, the suggestion unit can propose related books based on the genres of books the user has borrowed in the past. For example, the suggestion unit can propose related books based on the authors of books the user has borrowed in the past. For example, the suggestion unit can propose related books based on the themes of books the user has borrowed in the past. This improves user satisfaction by proposing books and themes tailored to the user's needs. Individual needs include, but are not limited to, the user's interests and preferences, and past borrowing history. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or without generative AI. For example, the suggestion unit can input the user's borrowing history and collection data into generative AI, which can then analyze the data and propose books and themes.

[0073] The suggestion unit can notify users of relevant new releases and event information based on their preferences. For example, if a user requests to read an adventure story, the suggestion unit will select suitable books from the user's past borrowing history and notify them of relevant new releases and event information. For example, if a user requests to read a history book, the suggestion unit will select suitable books from the user's past borrowing history and notify them of relevant new releases and event information. For example, if a user requests to read a mystery novel, the suggestion unit will select suitable books from the user's past borrowing history and notify them of relevant new releases and event information. By notifying users of relevant new releases and event information, the system can attract their interest and improve their satisfaction. Relevant new releases and event information includes, but is not limited to, information on new book releases and information on events held at the library. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's preferences into a generative AI, which can then select and notify the user of relevant new releases and event information.

[0074] The scanning unit can estimate the user's emotions and adjust the timing of scans based on the estimated emotions. For example, if the user is stressed, the scanning unit can reduce the frequency of scans to alleviate the user's burden. For example, if the user is relaxed, the scanning unit can increase the frequency of scans to collect more detailed data. For example, if the user is in a hurry, the scanning unit can speed up the timing of scans to efficiently collect data. In this way, the user's burden can be reduced by adjusting the timing of scans according to the user's emotions. The user's emotions are estimated using technologies such as facial recognition and voice analysis. Some or all of the above processing in the scanning unit may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the scanning unit can input the user's facial expression data into a generative AI, which can estimate the user's emotions and adjust the timing of scans.

[0075] The scanning unit can determine scanning priorities based on the genre and frequency of use of books. For example, the scanning unit can prioritize scanning frequently used books to identify maintenance needs early. For example, the scanning unit can prioritize scanning books of a specific genre to understand the status of each genre. For example, the scanning unit can prioritize scanning newly published books to record their initial state. This enables efficient scanning by determining scanning priorities based on the genre and frequency of use of books. The genre and frequency of use of books include, but are not limited to, methods for classifying genres and methods for measuring frequency of use. Some or all of the above processing in the scanning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the scanning unit can input book genre and frequency of use data into a generative AI, which can then determine scanning priorities.

[0076] The scanning unit can automatically adjust the scanning method according to the size and shape of the book. For example, in the case of a large book, the scanning unit widens the scanning range to scan the entire book. For example, in the case of a small book, the scanning unit narrows the scanning range to scan in detail. For example, in the case of a book with a special shape, the scanning unit customizes the scanning method to scan appropriately. This makes it possible to perform appropriate scanning by adjusting the scanning method according to the size and shape of the book. The size and shape of the book include, but are not limited to, the method of measuring the size and the method of classifying the shape. Some or all of the above processing in the scanning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scanning unit can input the size and shape data of the book into a generative AI, and the generative AI can automatically adjust the scanning method.

[0077] The scanning unit can estimate the user's emotions and determine the priority of books to scan based on the estimated emotions. For example, if the user is excited, the scanning unit will prioritize scanning books in genres of interest. If the user is relaxed, the scanning unit will prioritize scanning books that are frequently used. If the user is tired, the scanning unit will prioritize scanning fewer books to reduce the scanning burden. This allows for scanning tailored to the user's interests by determining the priority of books to scan according to the user's emotions. The priority of books to scan may include, but is not limited to, factors such as frequency of use and degree of damage. Some or all of the above processing in the scanning unit may be performed using, for example, an emotion engine or a generative AI, or it may be performed without using such an emotion engine or a generative AI. For example, the scanning unit can input the user's emotion data into a generative AI, which can then determine the priority of books to scan.

[0078] The scanning unit can adjust the level of detail of the scan, taking into account the publication year and author information of the book. For example, in the case of an older book, the scanning unit performs a detailed scan to understand its state of deterioration. For example, in the case of a book by a well-known author, the scanning unit performs a detailed scan to record its state of preservation. For example, in the case of a new book, the scanning unit performs a simplified scan to record its initial state. This allows for appropriate scanning by adjusting the level of detail of the scan, taking into account the publication year and author information of the book. The publication year and author information of the book include, but are not limited to, the range of publication years and the type of author information. Some or all of the above processing in the scanning unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the scanning unit can input the book's publication year and author information data into the generating AI, which can then adjust the level of detail of the scan.

[0079] The scanning unit can adjust the scanning frequency by referring to the book's lending history. For example, the scanning unit may frequently scan books that are frequently borrowed to keep track of their status. For example, the scanning unit may periodically scan books that are not frequently borrowed to check their status. For example, the scanning unit may scan books that have no lending history and record their initial state. This allows for efficient scanning by adjusting the scanning frequency by referring to the book's lending history. The book's lending history includes, but is not limited to, the number of times a book has been borrowed and the loan period. Some or all of the above processing in the scanning unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the scanning unit may input the book's lending history data into a generating AI, which can then adjust the scanning frequency.

[0080] The judgment unit can estimate the user's emotions and adjust the display method of the judgment result based on the estimated user emotions. For example, if the user is nervous, the judgment unit provides a simple and highly visible display method. For example, if the user is relaxed, the judgment unit provides a display method that includes detailed information. For example, if the user is in a hurry, the judgment unit provides a display method that gets straight to the point. By adjusting the display method of the judgment result according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. The display method of the judgment result includes, but is not limited to, display format and display timing. Some or all of the above processing in the judgment unit may be performed using, for example, an emotion engine or a generative AI, or it may be performed without using an emotion engine or a generative AI. For example, the judgment unit can input the user's emotion data into a generative AI, and the generative AI can adjust the display method of the judgment result.

[0081] The judgment unit can improve the accuracy of its judgments based on the frequency of book use and lending history. For example, the judgment unit can improve the accuracy of judgments for frequently used books and identify the need for maintenance early. For example, the judgment unit can improve the accuracy of judgments for books with a high lending history and accurately understand their condition. For example, the judgment unit can improve the accuracy of judgments for books with a low usage frequency and check their storage condition. By improving the accuracy of judgments based on the frequency of book use and lending history, accurate judgments become possible. The accuracy of the judgment includes, but is not limited to, the type of data used and the analysis algorithm. Some or all of the above processing in the judgment unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the judgment unit can input book use frequency and lending history data into a generating AI, which can then improve the accuracy of its judgments.

[0082] The judgment unit can apply different judgment algorithms depending on the genre and content of the book. For example, for a novel, the judgment unit applies a judgment algorithm that emphasizes the deterioration of the text and the tearing of pages. For example, for a photo book, the judgment unit applies a judgment algorithm that emphasizes the deterioration of the images and the damage to the cover. For example, for an academic book, the judgment unit applies a judgment algorithm that emphasizes the tearing of pages and the writing on the pages. By applying different judgment algorithms depending on the genre and content of the book, appropriate judgment becomes possible. Different judgment algorithms include, but are not limited to, differences in algorithms for each genre. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the judgment unit can input book genre and content data into a generative AI, and the generative AI can apply different judgment algorithms.

[0083] The judgment unit can estimate the user's emotions and determine the priority of judgment results based on the estimated user emotions. For example, if the user is excited, the judgment unit will prioritize displaying judgment results for books in genres of interest. For example, if the user is relaxed, the judgment unit will prioritize displaying judgment results for books that are frequently used. For example, if the user is tired, the judgment unit will simplify the display of judgment results and show only important information. In this way, by determining the priority of judgment results according to the user's emotions, it becomes possible to display judgment results that match the user's interests. The priority of judgment results includes, but is not limited to, the degree of damage and frequency of use. Some or all of the above processing in the judgment unit may be performed using, for example, an emotion engine or a generative AI, or it may be performed without using an emotion engine or a generative AI. For example, the judgment unit can input the user's emotion data into a generative AI, and the generative AI can determine the priority of judgment results.

[0084] The judgment unit can adjust the level of detail in its judgment by considering the publication year and author information of the book. For example, in the case of an older book, the judgment unit performs a detailed judgment to understand its state of deterioration. For example, in the case of a book by a well-known author, the judgment unit performs a detailed judgment and records its state of preservation. For example, in the case of a new book, the judgment unit performs a simplified judgment and records its initial state. By adjusting the level of detail in the judgment by considering the publication year and author information of the book, an appropriate judgment becomes possible. The level of detail in the judgment includes, but is not limited to, differences in detail based on the publication year and author information. Some or all of the above processing in the judgment unit may be performed using, for example, a generating AI, or without a generating AI. For example, the judgment unit can input the book's publication year and author information data into the generating AI, and the generating AI can adjust the level of detail in the judgment.

[0085] The judgment unit can improve the accuracy of its judgment by referring to related literature for the book. For example, the judgment unit makes a detailed judgment of the book's condition based on the information in the related literature. For example, the judgment unit accurately grasps the deterioration state of the book based on the information in the related literature. For example, the judgment unit checks the preservation state of the book based on the information in the related literature. By improving the accuracy of the judgment by referring to related literature for the book, accurate judgment becomes possible. Related literature includes, but is not limited to, cited literature and related research papers. Some or all of the above processing in the judgment unit may be performed using, for example, a generating AI, or without a generating AI. For example, the judgment unit can input the book's related literature data into a generating AI, which can then improve the accuracy of its judgment.

[0086] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit can reduce the frequency of notifications to alleviate the user's burden. For example, if the user is relaxed, the notification unit can increase the frequency of notifications and provide more detailed information. For example, if the user is in a hurry, the notification unit can speed up the timing of notifications and provide information efficiently. In this way, the user's burden can be reduced by adjusting the timing of notifications according to the user's emotions. The timing of notifications includes, but is not limited to, the user's usage status and emotional state. Some or all of the above processing in the notification unit may be performed using, for example, an emotion engine or a generative AI, or without using an emotion engine or a generative AI. For example, the notification unit can input the user's emotional data into a generative AI, which can then adjust the timing of notifications.

[0087] The notification unit can determine the priority of notifications based on the urgency of book repair or replacement. For example, the notification unit may prioritize notifications for repairs or replacements of books with high urgency. For example, the notification unit may periodically notify users of repairs or replacements of books with low urgency. For example, the notification unit may adjust the priority of repairs or replacements according to the condition of the books. This enables a quick response by determining the priority of notifications based on the urgency of book repair or replacement. The priority of notifications may include, but is not limited to, the urgency of the repair or the need for replacement. Some or all of the above processing in the notification unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the notification unit may input data on the urgency of book repair or replacement into a generating AI, and the generating AI may determine the priority of notifications.

[0088] The notification unit can customize notification content according to the book's genre and usage frequency. For example, the notification unit can make notifications for frequently used books more detailed to identify the need for maintenance early. For example, the notification unit can make notifications for books of a specific genre more detailed to understand the status of each genre. For example, the notification unit can simplify notifications for newly published books and record their initial state. This allows for efficient notifications by customizing notification content according to the book's genre and usage frequency. Customization of notification content includes, but is not limited to, differences in notification content for each genre. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can input book genre and usage frequency data into a generative AI, which can then customize the notification content.

[0089] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is stressed, the notification unit provides a simple and highly visible notification method. For example, if the user is relaxed, the notification unit provides a notification method that includes detailed information. For example, if the user is in a hurry, the notification unit provides a notification method that gets straight to the point. By adjusting the notification method according to the user's emotions, notifications that are easy for the user to see can be made. Notification methods include, but are not limited to, email notifications and app notifications. Some or all of the above processing in the notification unit may be performed using, for example, an emotion engine or a generative AI, or without using an emotion engine or a generative AI. For example, the notification unit can input the user's emotion data into a generative AI, and the generative AI can adjust the notification method.

[0090] The notification unit can adjust the level of detail of a notification, taking into account the publication year and author information of the book. For example, in the case of an older book, the notification unit provides a detailed notification to understand its deterioration status. For example, in the case of a book by a well-known author, the notification unit provides a detailed notification to record its preservation status. For example, in the case of a new book, the notification unit provides a simplified notification to record its initial state. This allows for appropriate notifications by adjusting the level of detail of the notification, taking into account the publication year and author information of the book. The level of detail of a notification includes, but is not limited to, differences in detail based on the publication year and author information. Some or all of the above processing in the notification unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the notification unit can input the book's publication year and author information data into the generating AI, which can then adjust the level of detail of the notification.

[0091] The notification unit can enrich the notification content by including information about events related to the book. For example, the notification unit may include event information related to book repair or replacement in the notification content. For example, the notification unit may include event information related to the genre of the book in the notification content. For example, the notification unit may include event information related to the frequency of book use in the notification content. By enriching the notification content by including information about events related to the book, the system can attract user interest and improve satisfaction. Information about events related to the book may include, but is not limited to, the type of event and the timing of the notification. Some or all of the processing described above in the notification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the notification unit may input information about events related to the book into a generative AI, which can then enrich the notification content.

[0092] The suggestion unit can estimate the user's emotions and adjust the presentation of suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can provide a simple and easily visible suggestion. If the user is relaxed, the suggestion unit can provide a suggestion that includes detailed information. If the user is in a hurry, the suggestion unit can provide a concise suggestion. By adjusting the presentation of suggestions according to the user's emotions, it becomes possible to provide suggestions that are easy for the user to understand. The presentation of suggestions includes, but is not limited to, text format and visual format. Some or all of the processing described above in the suggestion unit may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the suggestion unit can input user emotion data into a generative AI, which can then adjust the presentation of suggestions.

[0093] The suggestion unit can improve the accuracy of its suggestions based on the user's past borrowing history and preferences. For example, the suggestion unit can analyze the genres of books the user has borrowed in the past and suggest new books of the same genre. For example, the suggestion unit can suggest books on related themes based on the user's preferences. For example, the suggestion unit can suggest books by authors the user has not read before, based on the user's borrowing history. This improves the accuracy of suggestions based on the user's past borrowing history and preferences, enabling more appropriate suggestions. The accuracy of suggestions includes, but is not limited to, the type of data used and the analysis algorithm. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the user's past borrowing history and preference data into a generative AI, which can then improve the accuracy of its suggestions.

[0094] The suggestion unit can apply different suggestion algorithms depending on the user's current areas of interest and reading habits. For example, the suggestion unit may prioritize suggesting books in areas the user is currently interested in. For example, the suggestion unit may analyze the user's reading habits and suggest books that might interest them. For example, the suggestion unit may include event information related to the user's areas of interest in its suggestions. This allows for appropriate suggestions by applying different suggestion algorithms depending on the user's current areas of interest and reading habits. Different suggestion algorithms include, but are not limited to, differences in algorithms for each area of ​​interest. Some or all of the processing described above in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input data on the user's current areas of interest and reading habits into a generative AI, which can then apply different suggestion algorithms.

[0095] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is excited, the suggestion unit will prioritize suggesting books in genres of interest. If the user is relaxed, the suggestion unit will prioritize suggesting books that the user frequently reads. If the user is tired, the suggestion unit will reduce the number of suggestions and suggest only important books. This allows for suggestions tailored to the user's interests by determining the priority of suggestions according to the user's emotions. The priority of suggestions may include, but is not limited to, the user's interests and level of concern. Some or all of the processing described above in the suggestion unit may be performed using, for example, an emotion engine or a generative AI, or without using such an emotion engine or a generative AI. For example, the suggestion unit can input the user's emotion data into a generative AI, which can then determine the priority of suggestions.

[0096] The suggestion unit can suggest relevant books and event information, taking into account the user's geographical location. For example, the suggestion unit can suggest event information held at libraries near the user's current location. For example, the suggestion unit can suggest books related to the region based on the user's geographical location. For example, the suggestion unit can suggest books available at nearby libraries based on the user's location information. This makes it possible to make appropriate suggestions by suggesting relevant books and event information, taking into account the user's geographical location. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the processing described above in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's geographical location data into a generative AI, which can then suggest relevant books and event information.

[0097] The suggestion unit can analyze a user's social media activity and suggest relevant books and themes. For example, the suggestion unit can suggest books related to themes the user has shown interest in on social media. For example, the suggestion unit can suggest books that the user's social media followers are reading. For example, the suggestion unit can analyze the content of a user's social media posts and suggest relevant books. This allows for appropriate suggestions by analyzing the user's social media activity and suggesting relevant books and themes. Social media activity includes, but is not limited to, posts and followed accounts. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the user's social media activity data into a generative AI, which can then suggest relevant books and themes.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The scanning unit can not only scan the physical state of a book but also extract its contents as text data. For example, the scanning unit can digitize the text of a book using OCR technology and store the content in a database. For example, the scanning unit can extract keywords from the book's content and build a searchable database. For example, the scanning unit can summarize the book's content and provide it to the user. This allows users to easily search the book's content and quickly obtain the necessary information by digitizing its contents.

[0100] The judgment unit can determine not only the physical condition of the book but also the quality of its content. For example, the judgment unit can determine whether the content of the book contains up-to-date information. For example, the judgment unit can determine whether the content of the book is accurate. For example, the judgment unit can determine whether the content of the book meets the user's needs. By determining the quality of the book's content, it is possible to provide users with high-quality books.

[0101] The notification unit can notify users not only when a book needs repair or replacement, but also of important information regarding the book's content. For example, the notification unit can notify users of the latest information regarding the book's content. For example, the notification unit can notify users of important changes to the book's content. For example, the notification unit can notify users of new research findings regarding the book's content. This ensures that users are always up-to-date by notifying them of important information regarding the book's content.

[0102] The recommendation department can not only analyze users' borrowing history and holding data, but also learn users' reading habits to make more accurate recommendations. For example, the recommendation department can analyze the content of books users have read in the past and suggest books with similar content. For example, the recommendation department can suggest highly-rated books based on the ratings users have given to books they have read in the past. For example, the recommendation department can suggest books with an appropriate reading time based on the time users have spent reading books they have read in the past. In this way, by learning users' reading habits, more accurate recommendations become possible.

[0103] The recommendation department can not only analyze users' borrowing history and holding data, but also analyze users' social media activity and suggest relevant books and themes. For example, the recommendation department can suggest books related to themes that users have shown interest in on social media. For example, the recommendation department can suggest books that the user's social media followers are reading. For example, the recommendation department can analyze the content of users' social media posts and suggest relevant books. In this way, by analyzing users' social media activity and suggesting relevant books and themes, appropriate suggestions become possible.

[0104] The scanning unit can estimate the user's emotions and adjust the timing of scans based on those emotions. For example, if the user is stressed, the scanning unit reduces the frequency of scans to lessen the user's burden. For example, if the user is relaxed, the scanning unit increases the frequency of scans to collect more detailed data. For example, if the user is in a hurry, the scanning unit speeds up the timing of scans to collect data efficiently. In this way, the user's burden can be reduced by adjusting the timing of scans according to the user's emotions.

[0105] The judgment unit can estimate the user's emotions and adjust the display method of the judgment result based on the estimated user emotions. For example, if the user is nervous, the judgment unit provides a simple and highly visible display method. For example, if the user is relaxed, the judgment unit provides a display method that includes detailed information. For example, if the user is in a hurry, the judgment unit provides a display method that gets straight to the point. In this way, by adjusting the display method of the judgment result according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand.

[0106] The notification unit can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is stressed, the notification unit will reduce the frequency of notifications to lessen the user's burden. For example, if the user is relaxed, the notification unit will increase the frequency of notifications and provide more detailed information. For example, if the user is in a hurry, the notification unit will speed up the timing of notifications and provide information efficiently. In this way, the user's burden can be reduced by adjusting the timing of notifications according to the user's emotions.

[0107] The suggestion function can estimate the user's emotions and adjust the way the suggestion is presented based on those emotions. For example, if the user is nervous, the suggestion function provides a simple and highly visible suggestion. If the user is relaxed, the suggestion function provides a suggestion that includes detailed information. If the user is in a hurry, the suggestion function provides a suggestion that gets straight to the point. By adjusting the way the suggestion is presented according to the user's emotions, it becomes possible to create suggestions that are easy for the user to understand.

[0108] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is excited, the suggestion function will prioritize suggesting books in genres of interest. If the user is relaxed, the suggestion function will prioritize suggesting books that the user frequently reads. If the user is tired, the suggestion function will reduce the number of suggestions and suggest only important books. In this way, by prioritizing suggestions according to the user's emotions, it becomes possible to provide suggestions that match the user's interests.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The scanning unit scans the physical condition of the book. The scanning unit uses a dedicated book scanner to scan the condition of the book and saves any torn pages, damaged covers, or stains on the pages as digital data. Step 2: The judgment unit analyzes the data scanned by the scanning unit to determine the physical condition of the book. The judgment unit uses generated AI to analyze the scanned data and automatically determines any damage or wear to the book. Step 3: The notification unit notifies the administrator of books that require repair or replacement based on the results determined by the assessment unit. The notification unit notifies the administrator if a book has torn pages or a damaged cover, indicating that the book needs repair. Step 4: The Proposal Department analyzes the user's borrowing history and collection data to suggest books and themes tailored to individual needs. The Proposal Department uses generative AI to analyze the user's borrowing history and suggest books that match the user's interests.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the scanning unit, determination unit, notification unit, and suggestion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the scanning unit scans the physical state of the book using the camera 42 of the smart device 14. The determination unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the scanned data to determine the state of the book. The notification unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and notifies the administrator based on the determination result. The suggestion unit is implemented in the control unit 46A of the smart device 14, for example, and analyzes the user's borrowing history to suggest books. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the scanning unit, determination unit, notification unit, and suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the scanning unit scans the physical state of the book using the camera 42 of the smart glasses 214. The determination unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the scanned data to determine the state of the book. The notification unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and notifies the administrator based on the determination result. The suggestion unit is implemented in the control unit 46A of the smart glasses 214, for example, and analyzes the user's borrowing history to suggest books. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the scanning unit, determination unit, notification unit, and suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the scanning unit scans the physical state of the book using the camera 42 of the headset terminal 314. The determination unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the scanned data to determine the state of the book. The notification unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and notifies the administrator based on the determination result. The suggestion unit is implemented in the control unit 46A of the headset terminal 314, for example, and analyzes the user's borrowing history to suggest books. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the scanning unit, determination unit, notification unit, and suggestion unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the scanning unit scans the physical state of the book using the camera 42 of the robot 414. The determination unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the scanned data to determine the state of the book. The notification unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and notifies the administrator based on the determination result. The suggestion unit is implemented in the control unit 46A of the robot 414, for example, and analyzes the user's borrowing history to suggest a book. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A scanning unit that scans the physical condition of the book, A determination unit analyzes the data scanned by the aforementioned scanning unit and determines the physical condition of the book, A notification unit that notifies the administrator of books that require repair or replacement based on the results determined by the aforementioned determination unit, It includes a proposal department that analyzes users' borrowing history and collection data to suggest books and themes tailored to individual needs. A system characterized by the following features. (Note 2) The scanning unit is Use a dedicated book scanner to scan the condition of the book. The system described in Appendix 1, characterized by the features described herein. (Note 3) The determination unit, The scanned data is analyzed to automatically determine damage or breakage to the books. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, Based on the assessment results, the administrator will be notified of books that require repair or replacement. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We analyze users' borrowing history and collection data to suggest books and themes tailored to their individual needs. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Based on user preferences, we will notify them of relevant new releases and event information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The scanning unit is It estimates the user's emotions and adjusts the timing of scans based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The scanning unit is Prioritize scanning based on book genre and frequency of use. The system described in Appendix 1, characterized by the features described herein. (Note 9) The scanning unit is The scanning method is automatically adjusted according to the size and shape of the book. The system described in Appendix 1, characterized by the features described herein. (Note 10) The scanning unit is It estimates the user's emotions and determines the priority of books to scan based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The scanning unit is Adjust the scan detail level considering the book's publication year and author information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The scanning unit is Adjust the scanning frequency by referring to the book's lending history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The determination unit, The system estimates the user's emotions and adjusts how the judgment results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The determination unit, Improve the accuracy of the assessment based on book usage frequency and borrowing history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The determination unit, Different judgment algorithms are applied depending on the genre and content of the book. The system described in Appendix 1, characterized by the features described herein. (Note 16) The determination unit, The system estimates the user's emotions and determines the priority of the judgment results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The determination unit, The level of detail in the assessment is adjusted by considering the book's publication year and author information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The determination unit, Improve the accuracy of the assessment by referring to related literature in the book. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, When a notification is sent, the priority of the notification is determined based on the urgency of the book's repair or replacement. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, When a notification is sent, the content of the notification will be customized according to the genre of the book and how often it is used. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, When sending notifications, the level of detail in the notification will be adjusted based on the book's publication year and author information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification unit, When sending notifications, enrich the notification content by including information about events related to the book. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, Improve the accuracy of recommendations based on the user's past borrowing history and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, Apply different suggestion algorithms depending on the user's current areas of interest and reading habits. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, The system suggests relevant books and event information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, Analyze users' social media activity and suggest relevant books and topics. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A scanning unit that scans the physical condition of the book, A determination unit analyzes the data scanned by the aforementioned scanning unit and determines the physical condition of the book, A notification unit that notifies the administrator of books that require repair or replacement based on the results determined by the aforementioned determination unit, It includes a proposal department that analyzes users' borrowing history and collection data to suggest books and themes tailored to individual needs. A system characterized by the following features.

2. The scanning unit is Use a dedicated book scanner to scan the condition of the book. The system according to feature 1.

3. The determination unit, The scanned data is analyzed to automatically determine damage or breakage to the books. The system according to feature 1.

4. The aforementioned notification unit, Based on the assessment results, the administrator will be notified of books that require repair or replacement. The system according to feature 1.

5. The aforementioned proposal section is, We analyze users' borrowing history and collection data to suggest books and themes tailored to their individual needs. The system according to feature 1.

6. The aforementioned proposal section is, Based on user preferences, we will notify them of relevant new releases and event information. The system according to feature 1.

7. The scanning unit is It estimates the user's emotions and adjusts the timing of scans based on the estimated emotions. The system according to feature 1.

8. The scanning unit is Prioritize scanning based on book genre and frequency of use. The system according to feature 1.

Images