System

Abstract

An object of the system according to the embodiment is to efficiently process various tasks of a library and support multilingual support and management of digital materials.SOLUTION: A system includes an inquiry response part, a language translation part, an event planning part, a material management part, and a digital material management part. The query handling unit handles a query from a user by using the generated AI. The language translation unit translates a multilingual document. The event planning department plans an event or a workshop. The material management unit manages and organizes materials. The digital material management unit manages and archives digital materials.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has difficulty efficiently handling the diverse tasks of libraries, and there is room for improvement, particularly in multilingual support and digital resource management.

[0005] The system according to the embodiment aims to efficiently handle various library tasks and support multilingual support and the management of digital materials. [Means for solving the problem]

[0006] The system according to the embodiment includes an inquiry response unit, a language translation unit, an event planning unit, a material management unit, and a digital material management unit. The inquiry response unit uses a generative AI to respond to inquiries from users. The language translation unit translates materials in multiple languages. The event planning unit plans events and workshops. The material management unit manages and organizes materials. The digital material management unit manages and archives digital materials. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently handle various library tasks and support multilingual support and digital resource management. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A librarian support system according to an embodiment of the present invention utilizes generative AI in the work of librarians in libraries, improving user satisfaction and reducing the workload of librarians. This librarian support system effectively uses generative AI to support inquiry response, language translation and multilingual support, event and workshop planning, material management and organization, and digital material management and archiving. As a result, the librarian support system can improve user satisfaction and reduce the workload of librarians.

[0029] The librarian support system according to the embodiment includes an inquiry response unit, a language translation unit, an event planning unit, a material management unit, and a digital material management unit. The inquiry response unit responds to user inquiries. For example, if a user asks, "Please tell me the loan status of this book," the inquiry response unit searches the library database and immediately responds with the loan status of the corresponding book. The inquiry response unit can also provide personalized responses based on the user's past inquiry history. For example, if a user frequently inquires about books in a specific genre in the past, the unit can provide the latest information related to that genre. The language translation unit translates multilingual materials. For example, if a foreign user requests, "Please translate this material into English," the language translation unit quickly translates the material and provides it to the user. The language translation unit can also translate materials containing technical terms and industry-specific expressions to improve accuracy. For example, it accurately translates medical and legal terms. The event planning unit plans events and workshops. For example, a generative AI analyzes past event data and suggests content tailored to the target audience. The event planning department can also analyze feedback from past event participants to optimize the content of the next event. For example, it can re-implement a program that was highly rated by participants. The material management department manages and organizes materials. For example, when a new book arrives at the library, the material management department analyzes the book's content and appropriately classifies it. The material management department can also automatically prioritize materials based on the frequency of use and user ratings. For example, it can prioritize and display frequently used materials. The digital material management department manages and archives digital materials. For example, a generation AI can automatically generate metadata for digital materials to improve searchability. The digital material management department can also automatically back up digital materials to ensure data security. For example, it can regularly save backups of digital materials to the cloud. As a result, the librarian support system according to the embodiment can improve user satisfaction and reduce the workload of librarians.For example, faster response to inquiries will reduce waiting times for users, and language translation and multilingual support will make it easier for foreign users to use the library. In addition, the quality of service for the entire library will improve as events and workshops are better planned and materials management and organization are more efficient. Furthermore, the management and archiving of digital materials will promote the use of e-books and digital archives.

[0030] The inquiry response unit can analyze a user's past inquiry history and provide answers optimized for individual users. For example, the generation AI in the inquiry response unit retrieves the user's past inquiry history from a database and generates an optimal answer based on past answers to similar inquiries. For example, if a user has frequently inquired about books in a specific genre in the past, the latest information related to that genre is provided. The inquiry response unit also analyzes the user's past inquiry history to understand the user's interests. This allows the generation AI to predict the information the user will seek and provide it quickly. For example, it can automatically notify users of new releases by a specific author. The inquiry response unit also learns the user's preferences and patterns based on the user's past inquiry history and provides answers optimized for individual users. For example, it analyzes the trends in books the user has borrowed in the past and recommends related books. This allows the generation AI to provide more personalized answers to users.

[0031] The inquiry response unit can automatically recommend related books and materials based on the content of a user's inquiry. For example, the generation AI in the inquiry response unit analyzes the content of a user's inquiry and searches a database for related books and materials to recommend them. For example, in response to an inquiry about a specific topic, books and papers related to that topic are provided. The inquiry response unit also builds a system that automatically recommends related books and materials based on the content of a user's inquiry. For example, if a user inquires about a specific author, other works by that author are recommended. The generation AI in the inquiry response unit also analyzes the content of a user's inquiry and recommends related books and materials in real time. For example, if a user inquires about a specific genre, the latest materials related to that genre are provided. This allows related books and materials to be quickly recommended to users.

[0032] The inquiry response unit can respond to a user's inquiry using visual and interactive content based on the content of the user's inquiry. For example, the inquiry response unit uses a generative AI to analyze the content of the user's inquiry and provide an answer using visual content. For example, complex information is visually explained using diagrams and graphs. The inquiry response unit also uses interactive content to provide answers to user inquiries. For example, if a user inquires about a specific procedure, a step-by-step guide is provided. The inquiry response unit also combines visual and interactive content to provide answers to user inquiries. For example, if a user asks about the location of a specific document, a library map is displayed to guide the user. This allows the user to be provided with visual and interactive answers.

[0033] The language translation unit can improve accuracy by translating materials containing technical terminology and industry-specific expressions. For example, when the generation AI translates materials containing technical terminology and industry-specific expressions, the language translation unit refers to a technical dictionary to improve accuracy. For example, it accurately translates medical and legal terms. Furthermore, when translating materials containing industry-specific expressions, the language translation unit has the generation AI learn from past translation data to improve accuracy. For example, it accurately translates technical expressions when translating technical documents and research papers. Furthermore, when the generation AI translates materials containing technical terminology and industry-specific expressions, the language translation unit improves accuracy by receiving expert supervision. For example, an expert reviews the translation results and makes corrections as necessary. This allows for accurate translation of technical terminology and industry-specific expressions.

[0034] The language translation unit can provide a translation that takes cultural background into consideration based on the user's native language. For example, the generation AI provides a translation that takes cultural background into consideration based on the user's native language. For example, it performs a translation that reflects idiomatic expressions and customs in a particular culture. The language translation unit also considers the user's native language and cultural background to build a system in which the generation AI provides an appropriate translation. For example, it selects culturally appropriate expressions. The language translation unit also refers to a cultural database in order for the generation AI to provide a translation that takes cultural background into consideration based on the user's native language. For example, it performs a translation that reflects important events and holidays in a particular culture. This makes it possible to provide an appropriate translation that takes cultural background into consideration.

[0035] The language translation unit can translate multilingual conversations in real time and support communication between users who speak different languages. For example, the language translation unit uses a generation AI to translate multilingual conversations in real time and support communication between users who speak different languages. For example, it translates conversations between English and Japanese in real time. Furthermore, when users speak different languages, the language translation unit uses a generation AI to translate the conversation in real time and achieve smooth communication. For example, it provides multilingual support at library events. Furthermore, the language translation unit builds a system in which a generation AI translates multilingual conversations in real time and supports communication between users who speak different languages. For example, it provides multilingual support in online chats. This facilitates communication between users who speak different languages.

[0036] The language translation unit provides a multilingual chatbot, enabling a 24-hour inquiry service. For example, the generation AI provides a multilingual chatbot, enabling a 24-hour inquiry service. For example, it can respond in multiple languages, such as English, French, and Chinese. The language translation unit also uses the multilingual chatbot to build a system that responds to user inquiries 24 hours a day. For example, it can respond to inquiries about how to use the library or how to search for materials. The language translation unit also uses the generation AI to provide a multilingual chatbot, creating an environment where users can make inquiries at any time. For example, it can respond to user questions even at night or on holidays. This makes it possible to provide a 24-hour multilingual inquiry service.

[0037] The event planning department can analyze feedback from past event participants and optimize the content of the next event. For example, the event planning department's generation AI retrieves feedback from past event participants from a database and optimizes the content of the next event. For example, it may re-implement a program that was highly rated by participants. The event planning department also analyzes feedback from event participants and builds a system in which the generation AI optimizes the content of the next event. For example, it adds a new program based on participant opinions. The event planning department also analyzes feedback from past event participants and optimizes the content of the next event. For example, it adjusts the time and location of the event according to participant requests. This makes it possible to optimize the content of the next event based on past feedback.

[0038] The event planning department can send personalized event invitations based on the user's interests. For example, the generation AI analyzes the user's past event participation history and interests and sends personalized event invitations. For example, for a user who is interested in events in a specific genre, the generation AI will introduce new events in that genre. The event planning department also builds a system in which the generation AI automatically generates personalized event invitations based on the user's interests and sends them via email or notification. For example, it recommends events related to themes in which the user has shown interest. The event planning department also analyzes the user's interests in real time and sends personalized event invitations. For example, if a user searches for a specific topic, the generation AI will introduce events related to that topic. This makes it possible to provide event invitations based on the user's interests and interests.

[0039] The event planning department can plan crossover events inviting experts from different fields and promote the fusion of new knowledge. For example, the generative AI can plan crossover events inviting experts from different fields and promote the fusion of new knowledge. For example, an event can be held inviting experts in technology and art. When planning a crossover event inviting experts from different fields, the generative AI can analyze past event data and suggest the optimal combination. For example, an event can be held inviting experts in science and literature. The event planning department can also build a system in which the generative AI can plan crossover events inviting experts from different fields and promote the fusion of new knowledge. For example, an event can be held inviting experts in medicine and technology. This makes it possible to plan events that promote the fusion of knowledge from different fields.

[0040] The event planning department plans an online event using virtual reality (VR) and can allow users in remote locations to participate. For example, the generation AI in the event planning department plans an online event using virtual reality (VR) and allows users in remote locations to participate. For example, a library tour is held using VR. When planning an online event using virtual reality (VR), the generation AI suggests optimal content and platforms. For example, a reading group is held using VR. The event planning department also builds a system in which the generation AI plans an online event using virtual reality (VR) and allows users in remote locations to participate. For example, a workshop is held using VR. This makes it possible to plan online events that users in remote locations can participate in.

[0041] The material management unit can automatically set the priority of materials based on the frequency of material use and user ratings. For example, the generation AI in the material management unit retrieves the frequency of material use from a database and automatically sets the priority of materials based on user ratings. For example, frequently used materials are displayed preferentially. The material management unit also builds a system in which the generation AI automatically sets the priority of materials based on the frequency of material use and user ratings. For example, highly rated materials are recommended preferentially. The material management unit also analyzes the frequency of material use and user ratings in real time and automatically sets the priority of materials. For example, materials with high user ratings are displayed preferentially. This makes it possible to automatically set priorities based on the frequency of material use and ratings.

[0042] The material management unit can analyze the content of materials and automatically link related materials. For example, the material management unit uses a generation AI to analyze the content of materials and automatically link related materials. For example, materials related to the same theme are linked. The material management unit also builds a system in which the generation AI analyzes the content of materials and automatically links related materials. For example, works by a specific author are linked. The material management unit also uses a generation AI to analyze the content of materials in real time and automatically link related materials. For example, materials in the same genre are linked. This makes it possible to automatically link related materials based on the content of the materials.

[0043] The material management unit can optimize the physical placement of materials to allow users to easily access them. In the material management unit, for example, the generation AI optimizes the physical placement of materials to allow users to easily access them. For example, frequently used materials are placed in an easily reachable location. Furthermore, when optimizing the physical placement of materials, the generation AI analyzes user behavior patterns and proposes the optimal placement. For example, materials are placed taking into consideration the user's movement lines. Furthermore, the material management unit builds a system in which the generation AI optimizes the physical placement of materials in real time to allow users to easily access them. For example, the placement of materials is adjusted according to the user's frequency of use. This optimizes the physical placement of materials to allow users to easily access them.

[0044] The Material Management Department supports the digitization of materials, saving physical space. For example, the Material Management Department uses generative AI to support the digitization of materials, saving physical space. For example, old materials are scanned and stored in a digital archive. When the Material Management Department supports the digitization of materials, generative AI suggests the optimal digitization method. For example, materials are digitized using high-resolution scanning and OCR technology. The Material Management Department also builds a system in which generative AI supports the digitization of materials in real time, saving physical space. For example, new materials are digitized as they arrive. This supports the digitization of materials, saving physical space.

[0045] The digital resource management unit can automatically generate metadata for digital resources to improve searchability. For example, the digital resource management unit uses a generation AI to automatically generate metadata for digital resources, improving searchability. For example, it automatically extracts information such as the title, author, and publication year of the resource. When automatically generating metadata for digital resources, the generation AI analyzes the content of the resource and extracts relevant keywords. For example, it sets keywords based on the subject or theme of the resource. The digital resource management unit also builds a system in which the generation AI automatically generates metadata for digital resources in real time to improve searchability. For example, it generates metadata every time new digital resources are added. This automatically generates metadata for digital resources and improves searchability.

[0046] The digital material management unit can analyze the usage status of digital materials and prioritize the display of popular materials. For example, the generation AI in the digital material management unit retrieves the usage status of digital materials from a database and prioritizes the display of popular materials. For example, frequently downloaded materials are displayed at the top. The digital material management unit also builds a system in which the generation AI analyzes the usage status of digital materials and prioritizes the display of popular materials. For example, highly rated materials are recommended preferentially. The digital material management unit also builds a system in which the generation AI analyzes the usage status of digital materials in real time and prioritizes the display of popular materials. For example, materials that have been highly rated by users are displayed at the top. This allows popular materials to be displayed preferentially based on the usage status of digital materials.

[0047] The digital material management unit can automatically back up digital materials to ensure data safety. In the digital material management unit, for example, the generation AI automatically backs up digital materials to ensure data safety. For example, it periodically saves backups of digital materials to the cloud. When automatically backing up digital materials, the generation AI also proposes an optimal backup schedule. For example, it prioritizes backing up materials that are frequently used. The digital material management unit also builds a system in which the generation AI automatically backs up digital materials in real time to ensure data safety. For example, it backs up each time new digital material is added. This allows for automatic backups of digital materials to ensure data safety.

[0048] The digital material management unit can support format conversion of digital materials to enable viewing on different devices. For example, the digital material management unit uses a generation AI to support format conversion of digital materials to enable viewing on different devices. For example, converting materials in PDF format to ePub format. When supporting format conversion of digital materials, the generation AI suggests the optimal format. For example, it selects a format suitable for smartphones and tablets. The digital material management unit also builds a system in which the generation AI supports format conversion of digital materials in real time to enable viewing on different devices. For example, it automatically converts formats according to the user's device. This supports format conversion of digital materials to enable viewing on different devices.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The inquiry response unit can automatically recommend related books and materials based on the content of the user's inquiry. For example, the generation AI analyzes the content of the user's inquiry and searches a database for related books and materials to recommend them. For inquiries about a specific topic, books and papers related to that topic will be provided. A system will be built that automatically recommends related books and materials based on the content of the user's inquiry. If a user inquires about a specific author, other works by that author will be recommended. The generation AI analyzes the content of the user's inquiry and recommends related books and materials in real time. If a user inquires about a specific genre, the latest materials related to that genre will be provided. This allows related books and materials to be quickly recommended to users.

[0051] The inquiry response unit can respond using visual and interactive content based on the user's inquiry. For example, a generative AI can analyze the user's inquiry and provide an answer using visual content. Complex information can be explained visually using diagrams and graphs. Interactive content can be used to provide an answer to the user's inquiry. If a user inquires about a specific procedure, a step-by-step guide can be provided. A combination of visual and interactive content can be used to provide an answer to the user's inquiry. If a user asks about the location of a specific document, a library map can be displayed to guide them. This allows the user to be provided with visual and interactive answers.

[0052] The language translation unit can translate materials containing technical terminology and industry-specific expressions to improve accuracy. For example, when the generative AI translates materials containing technical terminology and industry-specific expressions, it refers to a technical dictionary to improve accuracy. Accurately translates medical and legal terms. When translating materials containing industry-specific expressions, the generative AI learns from past translation data to improve accuracy. Accurately translates technical expressions when translating technical documents and research papers. When the generative AI translates materials containing technical terminology and industry-specific expressions, it receives expert supervision to improve accuracy. Experts review the translation results and make corrections as necessary. This allows for accurate translation of technical terminology and industry-specific expressions.

[0053] The language translation unit can provide translations that take cultural background into account based on the user's native language. For example, the generation AI provides translations that take cultural background into account based on the user's native language. It provides translations that reflect idiomatic expressions and customs in a specific culture. A system is built in which the generation AI provides appropriate translations taking into account the user's native language and cultural background. It selects culturally appropriate expressions. The generation AI refers to a cultural database to provide translations that take cultural background into account based on the user's native language. It provides translations that reflect important events and holidays in a specific culture. This makes it possible to provide appropriate translations that take cultural background into account.

[0054] The event planning department can plan crossover events inviting experts from different fields to promote the fusion of new knowledge. For example, generative AI plans crossover events inviting experts from different fields to promote the fusion of new knowledge. An event is held inviting experts in technology and art. When planning a crossover event inviting experts from different fields, generative AI analyzes past event data and suggests the optimal combination. An event is held inviting experts in science and literature. Generative AI plans a crossover event inviting experts from different fields to build a system that promotes the fusion of new knowledge. An event is held inviting experts in medicine and technology. This makes it possible to plan events that promote the fusion of knowledge from different fields.

[0055] The Event Planning Department can plan online events using virtual reality (VR) and allow users in remote locations to participate. For example, the generation AI plans online events using virtual reality (VR) and allows users in remote locations to participate. Conduct a library tour using VR. When planning an online event using virtual reality (VR), the generation AI suggests the optimal content and platform. Hold a book club using VR. The generation AI plans an online event using virtual reality (VR) and builds a system that allows users in remote locations to participate. Hold a workshop using VR. This makes it possible to plan online events that users in remote locations can participate in.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The inquiry response unit responds to inquiries from users. For example, if a user asks, "Please tell me the loan status of this book," the inquiry response unit searches the library database and immediately responds with the loan status of the relevant book. The inquiry response unit can also provide answers optimized for individual users based on the user's past inquiry history. For example, if a user has frequently inquired about books in a particular genre in the past, the unit will provide the latest information related to that genre. Step 2: The language translation department translates multilingual materials. For example, if a foreign user requests "Please translate this document into English," the language translation department will quickly translate the material and provide it to the user. The language translation department can also translate materials that contain technical terms or industry-specific expressions to improve accuracy. For example, it can accurately translate medical and legal terms. Step 3: The event planning department plans events and workshops. For example, the generative AI analyzes past event data and proposes content tailored to the target audience. The event planning department can also analyze feedback from past event participants to optimize the content of the next event. For example, they can re-implement a program that was highly rated by participants. Step 4: The resource management unit manages and organizes the resources. For example, when a new book arrives in the library, the resource management unit analyzes the book's contents and classifies it appropriately. The resource management unit can also automatically prioritize resources based on the frequency of use and user ratings. For example, it can display frequently used resources first. Step 5: The Digital Resource Management Department manages and archives the digital resources. For example, the AI ​​can automatically generate metadata for the digital resources to improve searchability. The Digital Resource Management Department can also automatically back up the digital resources to ensure data security. For example, the digital resources can be regularly backed up and stored in the cloud.

[0058] (Example 2) A librarian support system according to an embodiment of the present invention utilizes generative AI in the work of librarians in libraries, improving user satisfaction and reducing the workload of librarians. This librarian support system effectively uses generative AI to support inquiry response, language translation and multilingual support, event and workshop planning, material management and organization, and digital material management and archiving. As a result, the librarian support system can improve user satisfaction and reduce the workload of librarians.

[0059] The librarian support system according to the embodiment includes an inquiry response unit, a language translation unit, an event planning unit, a material management unit, and a digital material management unit. The inquiry response unit responds to user inquiries. For example, if a user asks, "Please tell me the loan status of this book," the inquiry response unit searches the library database and immediately responds with the loan status of the corresponding book. The inquiry response unit can also provide personalized responses based on the user's past inquiry history. For example, if a user frequently inquires about books in a specific genre in the past, the unit can provide the latest information related to that genre. The language translation unit translates multilingual materials. For example, if a foreign user requests, "Please translate this material into English," the language translation unit quickly translates the material and provides it to the user. The language translation unit can also translate materials containing technical terms and industry-specific expressions to improve accuracy. For example, it accurately translates medical and legal terms. The event planning unit plans events and workshops. For example, a generative AI analyzes past event data and suggests content tailored to the target audience. The event planning department can also analyze feedback from past event participants to optimize the content of the next event. For example, it can re-implement a program that was highly rated by participants. The material management department manages and organizes materials. For example, when a new book arrives at the library, the material management department analyzes the book's content and appropriately classifies it. The material management department can also automatically prioritize materials based on the frequency of use and user ratings. For example, it can prioritize and display frequently used materials. The digital material management department manages and archives digital materials. For example, a generation AI can automatically generate metadata for digital materials to improve searchability. The digital material management department can also automatically back up digital materials to ensure data security. For example, it can regularly save backups of digital materials to the cloud. As a result, the librarian support system according to the embodiment can improve user satisfaction and reduce the workload of librarians.For example, faster response to inquiries will reduce waiting times for users, and language translation and multilingual support will make it easier for foreign users to use the library. In addition, the quality of service for the entire library will improve as events and workshops are better planned and materials management and organization are more efficient. Furthermore, the management and archiving of digital materials will promote the use of e-books and digital archives.

[0060] The inquiry response unit can analyze a user's past inquiry history and provide answers optimized for individual users. For example, the generation AI in the inquiry response unit retrieves the user's past inquiry history from a database and generates an optimal answer based on past answers to similar inquiries. For example, if a user has frequently inquired about books in a specific genre in the past, the latest information related to that genre is provided. The inquiry response unit also analyzes the user's past inquiry history to understand the user's interests. This allows the generation AI to predict the information the user will seek and provide it quickly. For example, it can automatically notify users of new releases by a specific author. The inquiry response unit also learns the user's preferences and patterns based on the user's past inquiry history and provides answers optimized for individual users. For example, it analyzes the trends in books the user has borrowed in the past and recommends related books. This allows the generation AI to provide more personalized answers to users.

[0061] The inquiry response unit can analyze the user's voice tone and facial expressions and respond according to their emotions. For example, the generation AI in the inquiry response unit analyzes the user's voice tone to estimate the user's emotional state. For example, if the user is feeling stressed, the unit provides a response in a calm tone. The inquiry response unit also captures the user's facial expression with a camera, and the generation AI analyzes the expression. For example, if the user is confused, the unit provides a more detailed explanation. The inquiry response unit also combines voice tone and facial expression analysis to comprehensively judge the user's emotional state and respond optimally. For example, if the user is happy, the unit provides positive feedback. This enables flexible responses according to the user's emotions.

[0062] The inquiry response unit can use the emotion estimation function to estimate the user's emotions and generate answers to reduce stress. For example, the inquiry response unit uses a generation AI to analyze the user's input and estimate the user's emotions using the emotion estimation function. For example, if the user is feeling anxious, the inquiry response unit provides an answer that gives a sense of security. The inquiry response unit also monitors the user's emotional state in real time and generates answers to reduce stress. For example, if the user is in a hurry, the inquiry response unit provides a concise and quick answer. The inquiry response unit also uses the emotion estimation function to generate customized answers according to the user's emotions. For example, if the user is angry, the inquiry response unit provides a calm and polite response. This makes it possible to provide an appropriate answer to reduce the user's stress.

[0063] The inquiry response unit can automatically recommend related books and materials based on the content of a user's inquiry. For example, the generation AI in the inquiry response unit analyzes the content of a user's inquiry and searches a database for related books and materials to recommend them. For example, in response to an inquiry about a specific topic, books and papers related to that topic are provided. The inquiry response unit also builds a system that automatically recommends related books and materials based on the content of a user's inquiry. For example, if a user inquires about a specific author, other works by that author are recommended. The generation AI in the inquiry response unit also analyzes the content of a user's inquiry and recommends related books and materials in real time. For example, if a user inquires about a specific genre, the latest materials related to that genre are provided. This allows related books and materials to be quickly recommended to users.

[0064] The inquiry response unit can respond to a user's inquiry using visual and interactive content based on the content of the user's inquiry. For example, the inquiry response unit uses a generative AI to analyze the content of the user's inquiry and provide an answer using visual content. For example, complex information is visually explained using diagrams and graphs. The inquiry response unit also uses interactive content to provide answers to user inquiries. For example, if a user inquires about a specific procedure, a step-by-step guide is provided. The inquiry response unit also combines visual and interactive content to provide answers to user inquiries. For example, if a user asks about the location of a specific document, a library map is displayed to guide the user. This allows the user to be provided with visual and interactive answers.

[0065] The inquiry response unit can use the emotion estimation function to provide a customized interface according to the user's emotion. The inquiry response unit, for example, uses the emotion estimation function to provide a customized interface according to the user's emotional state. For example, if the user is feeling stressed, a simple and intuitive interface is displayed. The inquiry response unit also monitors the user's emotional state in real time and dynamically adjusts the interface. For example, if the user is relaxed, detailed information is displayed. The inquiry response unit also uses the emotion estimation function to build a system that provides a customized interface according to the user's emotion. For example, if the user is excited, interactive elements are increased. This makes it possible to provide an interface according to the user's emotion.

[0066] The language translation unit can improve accuracy by translating materials containing technical terminology and industry-specific expressions. For example, when the generation AI translates materials containing technical terminology and industry-specific expressions, the language translation unit refers to a technical dictionary to improve accuracy. For example, it accurately translates medical and legal terms. Furthermore, when translating materials containing industry-specific expressions, the language translation unit has the generation AI learn from past translation data to improve accuracy. For example, it accurately translates technical expressions when translating technical documents and research papers. Furthermore, when the generation AI translates materials containing technical terminology and industry-specific expressions, the language translation unit improves accuracy by receiving expert supervision. For example, an expert reviews the translation results and makes corrections as necessary. This allows for accurate translation of technical terminology and industry-specific expressions.

[0067] The language translation unit can provide a translation that takes cultural background into consideration based on the user's native language. For example, the generation AI provides a translation that takes cultural background into consideration based on the user's native language. For example, it performs a translation that reflects idiomatic expressions and customs in a particular culture. The language translation unit also considers the user's native language and cultural background to build a system in which the generation AI provides an appropriate translation. For example, it selects culturally appropriate expressions. The language translation unit also refers to a cultural database in order for the generation AI to provide a translation that takes cultural background into consideration based on the user's native language. For example, it performs a translation that reflects important events and holidays in a particular culture. This makes it possible to provide an appropriate translation that takes cultural background into consideration.

[0068] The language translation unit can use the emotion estimation function to evaluate the emotional impact that the translated material has on the user and select the optimal expression. For example, the language translation unit uses the emotion estimation function to evaluate the emotional impact that the translated material has on the user. For example, it selects expressions that elicit positive emotions. The language translation unit also builds a system that evaluates the emotional impact of the translated material and selects the optimal expression. For example, it selects expressions that avoid negative emotions. The language translation unit also uses the emotion estimation function to evaluate the emotional impact that the translated material has on the user in real time and selects the optimal expression. For example, it adjusts the translation result according to the user's emotional state. This makes it possible to provide a translation that takes into account the emotional impact on the user.

[0069] The language translation unit can translate multilingual conversations in real time and support communication between users who speak different languages. For example, the language translation unit uses a generation AI to translate multilingual conversations in real time and support communication between users who speak different languages. For example, it translates conversations between English and Japanese in real time. Furthermore, when users speak different languages, the language translation unit uses a generation AI to translate the conversation in real time and achieve smooth communication. For example, it provides multilingual support at library events. Furthermore, the language translation unit builds a system in which a generation AI translates multilingual conversations in real time and supports communication between users who speak different languages. For example, it provides multilingual support in online chats. This facilitates communication between users who speak different languages.

[0070] The language translation unit provides a multilingual chatbot, enabling a 24-hour inquiry service. For example, the generation AI provides a multilingual chatbot, enabling a 24-hour inquiry service. For example, it can respond in multiple languages, such as English, French, and Chinese. The language translation unit also uses the multilingual chatbot to build a system that responds to user inquiries 24 hours a day. For example, it can respond to inquiries about how to use the library or how to search for materials. The language translation unit also uses the generation AI to provide a multilingual chatbot, creating an environment where users can make inquiries at any time. For example, it can respond to user questions even at night or on holidays. This makes it possible to provide a 24-hour multilingual inquiry service.

[0071] The language translation unit uses the emotion estimation function to select a translation style that corresponds to the user's emotion, thereby enabling more natural communication. The language translation unit, for example, uses the emotion estimation function to select a translation style that corresponds to the user's emotion. For example, if the user is relaxed, a casual translation style is selected. The language translation unit also monitors the user's emotional state in real time and builds a system that selects the optimal translation style. For example, if the user is nervous, a polite translation style is selected. The language translation unit also uses the emotion estimation function to select a translation style that corresponds to the user's emotion, thereby enabling more natural communication. For example, if the user is excited, an energetic translation style is selected. This makes it possible to provide a natural translation that corresponds to the user's emotion.

[0072] The event planning department can analyze feedback from past event participants and optimize the content of the next event. For example, the event planning department's generation AI retrieves feedback from past event participants from a database and optimizes the content of the next event. For example, it may re-implement a program that was highly rated by participants. The event planning department also analyzes feedback from event participants and builds a system in which the generation AI optimizes the content of the next event. For example, it adds a new program based on participant opinions. The event planning department also analyzes feedback from past event participants and optimizes the content of the next event. For example, it adjusts the time and location of the event according to participant requests. This makes it possible to optimize the content of the next event based on past feedback.

[0073] The event planning department can send personalized event invitations based on the user's interests. For example, the generation AI analyzes the user's past event participation history and interests and sends personalized event invitations. For example, for a user who is interested in events in a specific genre, the generation AI will introduce new events in that genre. The event planning department also builds a system in which the generation AI automatically generates personalized event invitations based on the user's interests and sends them via email or notification. For example, it recommends events related to themes in which the user has shown interest. The event planning department also analyzes the user's interests in real time and sends personalized event invitations. For example, if a user searches for a specific topic, the generation AI will introduce events related to that topic. This makes it possible to provide event invitations based on the user's interests and interests.

[0074] The event planning department can use the emotion estimation function to monitor the emotions of event participants in real time and adjust the progress of the event. For example, the event planning department uses the emotion estimation function to monitor the emotions of event participants in real time and adjust the progress of the event. For example, if participants are bored, an interactive element is added. The event planning department also analyzes the emotional state of event participants in real time and builds a system in which the generation AI adjusts the progress of the event. For example, if participants are excited, a break is provided. The event planning department also uses the emotion estimation function to monitor the emotions of event participants and optimize the progress of the event. For example, if participants are feeling stressed, content that helps them relax is provided. This makes it possible to adjust the progress of the event according to the emotions of the event participants.

[0075] The event planning department can plan crossover events inviting experts from different fields and promote the fusion of new knowledge. For example, the generative AI can plan crossover events inviting experts from different fields and promote the fusion of new knowledge. For example, an event can be held inviting experts in technology and art. When planning a crossover event inviting experts from different fields, the generative AI can analyze past event data and suggest the optimal combination. For example, an event can be held inviting experts in science and literature. The event planning department can also build a system in which the generative AI can plan crossover events inviting experts from different fields and promote the fusion of new knowledge. For example, an event can be held inviting experts in medicine and technology. This makes it possible to plan events that promote the fusion of knowledge from different fields.

[0076] The event planning department plans an online event using virtual reality (VR) and can allow users in remote locations to participate. For example, the generation AI in the event planning department plans an online event using virtual reality (VR) and allows users in remote locations to participate. For example, a library tour is held using VR. When planning an online event using virtual reality (VR), the generation AI suggests optimal content and platforms. For example, a reading group is held using VR. The event planning department also builds a system in which the generation AI plans an online event using virtual reality (VR) and allows users in remote locations to participate. For example, a workshop is held using VR. This makes it possible to plan online events that users in remote locations can participate in.

[0077] The event planning department can use the emotion estimation function to provide interactive content according to the emotions of event participants, thereby improving participant satisfaction. The event planning department, for example, uses the emotion estimation function to provide interactive content according to the emotions of event participants. For example, if a participant is excited, a quiz or game is added. The event planning department also analyzes the emotional state of event participants in real time, and builds a system in which a generation AI provides interactive content. For example, if a participant is bored, discussion is promoted. The event planning department also uses the emotion estimation function to provide interactive content according to the emotions of event participants, thereby improving participant satisfaction. For example, if a participant is relaxed, relaxing music is provided. This allows interactive content to be provided according to the emotions of event participants, thereby improving participant satisfaction.

[0078] The material management unit can automatically set the priority of materials based on the frequency of material use and user ratings. For example, the generation AI in the material management unit retrieves the frequency of material use from a database and automatically sets the priority of materials based on user ratings. For example, frequently used materials are displayed preferentially. The material management unit also builds a system in which the generation AI automatically sets the priority of materials based on the frequency of material use and user ratings. For example, highly rated materials are recommended preferentially. The material management unit also analyzes the frequency of material use and user ratings in real time and automatically sets the priority of materials. For example, materials with high user ratings are displayed preferentially. This makes it possible to automatically set priorities based on the frequency of material use and ratings.

[0079] The material management unit can analyze the content of materials and automatically link related materials. For example, the material management unit uses a generation AI to analyze the content of materials and automatically link related materials. For example, materials related to the same theme are linked. The material management unit also builds a system in which the generation AI analyzes the content of materials and automatically links related materials. For example, works by a specific author are linked. The material management unit also uses a generation AI to analyze the content of materials in real time and automatically link related materials. For example, materials in the same genre are linked. This makes it possible to automatically link related materials based on the content of the materials.

[0080] The material management unit can use the emotion estimation function to analyze the emotions of the user when viewing materials and recommend the most appropriate materials. The material management unit, for example, uses the emotion estimation function to analyze the emotions of the user when viewing materials and recommend the most appropriate materials. For example, if the user is excited, it recommends highly entertaining materials. The material management unit also monitors the user's emotional state in real time, and builds a system in which the generation AI recommends the most appropriate materials. For example, if the user is relaxed, it recommends relaxing materials. The material management unit also uses the emotion estimation function to analyze the emotions of the user when viewing materials and recommend the most appropriate materials. For example, if the user is feeling stressed, it recommends materials that will help relieve stress. This makes it possible to recommend the most appropriate materials based on the user's emotions.

[0081] The material management unit can optimize the physical placement of materials to allow users to easily access them. In the material management unit, for example, the generation AI optimizes the physical placement of materials to allow users to easily access them. For example, frequently used materials are placed in an easily reachable location. Furthermore, when optimizing the physical placement of materials, the generation AI analyzes user behavior patterns and proposes the optimal placement. For example, materials are placed taking into consideration the user's movement lines. Furthermore, the material management unit builds a system in which the generation AI optimizes the physical placement of materials in real time to allow users to easily access them. For example, the placement of materials is adjusted according to the user's frequency of use. This optimizes the physical placement of materials to allow users to easily access them.

[0082] The Material Management Department supports the digitization of materials, saving physical space. For example, the Material Management Department uses generative AI to support the digitization of materials, saving physical space. For example, old materials are scanned and stored in a digital archive. When the Material Management Department supports the digitization of materials, generative AI suggests the optimal digitization method. For example, materials are digitized using high-resolution scanning and OCR technology. The Material Management Department also builds a system in which generative AI supports the digitization of materials in real time, saving physical space. For example, new materials are digitized as they arrive. This supports the digitization of materials, saving physical space.

[0083] The material management unit can use the emotion estimation function to customize the display method of materials according to the user's emotions. For example, the material management unit uses the emotion estimation function to customize the display method of materials according to the user's emotions. For example, if the user is relaxed, a simple display method is selected. The material management unit also monitors the user's emotional state in real time, and builds a system in which the generation AI customizes the display method of materials. For example, if the user is excited, an interactive display method is selected. The material management unit also uses the emotion estimation function to customize the display method of materials according to the user's emotions. For example, if the user is feeling stressed, a display method with calm colors is selected. This makes it possible to customize the display method of materials according to the user's emotions.

[0084] The digital resource management unit can automatically generate metadata for digital resources to improve searchability. For example, the digital resource management unit uses a generation AI to automatically generate metadata for digital resources, improving searchability. For example, it automatically extracts information such as the title, author, and publication year of the resource. When automatically generating metadata for digital resources, the generation AI analyzes the content of the resource and extracts relevant keywords. For example, it sets keywords based on the subject or theme of the resource. The digital resource management unit also builds a system in which the generation AI automatically generates metadata for digital resources in real time to improve searchability. For example, it generates metadata every time new digital resources are added. This automatically generates metadata for digital resources and improves searchability.

[0085] The digital material management unit can analyze the usage status of digital materials and prioritize the display of popular materials. For example, the generation AI in the digital material management unit retrieves the usage status of digital materials from a database and prioritizes the display of popular materials. For example, frequently downloaded materials are displayed at the top. The digital material management unit also builds a system in which the generation AI analyzes the usage status of digital materials and prioritizes the display of popular materials. For example, highly rated materials are recommended preferentially. The digital material management unit also builds a system in which the generation AI analyzes the usage status of digital materials in real time and prioritizes the display of popular materials. For example, materials that have been highly rated by users are displayed at the top. This allows popular materials to be displayed preferentially based on the usage status of digital materials.

[0086] The digital material management unit can use the emotion estimation function to monitor the user's emotions when viewing digital materials and recommend the most appropriate materials. For example, the digital material management unit can use the emotion estimation function to monitor the user's emotions when viewing digital materials and recommend the most appropriate materials. For example, if the user is excited, it can recommend highly entertaining materials. The digital material management unit also monitors the user's emotional state in real time, and builds a system in which the generative AI recommends the most appropriate materials. For example, if the user is relaxed, it can recommend relaxing materials. The digital material management unit also uses the emotion estimation function to monitor the user's emotions when viewing digital materials and recommend the most appropriate materials. For example, if the user is feeling stressed, it can recommend materials that will help relieve stress. This makes it possible to monitor the user's emotions when viewing digital materials and recommend the most appropriate materials.

[0087] The digital material management unit can automatically back up digital materials to ensure data safety. In the digital material management unit, for example, the generation AI automatically backs up digital materials to ensure data safety. For example, it periodically saves backups of digital materials to the cloud. When automatically backing up digital materials, the generation AI also proposes an optimal backup schedule. For example, it prioritizes backing up materials that are frequently used. The digital material management unit also builds a system in which the generation AI automatically backs up digital materials in real time to ensure data safety. For example, it backs up each time new digital material is added. This allows for automatic backups of digital materials to ensure data safety.

[0088] The digital material management unit can support format conversion of digital materials to enable viewing on different devices. For example, the digital material management unit uses a generation AI to support format conversion of digital materials to enable viewing on different devices. For example, converting materials in PDF format to ePub format. When supporting format conversion of digital materials, the generation AI suggests the optimal format. For example, it selects a format suitable for smartphones and tablets. The digital material management unit also builds a system in which the generation AI supports format conversion of digital materials in real time to enable viewing on different devices. For example, it automatically converts formats according to the user's device. This supports format conversion of digital materials to enable viewing on different devices.

[0089] The digital material management unit can use the emotion estimation function to customize the display method of digital materials according to the user's emotions. For example, the digital material management unit uses the emotion estimation function to customize the display method of digital materials according to the user's emotions. For example, if the user is relaxed, a simple display method is selected. The digital material management unit also monitors the user's emotional state in real time, and builds a system in which the generation AI customizes the display method of digital materials. For example, if the user is excited, an interactive display method is selected. The digital material management unit also uses the emotion estimation function to customize the display method of digital materials according to the user's emotions. For example, if the user is feeling stressed, a display method with calm colors is selected. This makes it possible to customize the display method of digital materials according to the user's emotions.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The inquiry response unit can analyze the user's voice tone and facial expressions and respond according to their emotions. For example, the generation AI analyzes the user's voice tone to estimate their emotional state. If the user is feeling stressed, it will provide a response in a calm tone. The generation AI can also capture the user's facial expression with a camera and analyze it. If the user seems confused, it will provide a more detailed explanation. By combining the voice tone and facial expression analysis, it can comprehensively judge the user's emotional state and respond optimally. If the user is happy, it will provide positive feedback. This makes it possible to respond flexibly according to the user's emotions.

[0092] The inquiry response unit can automatically recommend related books and materials based on the content of the user's inquiry. For example, the generation AI analyzes the content of the user's inquiry and searches a database for related books and materials to recommend them. For inquiries about a specific topic, books and papers related to that topic will be provided. A system will be built that automatically recommends related books and materials based on the content of the user's inquiry. If a user inquires about a specific author, other works by that author will be recommended. The generation AI analyzes the content of the user's inquiry and recommends related books and materials in real time. If a user inquires about a specific genre, the latest materials related to that genre will be provided. This allows related books and materials to be quickly recommended to users.

[0093] The inquiry response unit can respond using visual and interactive content based on the user's inquiry. For example, a generative AI can analyze the user's inquiry and provide an answer using visual content. Complex information can be explained visually using diagrams and graphs. Interactive content can be used to provide an answer to the user's inquiry. If a user inquires about a specific procedure, a step-by-step guide can be provided. A combination of visual and interactive content can be used to provide an answer to the user's inquiry. If a user asks about the location of a specific document, a library map can be displayed to guide them. This allows the user to be provided with visual and interactive answers.

[0094] The inquiry response unit can use the emotion estimation function to estimate the user's emotions and generate answers to reduce stress. For example, the generation AI analyzes the user's input and uses the emotion estimation function to estimate the user's emotions. If the user is feeling anxious, it provides an answer that gives a sense of security. It monitors the user's emotional state in real time and generates answers to reduce stress. If the user is in a hurry, it provides a concise and quick answer. It uses the emotion estimation function to generate a customized answer according to the user's emotions. If the user is angry, it responds calmly and politely. This makes it possible to provide an appropriate answer to reduce the user's stress.

[0095] The language translation unit can translate materials containing technical terminology and industry-specific expressions to improve accuracy. For example, when the generative AI translates materials containing technical terminology and industry-specific expressions, it refers to a technical dictionary to improve accuracy. Accurately translates medical and legal terms. When translating materials containing industry-specific expressions, the generative AI learns from past translation data to improve accuracy. Accurately translates technical expressions when translating technical documents and research papers. When the generative AI translates materials containing technical terminology and industry-specific expressions, it receives expert supervision to improve accuracy. Experts review the translation results and make corrections as necessary. This allows for accurate translation of technical terminology and industry-specific expressions.

[0096] The language translation unit can provide translations that take cultural background into account based on the user's native language. For example, the generation AI provides translations that take cultural background into account based on the user's native language. It provides translations that reflect idiomatic expressions and customs in a specific culture. A system is built in which the generation AI provides appropriate translations taking into account the user's native language and cultural background. It selects culturally appropriate expressions. The generation AI refers to a cultural database to provide translations that take cultural background into account based on the user's native language. It provides translations that reflect important events and holidays in a specific culture. This makes it possible to provide appropriate translations that take cultural background into account.

[0097] The language translation unit can use the emotion estimation function to evaluate the emotional impact that translated material has on the user and select the most appropriate expression. For example, the emotion estimation function is used to evaluate the emotional impact that translated material has on the user. Expressions that elicit positive emotions are selected. A system is constructed to evaluate the emotional impact of translated material and select the most appropriate expression. Expressions are selected to avoid negative emotions. The emotion estimation function is used to evaluate the emotional impact that translated material has on the user in real time and select the most appropriate expression. The translation results are adjusted according to the user's emotional state. This makes it possible to provide a translation that takes into account the emotional impact on the user.

[0098] The event planning department can plan crossover events inviting experts from different fields to promote the fusion of new knowledge. For example, generative AI plans crossover events inviting experts from different fields to promote the fusion of new knowledge. An event is held inviting experts in technology and art. When planning a crossover event inviting experts from different fields, generative AI analyzes past event data and suggests the optimal combination. An event is held inviting experts in science and literature. Generative AI plans a crossover event inviting experts from different fields to build a system that promotes the fusion of new knowledge. An event is held inviting experts in medicine and technology. This makes it possible to plan events that promote the fusion of knowledge from different fields.

[0099] The Event Planning Department can plan online events using virtual reality (VR) and allow users in remote locations to participate. For example, the generation AI plans online events using virtual reality (VR) and allows users in remote locations to participate. Conduct a library tour using VR. When planning an online event using virtual reality (VR), the generation AI suggests the optimal content and platform. Hold a book club using VR. The generation AI plans an online event using virtual reality (VR) and builds a system that allows users in remote locations to participate. Hold a workshop using VR. This makes it possible to plan online events that users in remote locations can participate in.

[0100] The event planning department can use the emotion estimation function to provide interactive content that corresponds to the emotions of event participants, thereby improving participant satisfaction. For example, the emotion estimation function is used to provide interactive content that corresponds to the emotions of event participants. If participants are excited, a quiz or game is added. A system is built in which the emotional state of event participants is analyzed in real time and a generative AI provides interactive content. If participants are bored, discussion is promoted. The emotion estimation function is used to provide interactive content that corresponds to the emotions of event participants, thereby improving participant satisfaction. If participants are relaxed, relaxing music is provided. In this way, interactive content that corresponds to the emotions of event participants is provided, thereby improving participant satisfaction.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The inquiry response unit responds to inquiries from users. For example, if a user asks, "Please tell me the loan status of this book," the inquiry response unit searches the library database and immediately responds with the loan status of the relevant book. The inquiry response unit can also provide answers optimized for individual users based on the user's past inquiry history. For example, if a user has frequently inquired about books in a particular genre in the past, the unit will provide the latest information related to that genre. Step 2: The language translation department translates multilingual materials. For example, if a foreign user requests "Please translate this document into English," the language translation department will quickly translate the material and provide it to the user. The language translation department can also translate materials that contain technical terms or industry-specific expressions to improve accuracy. For example, it can accurately translate medical and legal terms. Step 3: The event planning department plans events and workshops. For example, the generative AI analyzes past event data and proposes content tailored to the target audience. The event planning department can also analyze feedback from past event participants to optimize the content of the next event. For example, they can re-implement a program that was highly rated by participants. Step 4: The resource management unit manages and organizes the resources. For example, when a new book arrives in the library, the resource management unit analyzes the book's contents and classifies it appropriately. The resource management unit can also automatically prioritize resources based on the frequency of use and user ratings. For example, it can display frequently used resources first. Step 5: The Digital Resource Management Department manages and archives the digital resources. For example, the AI ​​can automatically generate metadata for the digital resources to improve searchability. The Digital Resource Management Department can also automatically back up the digital resources to ensure data security. For example, the digital resources can be regularly backed up and stored in the cloud.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

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

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

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

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

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

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

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Using generative AI, an inquiry response unit that responds to inquiries from users; A language translation department that translates multilingual materials; The Event Planning Department plans events and workshops, a materials management department that manages and organizes materials; a digital material management unit that manages and archives digital materials; A system characterized by:

2. The inquiry response unit: Analyze the user's past inquiry history and provide answers optimized for each individual user 2. The system of claim 1.

3. The inquiry response unit: Analyzing the user's tone of voice and facial expressions and responding accordingly 2. The system of claim 1.

4. The inquiry response unit: Estimating the user's feelings and generating a response to reduce stress 2. The system of claim 1.

5. The inquiry response unit: Based on the user's inquiry, related books and materials are automatically recommended.

2. The system of claim 1.

6. The inquiry response unit: Responding to the user's inquiry using visual and interactive content 2. The system of claim 1.

7. The inquiry response unit: Providing a customized interface according to the user's emotions 2. The system of claim 1.

8. The language translation unit Translate the above materials, including technical terms and industry-specific expressions, to improve accuracy 2. The system of claim 1.

Images