System

Abstract

An object of the system according to the embodiment is to allow a distant or busy user to enjoy an exhibition of an art gallery with a guide.SOLUTION: A system according to an embodiment comprises a digital archiving component, a guide registration component, a matching component, and a feedback component. The digital archiving unit digitally archives the inside of the museum or the exhibition. A guide registration part registers an online tour guide having a favorite genre. The matching unit matches the user with the art gallery and the guide. The feedback unit analyzes the topic in the guide and the user's interest and feeds back the analysis result to the art gallery.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult for users who are far away or busy to enjoy museum exhibits with a guide.

[0005] The system according to the embodiment aims to enable users who are far away or busy to enjoy museum exhibits with a guide. [Means for solving the problem]

[0006] The system according to the embodiment comprises a digital archiving unit, a guide registration unit, a matching unit, and a feedback unit. The digital archiving unit digitally archives the museum's interior or exhibits. The guide registration unit registers online tour guides with specialized genres. The matching unit matches users with museums and guides. The feedback unit analyzes topics discussed during the guide and the user's interests and provides feedback to the museum. [Effects of the Invention]

[0007] The system according to the embodiment allows users who are far away or busy to enjoy guided tours of museum exhibits. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The virtual museum system according to an embodiment of the present invention is a system that digitally archives the interior and exhibits of a museum and provides them as a virtual museum. This allows users who are far away or busy to enjoy the museum. The virtual museum system allows users who are far away or busy to enjoy the museum, and also contributes to increasing the museum's revenue and improving its services.

[0029] A virtual art museum system according to an embodiment includes a digital archiving unit, a guide registration unit, a matching unit, and a feedback unit. The digital archiving unit digitally archives the interior of an art museum or its exhibits. For example, the digital archiving unit digitally archives the art museum's exhibits using high-resolution images and videos. The digital archiving unit can also provide a 360-degree panoramic view of the exhibition room. The digital archiving unit can also provide detailed images of the exhibits. The guide registration unit registers online tour guides with specialized genres. For example, the guide registration unit can register a guide who is knowledgeable about contemporary art. The guide registration unit can also register a guide who is knowledgeable about classical paintings. The guide registration unit can also register a guide who is knowledgeable about sculptures. The matching unit matches users with art museums and guides. For example, the matching unit can analyze a user's interests and past browsing history to suggest the most suitable guide. The matching unit can also match guides based on the content of the art museum's exhibits. The matching unit can also match users based on the guide's expertise. The feedback unit analyzes the topics discussed during the guide and the user's interests and provides feedback to the museum. For example, if the user shows interest in a particular exhibit, the feedback unit provides detailed information about that exhibit. The feedback unit can also analyze the user's interests and use the information to improve the museum's exhibit content and services. The feedback unit can also evaluate the guide's performance and provide feedback to the museum. This allows the virtual museum system according to the embodiment to allow even users who are far away or busy to enjoy the museum, contributing to increased revenue and improved services for the museum. For example, users can visit museums around the world from the comfort of their own homes and gain a deeper understanding through guided tours. Furthermore, museums can understand users' interests and optimize their exhibit content to convey their appeal to a wider audience.

[0030] The Digital Archiving Department can automatically generate 3D models of exhibits and provide interactive views that users can freely rotate and zoom in and out. The Digital Archiving Department can develop a system that automatically generates 3D models of exhibits using generative AI. For example, it can take photos of an exhibit from multiple angles and generate a 3D model based on those photos. The Digital Archiving Department can also provide interactive views that users can freely rotate and zoom in and out. For example, users can rotate an exhibit 360 degrees to observe it. The Digital Archiving Department can also provide interactive views that allow users to zoom in and out to check the details of the exhibit. The Digital Archiving Department can also provide interactive views that allow users to change their viewpoint and observe the exhibit from different angles. This allows users to observe the exhibits in more detail.

[0031] The digital archiving unit can add videos of the exhibit's production process and interviews with the artist to promote deeper understanding. For example, the digital archiving unit adds videos recording the exhibit's production process to the digital archive. For example, the digital archiving unit may film an artist creating a work and include the videos in the archive. The digital archiving unit may also add interview videos of the artist. For example, the digital archiving unit may add interview videos in which the artist explains the intentions and techniques behind the work. The digital archiving unit may also provide a combination of the exhibit's production process and the artist's interview videos. For example, the digital archiving unit may play the video of the production process and the interview video consecutively. The digital archiving unit may also allow users to freely select and watch the exhibit's production process or the artist's interview video. This allows users to gain a deeper understanding of the background and intentions of the exhibit.

[0032] The digital archiving unit can add audio commentary or background music for the exhibits to provide an experience that can be enjoyed both visually and aurally. For example, the digital archiving unit can add audio commentary for the exhibits to the digital archive, allowing the user to enjoy the exhibits both visually and aurally. For example, the digital archiving unit can provide an audio description of the exhibits. The digital archiving unit can also add background music for the exhibits. For example, the digital archiving unit can provide music that matches the theme of the exhibits. The digital archiving unit can also provide a combination of audio commentary and background music. For example, the digital archiving unit can provide an audio description of the exhibits while simultaneously playing background music. The digital archiving unit can also allow the user to freely select the audio commentary or background music. This allows the user to enjoy the exhibits both visually and aurally.

[0033] The digital archiving unit can share digital archives between different art museums, allowing users to visit multiple art museums on a single platform. The digital archiving unit can, for example, build a system for sharing digital archives between different art museums, allowing users to visit multiple art museums on a single platform. For example, it can integrate the digital archives of multiple art museums. The digital archiving unit can also centrally manage the digital archives of different art museums. For example, it can share digital archives using a common data format. The digital archiving unit can also enable users to search for exhibits at multiple art museums on a single platform. For example, it can provide keyword searches and category searches. The digital archiving unit can also link the digital archives of different art museums, allowing users to visit multiple art museums seamlessly. This allows users to visit multiple art museums on a single platform.

[0034] The guide registration unit can use the generation AI to analyze the guide's past tour history and evaluations and develop an algorithm to recommend the most suitable guide. The guide registration unit can, for example, use the generation AI to analyze the guide's past tour history and evaluations and develop an algorithm to recommend the most suitable guide. For example, it can select a guide based on the evaluation scores of past tours and participant feedback. The guide registration unit can also use the generation AI to analyze the guide's expertise and speaking style and recommend the most suitable guide. For example, it can preferentially recommend guides who are knowledgeable in a particular genre. The guide registration unit can also use the generation AI to evaluate the guide's performance based on the guide's past tour history and evaluations. This makes it possible to recommend the most suitable guide to the user.

[0035] The guide registration unit can analyze the guide's expertise and speaking style and match a guide that suits the user's preferences. The guide registration unit, for example, analyzes the guide's expertise and builds a system that matches a guide that suits the user's preferences. For example, it can preferentially recommend guides who are knowledgeable in a particular genre. The guide registration unit can also analyze the guide's speaking style and match a guide that suits the user's preferences. For example, it can select a guide based on the tempo of the guide's speech and tone of voice. The guide registration unit can also evaluate the guide's performance based on the guide's expertise and speaking style. This makes it possible to provide a guide that suits the user's preferences.

[0036] The guide registration unit can add highlight videos of past tours and user reviews to the guide's profile to help the user make a selection. For example, the guide registration unit adds highlight videos of past tours to the guide's profile to help the user make a selection. For example, an edited video of part of the guide's tour is posted to the profile. The guide registration unit also adds user reviews to the guide's profile. For example, ratings and comments from users who participated in past tours are posted to the profile. The guide registration unit can also provide a combination of highlight videos and reviews to the guide's profile. For example, the tour highlight video and user reviews are displayed consecutively. The guide registration unit can also add detailed information of past tours to the guide's profile. This can provide reference information when the user selects a guide.

[0037] The guide registration unit can register guides with different languages ​​and cultural backgrounds, making it possible to accommodate international users. The guide registration unit, for example, registers guides with different languages ​​and cultural backgrounds, building a system that can accommodate international users. For example, it registers guides that support multiple languages. The guide registration unit can also register guides with different cultural backgrounds. For example, it can register guides who are excellent at understanding different cultures. The guide registration unit can also evaluate the performance of guides based on guides with different languages ​​and cultural backgrounds. This makes it possible to accommodate international users.

[0038] The matching unit can use the generation AI to provide additional information and related exhibits in real time about exhibits in which the user has shown interest during the virtual tour. For example, the matching unit uses the generation AI to build a system that provides additional information in real time about exhibits in which the user has shown interest during the virtual tour. For example, it displays detailed descriptions of the exhibits and information about related artists. The matching unit can also use the generation AI to provide related exhibits in real time about exhibits in which the user has shown interest during the virtual tour. For example, it displays works by the same artist or exhibits with the same theme. The matching unit can also use the generation AI to optimize information about exhibits and related exhibits based on the user's interests. This makes it possible to provide additional information and related exhibits in real time about exhibits in which the user has shown interest.

[0039] The matching unit can analyze the content of questions asked by users during a virtual tour and support the guide so that they can provide an immediate response. For example, the matching unit uses a generation AI to analyze the content of questions asked by users during a virtual tour and builds a system that supports the guide so that they can provide an immediate response. For example, the content of the question can be automatically analyzed and related information can be provided to the guide. The matching unit can also optimize the guide's response based on the content of the user's question. For example, the generation AI can suggest the optimal response to the question. The matching unit can also evaluate the guide's performance based on the content of the user's question. This allows the guide to provide an immediate response to the user's question.

[0040] The matching unit can add a chat function that allows users to interact with other users in real time during a virtual tour, thereby promoting a collaborative experience. The matching unit, for example, builds a system that adds a chat function that allows users to interact with other users in real time during a virtual tour. For example, users participating in the same tour can exchange opinions through chat. The matching unit can also provide functions such as text chat, voice chat, and video chat. For example, users can send messages via text or have voice or video conversations. The matching unit can also enable users to share collaborative experiences through the chat function. This allows users to interact with each other in real time.

[0041] The matching unit can provide a recording function for the virtual tour so that the tour can be viewed again later. The matching unit, for example, provides a recording function for the virtual tour and builds a system that allows the user to view the tour again later. For example, the matching unit can store recorded data of the tour so that the user can access it at any time. The matching unit can also enable the recorded data to be viewed on-demand. For example, the recorded data can be played back at a user's preferred time. The matching unit can also provide the recorded data in a streaming format. For example, the recorded data can be played back in real time via the Internet. The matching unit can also provide the recorded data in a download format. This allows the user to view the virtual tour again later.

[0042] The feedback unit can use the generation AI to automatically analyze the feedback content and make specific suggestions for improvements to the museum's exhibits and services. The feedback unit, for example, uses the generation AI to automatically analyze the feedback content and build a system that makes specific suggestions for improvements to the museum's exhibits and services. For example, it analyzes user feedback and suggests improvements to the layout of exhibits and explanations. The feedback unit can also use the generation AI to suggest improvements to the museum's services based on the feedback content. For example, it can plan new services by reflecting user opinions. The feedback unit can also use the generation AI to optimize the museum's exhibits based on the feedback content. This makes it possible to make specific suggestions for improvements to the museum's exhibits and services.

[0043] The feedback unit can dynamically change the layout and explanations of exhibits in the museum based on the feedback. The feedback unit, for example, builds a system that dynamically changes the layout and explanations of exhibits in the museum based on the feedback. For example, the layout of exhibits is optimized by reflecting the user's opinions. The feedback unit can also change the explanations of exhibits based on the feedback. For example, the explanations are updated by reflecting the user's opinions. The feedback unit can also change the layout and explanations of exhibits in real time based on the feedback. This allows the layout and explanations of exhibits in the museum to be dynamically changed.

[0044] The feedback department can share the feedback with other museums and artists and use it to improve the industry as a whole. For example, the feedback department can build a system to share the feedback with other museums and artists and use it to improve the industry as a whole. For example, it can provide a platform for sharing feedback data. The feedback department can also propose improvements for the industry as a whole based on the feedback. For example, it can propose improvements to exhibition content or services. The feedback department can also propose joint projects based on the feedback. This will help improve the industry as a whole.

[0045] The feedback unit can propose new exhibits and event plans based on the feedback. The feedback unit, for example, builds a system that proposes new exhibits and event plans based on the feedback. For example, a new exhibit is planned by reflecting the user's opinions. The feedback unit can also propose new events based on the feedback. For example, a new event is planned based on the user's interests. The feedback unit can also optimize the exhibits and event plans based on the feedback. This makes it possible to propose new exhibits and event plans.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The virtual museum system can provide quizzes and trivia related to the exhibits to further attract users' interest. For example, a quiz about the exhibits can be displayed, and users can earn points by answering it. Trivia information related to the exhibits can also be displayed, allowing users to gain new knowledge. Furthermore, each time a user answers a quiz correctly, a hint about the next exhibit can be provided. This allows users to learn about the exhibits while having fun.

[0048] In addition to 3D models of exhibits, the Digital Archiving Department can provide interactive features that allow users to virtually manipulate parts of the exhibits. For example, users can rotate part of a sculpture or zoom in on a specific part of a painting to see the details. Users can also simulate the virtual restoration of part of an exhibit. Furthermore, users can change the color of an exhibit or observe it under different lighting conditions. This allows users to understand and enjoy the exhibits more deeply.

[0049] In addition to videos of the exhibit's creation process and interviews with artists, the Digital Archiving Department can also provide live sessions where users can directly interact with artists. For example, an artist can live explain the creation process of a work and users can ask questions in real time. It can also hold interactive sessions where artists answer users' questions. Furthermore, artists can demonstrate specific techniques at users' requests. This allows users to gain a deeper understanding of the exhibits through direct interaction with the artists.

[0050] The digital archiving unit may provide a function that allows users to select and play their favorite music in addition to audio commentary or background music for exhibits. For example, users can play music from their own playlist while viewing exhibits. Music that matches the theme of the exhibit can also be automatically suggested. Furthermore, users can select music that matches their emotions regarding a particular exhibit. This allows users to enjoy the exhibits both visually and aurally.

[0051] The digital archiving unit can share digital archives between different museums, allowing users to visit multiple museums on a single platform. For example, a user can view exhibits from multiple museums with a single account. It can also provide a function for comparing exhibits from different museums. Furthermore, users can save exhibits from different museums as a single collection and revisit them later. This allows users to visit multiple museums on a single platform.

[0052] The guide registration unit uses generative AI to analyze guides' past tour history and evaluations, and develop an algorithm to recommend the most suitable guide. For example, guides can be selected based on evaluation scores from past tours and participant feedback. It can also analyze guides' expertise and speaking style to recommend the most suitable guide. It can also evaluate guide performance based on the guide's past tour history and evaluations. This allows it to recommend the most suitable guide to the user.

[0053] The guide registration unit can analyze the guide's expertise and speaking style to match a guide that suits the user's preferences. For example, it can preferentially recommend a guide who is knowledgeable in a particular genre. It can also analyze the guide's speaking style to match a guide that suits the user's preferences. It can also evaluate the guide's performance based on the guide's expertise and speaking style. This makes it possible to provide a guide that suits the user's preferences.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The Digital Archiving Department creates a digital archive of the museum's interior or exhibits. For example, it can digitally archive the museum's exhibits with high-resolution images and videos, provide a 360-degree panoramic view of the exhibition room, and provide detailed images of the exhibits. Step 2: The guide registration department registers online tour guides with their specialties, such as a guide who is knowledgeable about contemporary art, a guide who is knowledgeable about classical paintings, and a guide who is knowledgeable about sculptures. Step 3: The matching unit matches users with museums and guides. For example, it analyzes the user's interests and past browsing history to suggest the most suitable guide. It also matches based on the museum's exhibits and the guide's expertise. Step 4: The feedback unit analyzes the topics discussed in the guide and the user's interests and provides feedback to the museum. For example, if the user shows interest in a particular exhibit, the feedback unit can provide detailed information about that exhibit and analyze the user's interests to help the museum improve its exhibits and services. The feedback unit can also evaluate the guide's performance and provide feedback to the museum.

[0056] (Example 2) The virtual museum system according to an embodiment of the present invention is a system that digitally archives the interior and exhibits of a museum and provides them as a virtual museum. This allows users who are far away or busy to enjoy the museum. The virtual museum system allows users who are far away or busy to enjoy the museum, and also contributes to increasing the museum's revenue and improving its services.

[0057] A virtual art museum system according to an embodiment includes a digital archiving unit, a guide registration unit, a matching unit, and a feedback unit. The digital archiving unit digitally archives the interior of an art museum or its exhibits. For example, the digital archiving unit digitally archives the art museum's exhibits using high-resolution images and videos. The digital archiving unit can also provide a 360-degree panoramic view of the exhibition room. The digital archiving unit can also provide detailed images of the exhibits. The guide registration unit registers online tour guides with specialized genres. For example, the guide registration unit can register a guide who is knowledgeable about contemporary art. The guide registration unit can also register a guide who is knowledgeable about classical paintings. The guide registration unit can also register a guide who is knowledgeable about sculptures. The matching unit matches users with art museums and guides. For example, the matching unit can analyze a user's interests and past browsing history to suggest the most suitable guide. The matching unit can also match guides based on the content of the art museum's exhibits. The matching unit can also match users based on the guide's expertise. The feedback unit analyzes the topics discussed during the guide and the user's interests and provides feedback to the museum. For example, if the user shows interest in a particular exhibit, the feedback unit provides detailed information about that exhibit. The feedback unit can also analyze the user's interests and use the information to improve the museum's exhibit content and services. The feedback unit can also evaluate the guide's performance and provide feedback to the museum. This allows the virtual museum system according to the embodiment to allow even users who are far away or busy to enjoy the museum, contributing to increased revenue and improved services for the museum. For example, users can visit museums around the world from the comfort of their own homes and gain a deeper understanding through guided tours. Furthermore, museums can understand users' interests and optimize their exhibit content to convey their appeal to a wider audience.

[0058] The Digital Archiving Department can automatically generate 3D models of exhibits and provide interactive views that users can freely rotate and zoom in and out. The Digital Archiving Department can develop a system that automatically generates 3D models of exhibits using generative AI. For example, it can take photos of an exhibit from multiple angles and generate a 3D model based on those photos. The Digital Archiving Department can also provide interactive views that users can freely rotate and zoom in and out. For example, users can rotate an exhibit 360 degrees to observe it. The Digital Archiving Department can also provide interactive views that allow users to zoom in and out to check the details of the exhibit. The Digital Archiving Department can also provide interactive views that allow users to change their viewpoint and observe the exhibit from different angles. This allows users to observe the exhibits in more detail.

[0059] The digital archiving unit can add videos of the exhibit's production process and interviews with the artist to promote deeper understanding. For example, the digital archiving unit adds videos recording the exhibit's production process to the digital archive. For example, the digital archiving unit may film an artist creating a work and include the videos in the archive. The digital archiving unit may also add interview videos of the artist. For example, the digital archiving unit may add interview videos in which the artist explains the intentions and techniques behind the work. The digital archiving unit may also provide a combination of the exhibit's production process and the artist's interview videos. For example, the digital archiving unit may play the video of the production process and the interview video consecutively. The digital archiving unit may also allow users to freely select and watch the exhibit's production process or the artist's interview video. This allows users to gain a deeper understanding of the background and intentions of the exhibit.

[0060] The digital archiving unit can use the emotion estimation function to record the emotional response of a user when viewing a specific exhibit and recommend related exhibits the next time the user views the exhibit. For example, the digital archiving unit uses the emotion estimation function to record the emotional response of a user when viewing a specific exhibit in real time. For example, the digital archiving unit analyzes the user's facial expressions and voice to calculate an emotion score. The digital archiving unit also uses the emotion estimation function to recommend related exhibits the next time the user views the exhibit based on the user's emotional response. For example, exhibits with a high number of positive emotional responses are preferentially recommended. The digital archiving unit can also generate a list of recommended exhibits based on the user's emotional response. For example, exhibits with a high emotion score are displayed at the top of the list. The digital archiving unit can also optimize the exhibit recommendation algorithm based on the user's emotional response. This allows related exhibits to be recommended based on the user's interests.

[0061] The digital archiving unit can add audio commentary or background music for the exhibits to provide an experience that can be enjoyed both visually and aurally. For example, the digital archiving unit can add audio commentary for the exhibits to the digital archive, allowing the user to enjoy the exhibits both visually and aurally. For example, the digital archiving unit can provide an audio description of the exhibits. The digital archiving unit can also add background music for the exhibits. For example, the digital archiving unit can provide music that matches the theme of the exhibits. The digital archiving unit can also provide a combination of audio commentary and background music. For example, the digital archiving unit can provide an audio description of the exhibits while simultaneously playing background music. The digital archiving unit can also allow the user to freely select the audio commentary or background music. This allows the user to enjoy the exhibits both visually and aurally.

[0062] The digital archiving unit can share digital archives between different art museums, allowing users to visit multiple art museums on a single platform. The digital archiving unit can, for example, build a system for sharing digital archives between different art museums, allowing users to visit multiple art museums on a single platform. For example, it can integrate the digital archives of multiple art museums. The digital archiving unit can also centrally manage the digital archives of different art museums. For example, it can share digital archives using a common data format. The digital archiving unit can also enable users to search for exhibits at multiple art museums on a single platform. For example, it can provide keyword searches and category searches. The digital archiving unit can also link the digital archives of different art museums, allowing users to visit multiple art museums seamlessly. This allows users to visit multiple art museums on a single platform.

[0063] The digital archiving unit can use the emotion estimation function to recommend related exhibits and artists based on the emotions felt by the user while browsing. For example, the digital archiving unit uses the emotion estimation function to analyze the emotions felt by the user while browsing in real time and recommend related exhibits and artists based on that data. For example, exhibits with a high number of positive emotional responses are preferentially recommended. The digital archiving unit can also generate a list of recommended exhibits and artists based on the user's emotional response. For example, exhibits and artists with high emotional scores are displayed at the top of the list. The digital archiving unit can also optimize the recommendation algorithm for exhibits and artists based on the user's emotional response. This allows related exhibits and artists to be recommended based on the user's emotions.

[0064] The guide registration unit can use the generation AI to analyze the guide's past tour history and evaluations and develop an algorithm to recommend the most suitable guide. The guide registration unit can, for example, use the generation AI to analyze the guide's past tour history and evaluations and develop an algorithm to recommend the most suitable guide. For example, it can select a guide based on the evaluation scores of past tours and participant feedback. The guide registration unit can also use the generation AI to analyze the guide's expertise and speaking style and recommend the most suitable guide. For example, it can preferentially recommend guides who are knowledgeable in a particular genre. The guide registration unit can also use the generation AI to evaluate the guide's performance based on the guide's past tour history and evaluations. This makes it possible to recommend the most suitable guide to the user.

[0065] The guide registration unit can analyze the guide's expertise and speaking style and match a guide that suits the user's preferences. The guide registration unit, for example, analyzes the guide's expertise and builds a system that matches a guide that suits the user's preferences. For example, it can preferentially recommend guides who are knowledgeable in a particular genre. The guide registration unit can also analyze the guide's speaking style and match a guide that suits the user's preferences. For example, it can select a guide based on the tempo of the guide's speech and tone of voice. The guide registration unit can also evaluate the guide's performance based on the guide's expertise and speaking style. This makes it possible to provide a guide that suits the user's preferences.

[0066] The guide registration unit can use the emotion estimation function to analyze the user's emotional response in real time and dynamically adjust the guide topic and commentary content. The guide registration unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotional response in real time and dynamically adjusts the guide topic and commentary content. For example, it prioritizes explaining topics that the user is interested in. The guide registration unit can also use the emotion estimation function to optimize the guide topic and commentary content based on the user's emotional response. For example, it can focus on explaining topics that have a high number of positive emotional responses. The guide registration unit can also use the emotion estimation function to evaluate the guide's performance based on the user's emotional response. This makes it possible to adjust the guide topic and commentary content according to the user's emotions.

[0067] The guide registration unit can add highlight videos of past tours and user reviews to the guide's profile to help the user make a selection. For example, the guide registration unit adds highlight videos of past tours to the guide's profile to help the user make a selection. For example, an edited video of part of the guide's tour is posted to the profile. The guide registration unit also adds user reviews to the guide's profile. For example, ratings and comments from users who participated in past tours are posted to the profile. The guide registration unit can also provide a combination of highlight videos and reviews to the guide's profile. For example, the tour highlight video and user reviews are displayed consecutively. The guide registration unit can also add detailed information of past tours to the guide's profile. This can provide reference information when the user selects a guide.

[0068] The guide registration unit can register guides with different languages ​​and cultural backgrounds, making it possible to accommodate international users. The guide registration unit, for example, registers guides with different languages ​​and cultural backgrounds, building a system that can accommodate international users. For example, it registers guides that support multiple languages. The guide registration unit can also register guides with different cultural backgrounds. For example, it can register guides who are excellent at understanding different cultures. The guide registration unit can also evaluate the performance of guides based on guides with different languages ​​and cultural backgrounds. This makes it possible to accommodate international users.

[0069] The guide registration unit can use the emotion estimation function to identify the guide in which the user is most interested and recommend that guide preferentially. The guide registration unit, for example, uses the emotion estimation function to build a system that identifies the guide in which the user is most interested. For example, it analyzes the user's emotional response and recommends a guide that is likely to be of interest. The guide registration unit can also use the emotion estimation function to generate a list of recommended guides based on the user's emotional response. For example, guides with high emotion scores are displayed at the top of the list. The guide registration unit can also use the emotion estimation function to evaluate the performance of the guide based on the user's emotional response. This allows the guide in which the user is most interested to be recommended preferentially.

[0070] The matching unit can use the generation AI to provide additional information and related exhibits in real time about exhibits in which the user has shown interest during the virtual tour. For example, the matching unit uses the generation AI to build a system that provides additional information in real time about exhibits in which the user has shown interest during the virtual tour. For example, it displays detailed descriptions of the exhibits and information about related artists. The matching unit can also use the generation AI to provide related exhibits in real time about exhibits in which the user has shown interest during the virtual tour. For example, it displays works by the same artist or exhibits with the same theme. The matching unit can also use the generation AI to optimize information about exhibits and related exhibits based on the user's interests. This makes it possible to provide additional information and related exhibits in real time about exhibits in which the user has shown interest.

[0071] The matching unit can analyze the content of questions asked by users during a virtual tour and support the guide so that they can provide an immediate response. For example, the matching unit uses a generation AI to analyze the content of questions asked by users during a virtual tour and builds a system that supports the guide so that they can provide an immediate response. For example, the content of the question can be automatically analyzed and related information can be provided to the guide. The matching unit can also optimize the guide's response based on the content of the user's question. For example, the generation AI can suggest the optimal response to the question. The matching unit can also evaluate the guide's performance based on the content of the user's question. This allows the guide to provide an immediate response to the user's question.

[0072] The matching unit can use the emotion estimation function to analyze the user's emotional response and optimize the progress of the tour. For example, the matching unit uses the emotion estimation function to analyze the user's emotional response in real time and build a system that optimizes the progress of the tour. For example, the matching unit extends the explanation about exhibits that the user is interested in. The matching unit can also use the emotion estimation function to adjust the progress of the tour based on the user's emotional response. For example, the matching unit prioritizes explanations about exhibits that have a high number of positive emotional responses. The matching unit can also use the emotion estimation function to optimize the tour progress algorithm based on the user's emotional response. This makes it possible to optimize the progress of the tour based on the user's emotions.

[0073] The matching unit can add a chat function that allows users to interact with other users in real time during a virtual tour, thereby promoting a collaborative experience. The matching unit, for example, builds a system that adds a chat function that allows users to interact with other users in real time during a virtual tour. For example, users participating in the same tour can exchange opinions through chat. The matching unit can also provide functions such as text chat, voice chat, and video chat. For example, users can send messages via text or have voice or video conversations. The matching unit can also enable users to share collaborative experiences through the chat function. This allows users to interact with each other in real time.

[0074] The matching unit can provide a recording function for the virtual tour so that the tour can be viewed again later. The matching unit, for example, provides a recording function for the virtual tour and builds a system that allows the user to view the tour again later. For example, the matching unit can store recorded data of the tour so that the user can access it at any time. The matching unit can also enable the recorded data to be viewed on-demand. For example, the recorded data can be played back at a user's preferred time. The matching unit can also provide the recorded data in a streaming format. For example, the recorded data can be played back in real time via the Internet. The matching unit can also provide the recorded data in a download format. This allows the user to view the virtual tour again later.

[0075] The matching unit can use the emotion estimation function to identify the parts that the user enjoyed most and save those parts as highlights. The matching unit, for example, uses the emotion estimation function to build a system that identifies the parts that the user enjoyed most and saves those parts as highlights. For example, it automatically extracts parts that have a high number of positive emotional responses. The matching unit can also use the emotion estimation function to generate highlights based on the user's emotional responses. For example, it saves parts with high emotional scores as highlights. The matching unit can also use the emotion estimation function to optimize highlight selection criteria based on the user's emotional responses. This makes it possible to save the parts that the user enjoyed most as highlights.

[0076] The feedback unit can use the generation AI to automatically analyze the feedback content and make specific suggestions for improvements to the museum's exhibits and services. The feedback unit, for example, uses the generation AI to automatically analyze the feedback content and build a system that makes specific suggestions for improvements to the museum's exhibits and services. For example, it analyzes user feedback and suggests improvements to the layout of exhibits and explanations. The feedback unit can also use the generation AI to suggest improvements to the museum's services based on the feedback content. For example, it can plan new services by reflecting user opinions. The feedback unit can also use the generation AI to optimize the museum's exhibits based on the feedback content. This makes it possible to make specific suggestions for improvements to the museum's exhibits and services.

[0077] The feedback unit can dynamically change the layout and explanations of exhibits in the museum based on the feedback. The feedback unit, for example, builds a system that dynamically changes the layout and explanations of exhibits in the museum based on the feedback. For example, the layout of exhibits is optimized by reflecting the user's opinions. The feedback unit can also change the explanations of exhibits based on the feedback. For example, the explanations are updated by reflecting the user's opinions. The feedback unit can also change the layout and explanations of exhibits in real time based on the feedback. This allows the layout and explanations of exhibits in the museum to be dynamically changed.

[0078] The feedback department can share the feedback with other museums and artists and use it to improve the industry as a whole. For example, the feedback department can build a system to share the feedback with other museums and artists and use it to improve the industry as a whole. For example, it can provide a platform for sharing feedback data. The feedback department can also propose improvements for the industry as a whole based on the feedback. For example, it can propose improvements to exhibition content or services. The feedback department can also propose joint projects based on the feedback. This will help improve the industry as a whole.

[0079] The feedback unit can propose new exhibits and event plans based on the feedback. The feedback unit, for example, builds a system that proposes new exhibits and event plans based on the feedback. For example, a new exhibit is planned by reflecting the user's opinions. The feedback unit can also propose new events based on the feedback. For example, a new event is planned based on the user's interests. The feedback unit can also optimize the exhibits and event plans based on the feedback. This makes it possible to propose new exhibits and event plans.

[0080] The feedback unit can use the emotion estimation function to identify exhibits or topics that the user is most interested in and provide feedback based on that. The feedback unit, for example, uses the emotion estimation function to identify exhibits or topics that the user is most interested in and builds a system that provides feedback based on that. For example, the feedback unit prioritizes analysis of feedback related to exhibits with many positive emotional responses. The feedback unit can also use the emotion estimation function to optimize the content of the feedback based on the user's emotional response. For example, the feedback unit prioritizes feedback related to exhibits with high emotional scores. The feedback unit can also use the emotion estimation function to evaluate the quality of the feedback based on the user's emotional response. This makes it possible to provide feedback based on the exhibits or topics that the user is most interested in.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The virtual museum system can provide quizzes and trivia related to the exhibits to further attract users' interest. For example, a quiz about the exhibits can be displayed, and users can earn points by answering it. Trivia information related to the exhibits can also be displayed, allowing users to gain new knowledge. Furthermore, each time a user answers a quiz correctly, a hint about the next exhibit can be provided. This allows users to learn about the exhibits while having fun.

[0083] In addition to 3D models of exhibits, the Digital Archiving Department can provide interactive features that allow users to virtually manipulate parts of the exhibits. For example, users can rotate part of a sculpture or zoom in on a specific part of a painting to see the details. Users can also simulate the virtual restoration of part of an exhibit. Furthermore, users can change the color of an exhibit or observe it under different lighting conditions. This allows users to understand and enjoy the exhibits more deeply.

[0084] In addition to videos of the exhibit's creation process and interviews with artists, the Digital Archiving Department can also provide live sessions where users can directly interact with artists. For example, an artist can live explain the creation process of a work and users can ask questions in real time. It can also hold interactive sessions where artists answer users' questions. Furthermore, artists can demonstrate specific techniques at users' requests. This allows users to gain a deeper understanding of the exhibits through direct interaction with the artists.

[0085] The digital archiving unit can use its emotion estimation function to record the user's emotional response when viewing a particular exhibit and recommend related exhibits the next time the user views the exhibit. For example, if a user is moved by a particular painting, other works by the same artist can be recommended. Also, if a user shows interest in sculpture, exhibits related to sculpture can be displayed preferentially. Furthermore, if a user shows positive emotions toward a particular theme, exhibits related to that theme can be recommended. This allows related exhibits to be recommended based on the user's interests.

[0086] The digital archiving unit may provide a function that allows users to select and play their favorite music in addition to audio commentary or background music for exhibits. For example, users can play music from their own playlist while viewing exhibits. Music that matches the theme of the exhibit can also be automatically suggested. Furthermore, users can select music that matches their emotions regarding a particular exhibit. This allows users to enjoy the exhibits both visually and aurally.

[0087] The digital archiving unit can share digital archives between different museums, allowing users to visit multiple museums on a single platform. For example, a user can view exhibits from multiple museums with a single account. It can also provide a function for comparing exhibits from different museums. Furthermore, users can save exhibits from different museums as a single collection and revisit them later. This allows users to visit multiple museums on a single platform.

[0088] The digital archiving unit can use the emotion estimation function to recommend related exhibits and artists based on the emotions felt by the user while browsing. For example, if a user is moved by a particular exhibit, other works by the same artist can be recommended. Also, if a user expresses positive emotions toward a particular theme, exhibits related to that theme can be preferentially displayed. Furthermore, if a user expresses interest in a particular genre, exhibits related to that genre can be recommended. This allows related exhibits and artists to be recommended based on the user's emotions.

[0089] The guide registration unit uses generative AI to analyze guides' past tour history and evaluations, and develop an algorithm to recommend the most suitable guide. For example, guides can be selected based on evaluation scores from past tours and participant feedback. It can also analyze guides' expertise and speaking style to recommend the most suitable guide. It can also evaluate guide performance based on the guide's past tour history and evaluations. This allows it to recommend the most suitable guide to the user.

[0090] The guide registration unit can analyze the guide's expertise and speaking style to match a guide that suits the user's preferences. For example, it can preferentially recommend a guide who is knowledgeable in a particular genre. It can also analyze the guide's speaking style to match a guide that suits the user's preferences. It can also evaluate the guide's performance based on the guide's expertise and speaking style. This makes it possible to provide a guide that suits the user's preferences.

[0091] The guide registration unit can use the emotion estimation function to analyze the user's emotional response in real time and dynamically adjust the guide topic and explanation content. For example, it can prioritize explanations on topics that the user is interested in. It can also optimize the guide topic and explanation content based on the user's emotional response. It can also evaluate the guide's performance based on the user's emotional response. This makes it possible to adjust the guide topic and explanation content according to the user's emotions.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The Digital Archiving Department creates a digital archive of the museum's interior or exhibits. For example, it can digitally archive the museum's exhibits with high-resolution images and videos, provide a 360-degree panoramic view of the exhibition room, and provide detailed images of the exhibits. Step 2: The guide registration department registers online tour guides with their specialties, such as a guide who is knowledgeable about contemporary art, a guide who is knowledgeable about classical paintings, and a guide who is knowledgeable about sculptures. Step 3: The matching unit matches users with museums and guides. For example, it analyzes the user's interests and past browsing history to suggest the most suitable guide. It also matches based on the museum's exhibits and the guide's expertise. Step 4: The feedback unit analyzes the topics discussed in the guide and the user's interests and provides feedback to the museum. For example, if the user shows interest in a particular exhibit, the feedback unit can provide detailed information about that exhibit and analyze the user's interests to help the museum improve its exhibits and services. The feedback unit can also evaluate the guide's performance and provide feedback to the museum.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 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. The Digital Archiving Department creates digital archives of the museum's interior and exhibitions; A guide registration department that registers online tour guides with their specialty genres, A matching section that matches users with museums and guides; A feedback unit that analyzes topics being guided and the user's interests and provides feedback to the museum. A system characterized by:

2. The digital archiving unit Automatically generate 3D models of exhibits and provide interactive views that users can freely rotate and zoom in and out.

2. The system of claim 1.

3. The digital archiving unit Add audio commentary or background music to your exhibits to create an experience that is both visually and aurally engaging.

2. The system of claim 1.

4. The guide registration unit Using generative AI, we will analyze the guide's past tour history and ratings and develop an algorithm to recommend the most suitable guide.

2. The system of claim 1.

5. The matching unit Using generative AI, the system provides additional information and related exhibits in real time about exhibits that the user is interested in during the virtual tour.

2. The system of claim 1.

6. The feedback unit Using generative AI, the feedback is automatically analyzed and specific suggestions are made for improvements to the museum's exhibits and services.

2. The system of claim 1.

7. The digital archiving unit Recording a user's emotional response when viewing a particular exhibit and recommending related exhibits the next time the user visits 2. The system of claim 1.

8. The guide registration unit Analyzing the user's emotional response in real time and dynamically adjusting the topic or commentary of the guide.

2. The system of claim 1.

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