system

Abstract

The system according to the embodiment aims to provide a deeper enjoyment of the viewing experience of the exhibits. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a provision unit, a VR unit, and an eye camera unit. The reception unit accepts a selection of an exhibit from a user. The generation unit analyzes information about the exhibit accepted by the reception unit and generates an audio guide. The provision unit provides the audio guide generated by the generation unit. The VR unit enables the exhibit to be viewed from all directions based on the audio guide provided by the provision unit. The eye camera unit tracks the user's line of sight based on the viewpoint provided by the VR unit and provides detailed information about parts of interest.

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 technologies do not adequately provide means for enhancing the experience of viewing exhibits, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a deeper enjoyment of the viewing experience of the exhibits. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a provision unit, a VR unit, and an eye camera unit. The reception unit receives a selection of an exhibit from a user. The generation unit analyzes information about the exhibit received by the reception unit and generates an audio guide. The provision unit provides the audio guide generated by the generation unit. The VR unit enables the exhibit to be viewed from all directions based on the audio guide provided by the provision unit. The eye camera unit tracks the user's line of sight based on the viewpoint provided by the VR unit and provides detailed information about parts of interest. [Effects of the Invention]

[0007] The system according to the embodiment allows the user to enjoy the experience of viewing exhibits in a deeper way. [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 pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example 1) An art appreciation system according to an embodiment of the present invention collaborates with art galleries and museums to provide and sell apps that allow users to enjoy the worldview and profound depth of art. This art appreciation system uses a generative AI, VR, and an eye camera to provide an audio guide to users. First, the user launches the app and selects an exhibit at an art galleries or museums. Next, the generative AI analyzes information about the selected exhibit and provides the user with an audio guide. The audio guide provides detailed information about the exhibit's history, background, and the artist's intentions. Furthermore, VR technology is used to enable users to appreciate the exhibit from a 360-degree perspective. For example, a user can use a smartphone or VR goggles to view the exhibit from various angles. This allows users to appreciate the exhibit in detail and gain a deeper understanding of the worldview and profound depth of art. The eye camera is also used to track the user's gaze and provide additional information about areas of interest. For example, if a user focuses their gaze on a particular area, a detailed explanation about that area is provided via an audio guide. This allows users to obtain information tailored to their interests and enjoy a more fulfilling appreciation experience. This art appreciation system can provide the latest information about exhibits by collaborating with art galleries and museums. Furthermore, by using generative AI, it is possible to provide users with personalized audio guides. This allows users to obtain information tailored to their interests and to enjoy the worldview and depth of art. In this way, the art appreciation system allows users to enjoy the worldview and depth of art.

[0029] An art appreciation system according to an embodiment includes a reception unit, a generation unit, a provision unit, a VR unit, and an eye camera unit. The reception unit accepts a selection of an exhibit from a user. The user can select an exhibit using, for example, a touchscreen, voice input, or gesture recognition. The generation unit uses a generation AI to analyze information about the exhibit accepted by the reception unit and generate an audio guide. The generation AI analyzes information about the exhibit using, for example, natural language processing, image recognition, or data mining. The provision unit provides the audio guide generated by the generation unit. The audio guide is provided in the form of, for example, an audio file, text-to-speech, or an interactive guide. The VR unit enables the user to view the exhibit from all directions based on the audio guide provided by the provision unit. The user can view the exhibit from various angles using, for example, a 360-degree camera, VR goggles, or a panoramic view. The eye camera unit tracks the user's gaze based on the viewpoint provided by the VR unit and provides detailed information about the part of the exhibit that interests them. Gaze tracking is performed using, for example, eye tracking technology, camera type, tracking accuracy, and the like. As a result, the art appreciation system according to the embodiment allows the user to enjoy the worldview and depth of art.

[0030] The generation unit can learn the user's past listening history and interests and generate a personalized audio guide. The generation unit, for example, collects the user's past listening history and generates the audio guide based on that data. The past listening history includes, for example, browsing history, rating history, and comment history. The generation unit analyzes this data and learns the user's interests. Methods for measuring interest include, for example, the number of clicks, the length of stay, and user feedback. The generation unit generates a personalized audio guide based on the user's interests. This allows the user to be provided with a personalized audio guide. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input the user's past listening history into the generation AI and have the generation AI generate a personalized audio guide.

[0031] The VR unit can enable a user to view an exhibit from multiple angles using a smartphone or VR goggles. The VR unit, for example, provides a system for a user to view an exhibit using a smartphone or VR goggles. Types of smartphones and VR goggles include, for example, specific devices and necessary applications. Using these devices, a user can view an exhibit from multiple angles, such as horizontal angles, vertical angles, and rotational angles. This allows the user to view the exhibit from a 360-degree perspective. Some or all of the above-described processing in the VR unit may be performed using AI or without AI. For example, the VR unit can input the user's device information into AI and have the AI ​​provide the optimal perspective.

[0032] The eye camera unit can track the user's gaze and provide additional information on areas of interest. The eye camera unit uses, for example, eye tracking technology to track the user's gaze. Eye tracking technology includes, for example, a specific camera type and tracking accuracy. When the user directs their gaze at a specific area, the eye camera unit provides detailed information about that area. The additional information includes, for example, text information, images, videos, links, etc. This allows the user to obtain additional information on areas of interest. Some or all of the above-described processing in the eye camera unit may be performed using AI, or may be performed without using AI. For example, the eye camera unit can input the user's gaze data into AI and have the AI ​​provide the additional information.

[0033] The providing unit can provide an audio guide that provides detailed explanations of the history, background, and artist's intentions of the exhibit. The providing unit, for example, collects information about the history, background, and artist's intentions of the exhibit and provides the audio guide based on that information. The historical content includes, for example, the year of production, past exhibition history, and changes in ownership. The background content includes, for example, the cultural background, social background, and technological background. The artist's intention content includes, for example, interviews, comments from the artist, and explanations of the work. This allows the user to obtain detailed information about the exhibit. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the collected information into AI and have the AI ​​generate the audio guide.

[0034] The generation unit can collect data related to the exhibits and generate an audio guide based on that data. The generation unit uses, for example, sensors, databases, APIs, etc. to collect data related to the exhibits. The collected data includes, for example, detailed information about the exhibits, related literature, and past exhibition history. The generation unit generates an audio guide based on this data. This allows the provision of up-to-date information about the exhibits. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the collected data into the generation AI and have the generation AI generate the audio guide.

[0035] The reception unit can analyze the user's past viewing history and suggest optimal exhibit options. The reception unit, for example, collects the user's past viewing history and suggests optimal exhibit options based on that data. The past viewing history includes, for example, browsing history, rating history, comment history, etc. The reception unit analyzes this data to learn the user's interests. Methods for measuring interest include, for example, the number of clicks, length of stay, and user feedback. The reception unit suggests optimal exhibit options based on the user's interests. This makes it possible to suggest optimal exhibits based on the user's past viewing history. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's past viewing history into AI and have the AI ​​suggest optimal exhibit options.

[0036] The reception unit can filter exhibits based on the user's current areas of interest when selecting an exhibit. The reception unit, for example, collects data for identifying the user's current areas of interest. The area of ​​interest data includes, for example, the user's search history, browsing history, and feedback. The reception unit analyzes this data to identify the user's current areas of interest. Methods for measuring the area of ​​interest include, for example, the number of clicks, the length of stay, and the user's feedback. The reception unit filters the exhibit options based on the user's current areas of interest. This allows exhibits to be selected based on the user's current areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's area of ​​interest data into AI and have the AI ​​filter the exhibit options.

[0037] When selecting an exhibit, the reception unit can prioritize relevant exhibits by taking into account the user's geographical location information. The reception unit uses, for example, GPS data, IP address, location information services, etc. to collect the user's geographical location information. Methods for collecting geographical location information include, for example, smartphone location information services and IP addresses when connected to the Internet. The reception unit identifies exhibits related to the user's current location, areas the user has visited in the past, and areas the user plans to visit in the future based on the collected geographical location information. This allows the user to be provided with relevant exhibits based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit may input the user's geographical location information into AI and have the AI ​​present relevant exhibits.

[0038] When selecting an exhibit, the reception unit can analyze the user's social media activity and suggest related exhibits. The reception unit, for example, uses an API or database for collecting the user's social media activity. Social media activity data includes, for example, post content, number of likes, number of followers, etc. The reception unit analyzes this data and identifies the user's interests. The reception unit suggests related exhibits based on the user's social media activity. This makes it possible to suggest related exhibits based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into AI and have the AI ​​suggest related exhibits.

[0039] When generating an audio guide, the generation unit can adjust the level of detail based on the importance of the exhibits. The generation unit, for example, sets criteria for evaluating the importance of the exhibits. Criteria for importance include, for example, the historical value, popularity, and expert evaluation of the exhibits. The generation unit evaluates the importance of the exhibits based on these criteria. The generation unit adjusts the level of detail of the audio guide based on the importance of the exhibits. Methods for adjusting the level of detail include, for example, the depth of information, the range of information, and the type of information. This makes it possible to provide an audio guide that corresponds to the importance of the exhibits. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input importance data of the exhibits into the generation AI and have the generation AI adjust the level of detail of the audio guide.

[0040] When generating an audio guide, the generation unit can apply different generation algorithms depending on the category of the exhibit. For example, the generation unit sets criteria for defining the category of the exhibit. The category criteria include, for example, painting, sculpture, and photography. The generation unit classifies the category of the exhibit based on these criteria. The generation unit applies different generation algorithms depending on the category of the exhibit. The generation algorithms include, for example, machine learning algorithms and rule-based algorithms. This makes it possible to provide an audio guide according to the category of the exhibit. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input exhibit category data into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0041] When generating an audio guide, the generation unit can determine the priority of content based on the exhibition period of the exhibits. The generation unit, for example, sets criteria for evaluating the exhibition period of the exhibits. The exhibition period criteria include, for example, past exhibition history, current exhibition period, and future exhibition schedule. The generation unit evaluates the exhibition period of the exhibits based on these criteria. The generation unit determines the priority of the audio guide content based on the exhibition period of the exhibits. Methods for determining the priority include, for example, the importance of the exhibits, the user's interests, and the exhibition period. This makes it possible to provide an audio guide that is appropriate for the exhibition period of the exhibits. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input exhibition period data of the exhibits into the generation AI and have the generation AI determine the priority of the audio guide content.

[0042] When generating an audio guide, the generation unit can adjust the order of the content based on the relevance of the exhibits. The generation unit, for example, sets criteria for evaluating the relevance of the exhibits. Relevance criteria include, for example, thematic similarity, technical relevance, historical relevance, etc. The generation unit evaluates the relevance of the exhibits based on these criteria. The generation unit adjusts the order of the audio guide content based on the relevance of the exhibits. Methods for adjusting the order include, for example, algorithms, user priorities, filtering conditions, etc. This makes it possible to provide an audio guide that is tailored to the relevance of the exhibits. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input relevance data of the exhibits into the generation AI and have the generation AI adjust the order of the audio guide content.

[0043] When providing an audio guide, the providing unit can select the optimal delivery method by referring to the user's past listening history. The providing unit, for example, collects the user's past listening history and selects the optimal audio guide delivery method based on that data. The past listening history includes, for example, browsing history, rating history, comment history, etc. The providing unit analyzes this data to learn the user's interests. Methods for measuring interest include, for example, the number of clicks, the duration of stay, and user feedback. The providing unit selects the optimal audio guide delivery method based on the user's interests. Criteria for determining the optimal delivery method include, for example, the user's preferences, the type of device, and the type of information. This allows the optimal audio guide to be provided based on the user's past listening history. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input the user's past listening history into AI and have the AI ​​select the optimal audio guide delivery method.

[0044] The providing unit can customize the content of the audio guide based on the user's current areas of interest when providing the audio guide. The providing unit, for example, collects data for identifying the user's current areas of interest. The area of ​​interest data includes, for example, the user's search history, browsing history, and feedback. The providing unit analyzes this data to identify the user's current areas of interest. Methods for measuring the area of ​​interest include, for example, the number of clicks, the length of stay, and the user's feedback. The providing unit customizes the content of the audio guide based on the user's current areas of interest. Methods for content customization include, for example, the level of detail of information, the order of information, and the type of information. This makes it possible to provide an audio guide based on the user's current areas of interest. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input the user's area of ​​interest data into AI and have the AI ​​customize the content of the audio guide.

[0045] When providing an audio guide, the providing unit can select the optimal delivery method taking into account the user's geographical location information. The providing unit uses, for example, GPS data, IP address, location information services, etc. to collect the user's geographical location information. Methods for collecting geographical location information include, for example, smartphone location information services and IP addresses when connected to the Internet. The providing unit identifies audio guides for exhibits related to the user's current location, areas the user has visited in the past, and areas the user plans to visit in the future based on the collected geographical location information. This allows the optimal audio guide to be provided based on the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input the user's geographical location information into AI and have the AI ​​select the optimal audio guide delivery method.

[0046] When providing an audio guide, the providing unit can analyze the user's social media activity and customize the content. The providing unit, for example, uses an API or database to collect the user's social media activity. Social media activity data includes, for example, post content, number of likes, number of followers, etc. The providing unit analyzes this data to identify the user's interests. The providing unit customizes the content of the audio guide based on the user's social media activity. Methods for content customization include, for example, the level of detail of information, the order of information, and the type of information. This makes it possible to provide an audio guide based on the user's social media activity. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input the user's social media data into AI and have the AI ​​customize the content of the audio guide.

[0047] When providing a VR viewpoint, the VR unit can select the optimal viewpoint by referring to the user's past viewing history. For example, the VR unit collects the user's past viewing history and selects the optimal VR viewpoint based on that data. The past viewing history includes, for example, browsing history, rating history, and comment history. The VR unit analyzes this data to learn the user's interests. Methods for measuring interest include, for example, the number of clicks, time spent, and user feedback. The VR unit selects the optimal VR viewpoint based on the user's interests. Criteria for the optimal viewpoint include, for example, the user's preferences, exhibit characteristics, and viewpoint type. This allows the optimal VR viewpoint to be provided based on the user's past viewing history. Some or all of the above-described processing in the VR unit may be performed using AI, or may be performed without AI. For example, the VR unit can input the user's past viewing history into AI and have the AI ​​select the optimal VR viewpoint.

[0048] When providing a VR viewpoint, the VR unit can customize the viewpoint based on the user's current area of ​​interest. The VR unit, for example, collects data to identify the user's current area of ​​interest. The area of ​​interest data includes, for example, the user's search history, browsing history, and feedback. The VR unit analyzes this data to identify the user's current area of ​​interest. Methods for measuring the area of ​​interest include, for example, the number of clicks, the duration of stay, and the user's feedback. The VR unit customizes the VR viewpoint based on the user's current area of ​​interest. Methods for customizing the viewpoint include, for example, the level of detail of the viewpoint, the order of the viewpoints, and the type of viewpoint. This makes it possible to provide a VR viewpoint based on the user's current area of ​​interest. Some or all of the above-described processing in the VR unit may be performed using AI or without AI. For example, the VR unit may input the user's area of ​​interest data into AI and have the AI ​​customize the VR viewpoint.

[0049] When providing a VR viewpoint, the VR unit can select an optimal viewpoint by taking into account the user's geographical location information. The VR unit uses, for example, GPS data, an IP address, a location information service, etc. to collect the user's geographical location information. Methods for collecting geographical location information include, for example, a smartphone's location information service and an IP address when connected to the Internet. Based on the collected geographical location information, the VR unit identifies viewpoints of exhibits related to the user's current location, areas visited in the past, and areas planned to be visited in the future. This allows the optimal VR viewpoint to be provided based on the user's geographical location information. Some or all of the above-described processing in the VR unit may be performed using AI or without AI. For example, the VR unit can input the user's geographical location information into AI and have the AI ​​select an optimal VR viewpoint.

[0050] When providing a VR viewpoint, the VR unit can customize the viewpoint by analyzing the user's social media activity. The VR unit, for example, uses an API or database to collect the user's social media activity. Social media activity data includes, for example, post content, number of likes, number of followers, etc. The VR unit analyzes this data to identify the user's interests. The VR unit customizes the VR viewpoint based on the user's social media activity. Methods for viewpoint customization include, for example, the level of detail of the viewpoint, the order of the viewpoints, and the type of viewpoint. This allows the VR viewpoint to be provided based on the user's social media activity. Some or all of the above-described processing in the VR unit may be performed using AI, or may be performed without using AI. For example, the VR unit can input the user's social media data into AI and have the AI ​​customize the VR viewpoint.

[0051] The eye camera unit can select the optimal tracking method by referring to the user's past viewing history when tracking gaze. For example, the eye camera unit collects the user's past viewing history and selects the optimal gaze tracking method based on that data. The past viewing history includes, for example, browsing history, rating history, and comment history. The eye camera unit analyzes this data to learn the user's interests. Methods for measuring interest include, for example, the number of clicks, the duration of stay, and user feedback. The eye camera unit selects the optimal gaze tracking method based on the user's interests. Criteria for the optimal tracking method include, for example, the tracking algorithm, the type of sensor, and the data collection method. This allows optimal gaze tracking to be provided based on the user's past viewing history. Some or all of the above-described processing in the eye camera unit may be performed using AI or without AI. For example, the eye camera unit can input the user's past viewing history into AI and have the AI ​​select the optimal gaze tracking method.

[0052] The eye camera unit can improve the accuracy of gaze tracking based on the user's current areas of interest. The eye camera unit, for example, collects data to identify the user's current areas of interest. Area-of-interest data includes, for example, the user's search history, browsing history, and feedback. The eye camera unit analyzes this data to identify the user's current areas of interest. Area-of-interest measurement methods include, for example, the number of clicks, time spent on a page, and user feedback. The eye camera unit improves the accuracy of gaze tracking based on the user's current areas of interest. Methods for improving accuracy include, for example, improving algorithms, improving data accuracy, and improving sensor performance. This allows for gaze tracking based on the user's current areas of interest. Some or all of the above-described processing in the eye camera unit may be performed using AI, or may be performed without AI. For example, the eye camera unit can input the user's area-of-interest data into AI and have the AI ​​improve the accuracy of gaze tracking.

[0053] The eye camera unit can select the optimal tracking method during gaze tracking, taking into account the user's geographic location information. The eye camera unit uses, for example, GPS data, IP address, location information services, etc. to collect the user's geographic location information. Methods for collecting geographic location information include, for example, smartphone location information services and IP addresses when connected to the Internet. Based on the collected geographic location information, the eye camera unit identifies gaze tracking of exhibits related to the user's current location, areas visited in the past, and areas planned to be visited in the future. This allows optimal gaze tracking to be provided based on the user's geographic location information. Some or all of the above-described processing in the eye camera unit may be performed using AI, or may be performed without AI. For example, the eye camera unit can input the user's geographic location information into AI and have the AI ​​select the optimal gaze tracking method.

[0054] The eye camera unit can analyze the user's social media activity during gaze tracking to improve tracking accuracy. The eye camera unit, for example, uses an API or database to collect the user's social media activity. Social media activity data includes, for example, post content, number of likes, number of followers, etc. The eye camera unit analyzes this data to identify the user's interests. The eye camera unit improves gaze tracking accuracy based on the user's social media activity. Methods for improving accuracy include, for example, improving algorithms, improving data accuracy, and improving sensor performance. This allows for gaze tracking based on the user's social media activity. Some or all of the above-described processing in the eye camera unit may be performed using AI or without AI. For example, the eye camera unit can input the user's social media data into AI and have the AI ​​improve the gaze tracking accuracy.

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

[0056] The generator can generate an audio guide based on the user's current interests as well as the user's past viewing history. For example, if the user has recently shown interest in a particular artist or theme, the content of the audio guide can be customized based on that information. Furthermore, the generator can incorporate the latest exhibit and event information related to the user's interests. This allows the user to always receive a personalized audio guide based on the latest information.

[0057] The VR section can prioritize the display of exhibits related to the user's geographic location. For example, if the user is in a specific area, it can prioritize the display of artworks or exhibits with historical background related to that area. Also, if the user is traveling, it can suggest exhibits related to the area they are visiting. This allows the user to have a deeper viewing experience based on their geographic location.

[0058] The eye camera unit can use the user's gaze tracking data to identify parts of an exhibit that the user is particularly interested in and provide detailed information about those parts. For example, if the user focuses their gaze on a particular part of a painting, a detailed explanation of the artist's intentions and techniques related to that part can be provided. Furthermore, based on the gaze tracking data, important parts that the user may otherwise overlook can be highlighted. This allows the user to have a more fulfilling viewing experience.

[0059] The provider can analyze the user's social media activity and incorporate information about artworks and artists that the user is interested in into the audio guide. For example, if the user frequently mentions a particular artist on social media, detailed information about that artist can be included in the audio guide. The provider can also provide information about exhibits that the user's followers and friends are interested in. This allows the user to have a personalized viewing experience based on their social network.

[0060] The providing unit may provide audio guides for exhibits related to a region by taking into consideration the user's geographical location information. For example, if the user is in a specific region, audio guides for artworks or exhibits with historical background related to that region may be provided preferentially. Also, if the user is traveling, audio guides for exhibits related to the region the user is visiting may be suggested. This allows the user to have a deeper viewing experience based on their geographical location.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit accepts the selection of an exhibit from the user. The user can select an exhibit by using a touch screen, voice input, gesture recognition, or other methods. Step 2: The generation unit uses the generation AI to analyze the information about the exhibits received by the reception unit and generate an audio guide. The generation AI analyzes the information about the exhibits using technologies such as natural language processing, image recognition, and data mining. Step 3: The providing unit provides the audio guide generated by the generating unit. The audio guide is provided in the form of, for example, an audio file, text-to-speech, an interactive guide, etc. Step 4: The VR unit allows the user to view the exhibit from all angles based on the audio guide provided by the provider. For example, the user can view the exhibit from various angles using a 360-degree camera, VR goggles, panoramic view, etc. Step 5: The eye camera unit tracks the user's gaze based on the viewpoint provided by the VR unit and provides detailed information on the part of interest. Gaze tracking is performed using various methods, such as eye tracking technology, camera type, and tracking accuracy.

[0063] (Example 2) An art appreciation system according to an embodiment of the present invention collaborates with art galleries and museums to provide and sell apps that allow users to enjoy the worldview and profound depth of art. This art appreciation system uses a generative AI, VR, and an eye camera to provide an audio guide to users. First, the user launches the app and selects an exhibit at an art galleries or museums. Next, the generative AI analyzes information about the selected exhibit and provides the user with an audio guide. The audio guide provides detailed information about the exhibit's history, background, and the artist's intentions. Furthermore, VR technology is used to enable users to appreciate the exhibit from a 360-degree perspective. For example, a user can use a smartphone or VR goggles to view the exhibit from various angles. This allows users to appreciate the exhibit in detail and gain a deeper understanding of the worldview and profound depth of art. The eye camera is also used to track the user's gaze and provide additional information about areas of interest. For example, if a user focuses their gaze on a particular area, a detailed explanation about that area is provided via an audio guide. This allows users to obtain information tailored to their interests and enjoy a more fulfilling appreciation experience. This art appreciation system can provide the latest information about exhibits by collaborating with art galleries and museums. Furthermore, by using generative AI, it is possible to provide users with personalized audio guides. This allows users to obtain information tailored to their interests and to enjoy the worldview and depth of art. In this way, the art appreciation system allows users to enjoy the worldview and depth of art.

[0064] An art appreciation system according to an embodiment includes a reception unit, a generation unit, a provision unit, a VR unit, and an eye camera unit. The reception unit accepts a selection of an exhibit from a user. The user can select an exhibit using, for example, a touchscreen, voice input, or gesture recognition. The generation unit uses a generation AI to analyze information about the exhibit accepted by the reception unit and generate an audio guide. The generation AI analyzes information about the exhibit using, for example, natural language processing, image recognition, or data mining. The provision unit provides the audio guide generated by the generation unit. The audio guide is provided in the form of, for example, an audio file, text-to-speech, or an interactive guide. The VR unit enables the user to view the exhibit from all directions based on the audio guide provided by the provision unit. The user can view the exhibit from various angles using, for example, a 360-degree camera, VR goggles, or a panoramic view. The eye camera unit tracks the user's gaze based on the viewpoint provided by the VR unit and provides detailed information about the part of the exhibit that interests them. Gaze tracking is performed using, for example, eye tracking technology, camera type, tracking accuracy, and the like. As a result, the art appreciation system according to the embodiment allows the user to enjoy the worldview and depth of art.

[0065] The generation unit can learn the user's past listening history and interests and generate a personalized audio guide. The generation unit, for example, collects the user's past listening history and generates the audio guide based on that data. The past listening history includes, for example, browsing history, rating history, and comment history. The generation unit analyzes this data and learns the user's interests. Methods for measuring interest include, for example, the number of clicks, the length of stay, and user feedback. The generation unit generates a personalized audio guide based on the user's interests. This allows the user to be provided with a personalized audio guide. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input the user's past listening history into the generation AI and have the generation AI generate a personalized audio guide.

[0066] The VR unit can enable a user to view an exhibit from multiple angles using a smartphone or VR goggles. The VR unit, for example, provides a system for a user to view an exhibit using a smartphone or VR goggles. Types of smartphones and VR goggles include, for example, specific devices and necessary applications. Using these devices, a user can view an exhibit from multiple angles, such as horizontal angles, vertical angles, and rotational angles. This allows the user to view the exhibit from a 360-degree perspective. Some or all of the above-described processing in the VR unit may be performed using AI or without AI. For example, the VR unit can input the user's device information into AI and have the AI ​​provide the optimal perspective.

[0067] The eye camera unit can track the user's gaze and provide additional information on areas of interest. The eye camera unit uses, for example, eye tracking technology to track the user's gaze. Eye tracking technology includes, for example, a specific camera type and tracking accuracy. When the user directs their gaze at a specific area, the eye camera unit provides detailed information about that area. The additional information includes, for example, text information, images, videos, links, etc. This allows the user to obtain additional information on areas of interest. Some or all of the above-described processing in the eye camera unit may be performed using AI, or may be performed without using AI. For example, the eye camera unit can input the user's gaze data into AI and have the AI ​​provide the additional information.

[0068] The providing unit can provide an audio guide that provides detailed explanations of the history, background, and artist's intentions of the exhibit. The providing unit, for example, collects information about the history, background, and artist's intentions of the exhibit and provides the audio guide based on that information. The historical content includes, for example, the year of production, past exhibition history, and changes in ownership. The background content includes, for example, the cultural background, social background, and technological background. The artist's intention content includes, for example, interviews, comments from the artist, and explanations of the work. This allows the user to obtain detailed information about the exhibit. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the collected information into AI and have the AI ​​generate the audio guide.

[0069] The generation unit can collect data related to the exhibits and generate an audio guide based on that data. The generation unit uses, for example, sensors, databases, APIs, etc. to collect data related to the exhibits. The collected data includes, for example, detailed information about the exhibits, related literature, and past exhibition history. The generation unit generates an audio guide based on this data. This allows the provision of up-to-date information about the exhibits. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the collected data into the generation AI and have the generation AI generate the audio guide.

[0070] The reception unit can estimate the user's emotions and present exhibit options based on the estimated user emotions. The reception unit uses technologies such as facial expression recognition, voice analysis, and biometrics to estimate the user's emotions. Specific methods for emotion estimation include, for example, changes in facial expression, tone of voice, and heart rate fluctuations. The reception unit presents exhibit options based on the estimated user emotions. Methods for presenting options include, for example, list display, grid display, and filtering. This allows exhibit options to be provided according to the user's emotions. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's emotion data into AI and have the AI ​​present exhibit options.

[0071] The reception unit can analyze the user's past viewing history and suggest optimal exhibit options. The reception unit, for example, collects the user's past viewing history and suggests optimal exhibit options based on that data. The past viewing history includes, for example, browsing history, rating history, comment history, etc. The reception unit analyzes this data to learn the user's interests. Methods for measuring interest include, for example, the number of clicks, length of stay, and user feedback. The reception unit suggests optimal exhibit options based on the user's interests. This makes it possible to suggest optimal exhibits based on the user's past viewing history. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's past viewing history into AI and have the AI ​​suggest optimal exhibit options.

[0072] The reception unit can filter exhibits based on the user's current areas of interest when selecting an exhibit. The reception unit, for example, collects data for identifying the user's current areas of interest. The area of ​​interest data includes, for example, the user's search history, browsing history, and feedback. The reception unit analyzes this data to identify the user's current areas of interest. Methods for measuring the area of ​​interest include, for example, the number of clicks, the length of stay, and the user's feedback. The reception unit filters the exhibit options based on the user's current areas of interest. This allows exhibits to be selected based on the user's current areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's area of ​​interest data into AI and have the AI ​​filter the exhibit options.

[0073] The reception unit can estimate the user's emotions and adjust the display order of options based on the estimated user emotions. The reception unit uses technologies such as facial expression recognition, voice analysis, and biometrics to estimate the user's emotions. Specific methods for emotion estimation include, for example, changes in facial expressions, tone of voice, and heart rate fluctuations. The reception unit adjusts the display order of the exhibit options based on the estimated user emotions. Methods for adjusting the display order include, for example, algorithms, user priorities, and filtering conditions. This allows the display order of options to be provided according to the user's emotions. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's emotion data into AI and have the AI ​​adjust the display order of options.

[0074] When selecting an exhibit, the reception unit can prioritize relevant exhibits by taking into account the user's geographical location information. The reception unit uses, for example, GPS data, IP address, location information services, etc. to collect the user's geographical location information. Methods for collecting geographical location information include, for example, smartphone location information services and IP addresses when connected to the Internet. The reception unit identifies exhibits related to the user's current location, areas the user has visited in the past, and areas the user plans to visit in the future based on the collected geographical location information. This allows the user to be provided with relevant exhibits based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit may input the user's geographical location information into AI and have the AI ​​present relevant exhibits.

[0075] When selecting an exhibit, the reception unit can analyze the user's social media activity and suggest related exhibits. The reception unit, for example, uses an API or database for collecting the user's social media activity. Social media activity data includes, for example, post content, number of likes, number of followers, etc. The reception unit analyzes this data and identifies the user's interests. The reception unit suggests related exhibits based on the user's social media activity. This makes it possible to suggest related exhibits based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into AI and have the AI ​​suggest related exhibits.

[0076] The generation unit can estimate the user's emotions and adjust the content of the audio guide based on the estimated user emotions. The generation unit uses technologies such as facial expression recognition, voice analysis, and biometrics to estimate the user's emotions. Specific methods for emotion estimation include, for example, changes in facial expressions, tone of voice, and heart rate fluctuations. The generation unit adjusts the content of the audio guide based on the estimated user emotions. Methods for content adjustment include, for example, the level of detail of information, the order of information, and the type of information. This allows the audio guide content to be provided according to the user's emotions. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the content of the audio guide.

[0077] When generating an audio guide, the generation unit can adjust the level of detail based on the importance of the exhibits. The generation unit, for example, sets criteria for evaluating the importance of the exhibits. Criteria for importance include, for example, the historical value, popularity, and expert evaluation of the exhibits. The generation unit evaluates the importance of the exhibits based on these criteria. The generation unit adjusts the level of detail of the audio guide based on the importance of the exhibits. Methods for adjusting the level of detail include, for example, the depth of information, the range of information, and the type of information. This makes it possible to provide an audio guide that corresponds to the importance of the exhibits. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input importance data of the exhibits into the generation AI and have the generation AI adjust the level of detail of the audio guide.

[0078] When generating an audio guide, the generation unit can apply different generation algorithms depending on the category of the exhibit. For example, the generation unit sets criteria for defining the category of the exhibit. The category criteria include, for example, painting, sculpture, and photography. The generation unit classifies the category of the exhibit based on these criteria. The generation unit applies different generation algorithms depending on the category of the exhibit. The generation algorithms include, for example, machine learning algorithms and rule-based algorithms. This makes it possible to provide an audio guide according to the category of the exhibit. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input exhibit category data into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0079] The generation unit can estimate the user's emotions and adjust the length of the audio guide based on the estimated user emotions. The generation unit uses technologies such as facial expression recognition, voice analysis, and biometrics to estimate the user's emotions. Specific methods for emotion estimation include, for example, changes in facial expressions, tone of voice, and heart rate fluctuations. The generation unit adjusts the length of the audio guide based on the estimated user emotions. Methods for adjusting the length include, for example, the level of detail of the information, the user's interests, and time constraints. This allows the audio guide length to be provided according to the user's emotions. Some or all of the above-described processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the audio guide.

[0080] When generating an audio guide, the generation unit can determine the priority of content based on the exhibition period of the exhibits. The generation unit, for example, sets criteria for evaluating the exhibition period of the exhibits. The exhibition period criteria include, for example, past exhibition history, current exhibition period, and future exhibition schedule. The generation unit evaluates the exhibition period of the exhibits based on these criteria. The generation unit determines the priority of the audio guide content based on the exhibition period of the exhibits. Methods for determining the priority include, for example, the importance of the exhibits, the user's interests, and the exhibition period. This makes it possible to provide an audio guide that is appropriate for the exhibition period of the exhibits. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input exhibition period data of the exhibits into the generation AI and have the generation AI determine the priority of the audio guide content.

[0081] When generating an audio guide, the generation unit can adjust the order of the content based on the relevance of the exhibits. The generation unit, for example, sets criteria for evaluating the relevance of the exhibits. Relevance criteria include, for example, thematic similarity, technical relevance, historical relevance, etc. The generation unit evaluates the relevance of the exhibits based on these criteria. The generation unit adjusts the order of the audio guide content based on the relevance of the exhibits. Methods for adjusting the order include, for example, algorithms, user priorities, filtering conditions, etc. This makes it possible to provide an audio guide that is tailored to the relevance of the exhibits. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input relevance data of the exhibits into the generation AI and have the generation AI adjust the order of the audio guide content.

[0082] The providing unit can estimate the user's emotions and adjust the audio guide provision method based on the estimated user's emotions. The providing unit uses technologies such as facial expression recognition, voice analysis, and biometrics to estimate the user's emotions. Specific methods for emotion estimation include, for example, changes in facial expressions, tone of voice, and heart rate fluctuations. The providing unit adjusts the audio guide provision method based on the estimated user's emotions. Methods for adjusting the provision method include, for example, audio guide, text guide, and interactive guide. This makes it possible to provide an audio guide provision method that corresponds to the user's emotions. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input user emotion data into AI and have the AI ​​adjust the audio guide provision method.

[0083] When providing an audio guide, the providing unit can select the optimal delivery method by referring to the user's past listening history. The providing unit, for example, collects the user's past listening history and selects the optimal audio guide delivery method based on that data. The past listening history includes, for example, browsing history, rating history, comment history, etc. The providing unit analyzes this data to learn the user's interests. Methods for measuring interest include, for example, the number of clicks, the duration of stay, and user feedback. The providing unit selects the optimal audio guide delivery method based on the user's interests. Criteria for determining the optimal delivery method include, for example, the user's preferences, the type of device, and the type of information. This allows the optimal audio guide to be provided based on the user's past listening history. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input the user's past listening history into AI and have the AI ​​select the optimal audio guide delivery method.

[0084] The providing unit can customize the content of the audio guide based on the user's current areas of interest when providing the audio guide. The providing unit, for example, collects data for identifying the user's current areas of interest. The area of ​​interest data includes, for example, the user's search history, browsing history, and feedback. The providing unit analyzes this data to identify the user's current areas of interest. Methods for measuring the area of ​​interest include, for example, the number of clicks, the length of stay, and the user's feedback. The providing unit customizes the content of the audio guide based on the user's current areas of interest. Methods for content customization include, for example, the level of detail of information, the order of information, and the type of information. This makes it possible to provide an audio guide based on the user's current areas of interest. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input the user's area of ​​interest data into AI and have the AI ​​customize the content of the audio guide.

[0085] The providing unit can estimate the user's emotions and adjust the order in which the audio guide is presented based on the estimated user's emotions. The providing unit uses technologies such as facial expression recognition, voice analysis, and biometrics to estimate the user's emotions. Specific methods for estimating emotions include, for example, changes in facial expressions, tone of voice, and fluctuations in heart rate. The providing unit adjusts the order in which the audio guide is presented based on the estimated user's emotions. Methods for adjusting the order in which the audio guide is presented include, for example, algorithms, user priorities, and filtering conditions. This allows the audio guide to be presented in an order that matches the user's emotions. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input user emotion data into AI and have the AI ​​adjust the order in which the audio guide is presented.

[0086] When providing an audio guide, the providing unit can select the optimal delivery method taking into account the user's geographical location information. The providing unit uses, for example, GPS data, IP address, location information services, etc. to collect the user's geographical location information. Methods for collecting geographical location information include, for example, smartphone location information services and IP addresses when connected to the Internet. The providing unit identifies audio guides for exhibits related to the user's current location, areas the user has visited in the past, and areas the user plans to visit in the future based on the collected geographical location information. This allows the optimal audio guide to be provided based on the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input the user's geographical location information into AI and have the AI ​​select the optimal audio guide delivery method.

[0087] When providing an audio guide, the providing unit can analyze the user's social media activity and customize the content. The providing unit, for example, uses an API or database to collect the user's social media activity. Social media activity data includes, for example, post content, number of likes, number of followers, etc. The providing unit analyzes this data to identify the user's interests. The providing unit customizes the content of the audio guide based on the user's social media activity. Methods for content customization include, for example, the level of detail of information, the order of information, and the type of information. This makes it possible to provide an audio guide based on the user's social media activity. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input the user's social media data into AI and have the AI ​​customize the content of the audio guide.

[0088] The VR unit can estimate the user's emotions and adjust the display method of the VR viewpoint based on the estimated user's emotions. The VR unit uses technologies such as facial expression recognition, voice analysis, and biometrics to estimate the user's emotions. Specific methods for emotion estimation include, for example, changes in facial expressions, tone of voice, and fluctuations in heart rate. The VR unit adjusts the display method of the VR viewpoint based on the estimated user's emotions. Methods for adjusting the display method include, for example, a method for switching viewpoints, a display order, and a level of detail of the display. This makes it possible to provide a display method of the VR viewpoint that corresponds to the user's emotions. Some or all of the above-described processing in the VR unit may be performed using AI, or may be performed without using AI. For example, the VR unit can input user's emotion data into AI and have the AI ​​adjust the display method of the VR viewpoint.

[0089] When providing a VR viewpoint, the VR unit can select the optimal viewpoint by referring to the user's past viewing history. For example, the VR unit collects the user's past viewing history and selects the optimal VR viewpoint based on that data. The past viewing history includes, for example, browsing history, rating history, and comment history. The VR unit analyzes this data to learn the user's interests. Methods for measuring interest include, for example, the number of clicks, time spent, and user feedback. The VR unit selects the optimal VR viewpoint based on the user's interests. Criteria for the optimal viewpoint include, for example, the user's preferences, exhibit characteristics, and viewpoint type. This allows the optimal VR viewpoint to be provided based on the user's past viewing history. Some or all of the above-described processing in the VR unit may be performed using AI, or may be performed without AI. For example, the VR unit can input the user's past viewing history into AI and have the AI ​​select the optimal VR viewpoint.

[0090] When providing a VR viewpoint, the VR unit can customize the viewpoint based on the user's current area of ​​interest. The VR unit, for example, collects data to identify the user's current area of ​​interest. The area of ​​interest data includes, for example, the user's search history, browsing history, and feedback. The VR unit analyzes this data to identify the user's current area of ​​interest. Methods for measuring the area of ​​interest include, for example, the number of clicks, the duration of stay, and the user's feedback. The VR unit customizes the VR viewpoint based on the user's current area of ​​interest. Methods for customizing the viewpoint include, for example, the level of detail of the viewpoint, the order of the viewpoints, and the type of viewpoint. This makes it possible to provide a VR viewpoint based on the user's current area of ​​interest. Some or all of the above-described processing in the VR unit may be performed using AI or without AI. For example, the VR unit may input the user's area of ​​interest data into AI and have the AI ​​customize the VR viewpoint.

[0091] The VR unit can estimate the user's emotions and adjust the display order of the VR viewpoints based on the estimated user's emotions. The VR unit uses technologies such as facial expression recognition, voice analysis, and biometrics to estimate the user's emotions. Specific methods for emotion estimation include, for example, changes in facial expressions, tone of voice, and heart rate fluctuations. The VR unit adjusts the display order of the VR viewpoints based on the estimated user's emotions. Methods for adjusting the display order include, for example, algorithms, user priorities, and filtering conditions. This allows the display order of the VR viewpoints to be provided according to the user's emotions. Some or all of the above-described processing in the VR unit may be performed using AI, or may be performed without using AI. For example, the VR unit can input user emotion data into AI and have the AI ​​adjust the display order of the VR viewpoints.

[0092] When providing a VR viewpoint, the VR unit can select an optimal viewpoint by taking into account the user's geographical location information. The VR unit uses, for example, GPS data, an IP address, a location information service, etc. to collect the user's geographical location information. Methods for collecting geographical location information include, for example, a smartphone's location information service and an IP address when connected to the Internet. Based on the collected geographical location information, the VR unit identifies viewpoints of exhibits related to the user's current location, areas visited in the past, and areas planned to be visited in the future. This allows the optimal VR viewpoint to be provided based on the user's geographical location information. Some or all of the above-described processing in the VR unit may be performed using AI or without AI. For example, the VR unit can input the user's geographical location information into AI and have the AI ​​select an optimal VR viewpoint.

[0093] When providing a VR viewpoint, the VR unit can customize the viewpoint by analyzing the user's social media activity. The VR unit, for example, uses an API or database to collect the user's social media activity. Social media activity data includes, for example, post content, number of likes, number of followers, etc. The VR unit analyzes this data to identify the user's interests. The VR unit customizes the VR viewpoint based on the user's social media activity. Methods for viewpoint customization include, for example, the level of detail of the viewpoint, the order of the viewpoints, and the type of viewpoint. This allows the VR viewpoint to be provided based on the user's social media activity. Some or all of the above-described processing in the VR unit may be performed using AI, or may be performed without using AI. For example, the VR unit can input the user's social media data into AI and have the AI ​​customize the VR viewpoint.

[0094] The eye camera unit can estimate the user's emotions and adjust the accuracy of gaze tracking based on the estimated user's emotions. The eye camera unit uses technologies such as facial expression recognition, voice analysis, and biometrics to estimate the user's emotions. Specific methods for emotion estimation include, for example, changes in facial expressions, tone of voice, and heart rate fluctuations. The eye camera unit adjusts the accuracy of gaze tracking based on the estimated user's emotions. Methods for adjusting the accuracy include, for example, tracking accuracy, analysis accuracy, and display accuracy. This allows for providing gaze tracking accuracy that matches the user's emotions. Some or all of the above-described processing in the eye camera unit may be performed using AI, or may be performed without AI. For example, the eye camera unit can input user emotion data into AI and have the AI ​​adjust the accuracy of gaze tracking.

[0095] The eye camera unit can select the optimal tracking method by referring to the user's past viewing history when tracking gaze. For example, the eye camera unit collects the user's past viewing history and selects the optimal gaze tracking method based on that data. The past viewing history includes, for example, browsing history, rating history, and comment history. The eye camera unit analyzes this data to learn the user's interests. Methods for measuring interest include, for example, the number of clicks, the duration of stay, and user feedback. The eye camera unit selects the optimal gaze tracking method based on the user's interests. Criteria for the optimal tracking method include, for example, the tracking algorithm, the type of sensor, and the data collection method. This allows optimal gaze tracking to be provided based on the user's past viewing history. Some or all of the above-described processing in the eye camera unit may be performed using AI or without AI. For example, the eye camera unit can input the user's past viewing history into AI and have the AI ​​select the optimal gaze tracking method.

[0096] The eye camera unit can improve the accuracy of gaze tracking based on the user's current areas of interest. The eye camera unit, for example, collects data to identify the user's current areas of interest. Area-of-interest data includes, for example, the user's search history, browsing history, and feedback. The eye camera unit analyzes this data to identify the user's current areas of interest. Area-of-interest measurement methods include, for example, the number of clicks, time spent on a page, and user feedback. The eye camera unit improves the accuracy of gaze tracking based on the user's current areas of interest. Methods for improving accuracy include, for example, improving algorithms, improving data accuracy, and improving sensor performance. This allows for gaze tracking based on the user's current areas of interest. Some or all of the above-described processing in the eye camera unit may be performed using AI, or may be performed without AI. For example, the eye camera unit can input the user's area-of-interest data into AI and have the AI ​​improve the accuracy of gaze tracking.

[0097] The eye camera unit can estimate the user's emotion and adjust the display order of the gaze tracking results based on the estimated user emotion. The eye camera unit uses technologies such as facial expression recognition, voice analysis, and biometrics to estimate the user's emotion. Specific methods for emotion estimation include, for example, changes in facial expression, tone of voice, and heart rate fluctuations. The eye camera unit adjusts the display order of the gaze tracking results based on the estimated user emotion. Methods for adjusting the display order include, for example, algorithms, user priorities, and filtering conditions. This allows for providing gaze tracking results that correspond to the user's emotion. Some or all of the above-described processing in the eye camera unit may be performed using AI, or may be performed without AI. For example, the eye camera unit can input user emotion data into AI and have the AI ​​adjust the display order of the gaze tracking results.

[0098] The eye camera unit can select the optimal tracking method during gaze tracking, taking into account the user's geographic location information. The eye camera unit uses, for example, GPS data, IP address, location information services, etc. to collect the user's geographic location information. Methods for collecting geographic location information include, for example, smartphone location information services and IP addresses when connected to the Internet. Based on the collected geographic location information, the eye camera unit identifies gaze tracking of exhibits related to the user's current location, areas visited in the past, and areas planned to be visited in the future. This allows optimal gaze tracking to be provided based on the user's geographic location information. Some or all of the above-described processing in the eye camera unit may be performed using AI, or may be performed without AI. For example, the eye camera unit can input the user's geographic location information into AI and have the AI ​​select the optimal gaze tracking method.

[0099] The eye camera unit can analyze the user's social media activity during gaze tracking to improve tracking accuracy. The eye camera unit, for example, uses an API or database to collect the user's social media activity. Social media activity data includes, for example, post content, number of likes, number of followers, etc. The eye camera unit analyzes this data to identify the user's interests. The eye camera unit improves gaze tracking accuracy based on the user's social media activity. Methods for improving accuracy include, for example, improving algorithms, improving data accuracy, and improving sensor performance. This allows for gaze tracking based on the user's social media activity. Some or all of the above-described processing in the eye camera unit may be performed using AI or without AI. For example, the eye camera unit can input the user's social media data into AI and have the AI ​​improve the gaze tracking accuracy. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, generation unit, provision unit, VR unit, and eye camera unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized using the touch screen or voice input device of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes information about the exhibits using a generation AI. The provision unit provides an audio guide using the output device 40 of the smart device 14. The VR unit enables the exhibits to be viewed from all directions using the display 40A of the smart device 14 and VR goggles. The eye camera unit tracks the user's gaze using the camera 42 of the smart device 14 and provides detailed information about parts of interest. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, VR unit, and eye camera unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized using a voice input device or a gesture recognition device of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes information about the exhibits using a generation AI. The provision unit provides an audio guide using the speaker 240 of the smart glasses 214. The VR unit enables the exhibits to be viewed from all directions using the display of the smart glasses 214. The eye camera unit tracks the user's gaze using the camera 42 of the smart glasses 214 and provides detailed information about parts of interest. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, VR unit, and eye camera unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized using a voice input device or a gesture recognition device of the headset terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes information about the exhibits using a generation AI. The provision unit provides an audio guide using the speaker 240 of the headset terminal 314. The VR unit enables the exhibits to be viewed from all directions using the display 343 of the headset terminal 314. The eye camera unit tracks the user's line of sight using the camera 42 of the headset terminal 314 and provides detailed information about parts of interest. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, generation unit, provision unit, VR unit, and eye camera unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized using a voice input device or a gesture recognition device of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes information about the exhibits using a generation AI. The provision unit provides an audio guide using the speaker 240 of the robot 414. The VR unit enables the exhibits to be viewed from all directions using the display of the robot 414. The eye camera unit tracks the user's line of sight using the camera 42 of the robot 414 and provides detailed information about parts of interest.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The art appreciation system may further include an emotion customization unit that estimates the user's emotion and customizes the exhibit selection options based on the estimated emotion. The emotion customization unit estimates the user's emotion from the user's facial expression and voice, and can, for example, suggest more stimulating exhibits if the user is excited, or suggest calm exhibits if the user is relaxed. Furthermore, the emotion customization unit can track changes in the user's emotion in real time and dynamically adjust the exhibit selection options. This allows the user to have an optimal appreciation experience that suits their emotion.

[0102] The generator can generate an audio guide based on the user's current interests as well as the user's past viewing history. For example, if the user has recently shown interest in a particular artist or theme, the content of the audio guide can be customized based on that information. Furthermore, the generator can incorporate the latest exhibit and event information related to the user's interests. This allows the user to always receive a personalized audio guide based on the latest information.

[0103] The VR section can prioritize the display of exhibits related to the user's geographic location. For example, if the user is in a specific area, it can prioritize the display of artworks or exhibits with historical background related to that area. Also, if the user is traveling, it can suggest exhibits related to the area they are visiting. This allows the user to have a deeper viewing experience based on their geographic location.

[0104] The eye camera unit can use the user's gaze tracking data to identify parts of an exhibit that the user is particularly interested in and provide detailed information about those parts. For example, if the user focuses their gaze on a particular part of a painting, a detailed explanation of the artist's intentions and techniques related to that part can be provided. Furthermore, based on the gaze tracking data, important parts that the user may otherwise overlook can be highlighted. This allows the user to have a more fulfilling viewing experience.

[0105] The provider can analyze the user's social media activity and incorporate information about artworks and artists that the user is interested in into the audio guide. For example, if the user frequently mentions a particular artist on social media, detailed information about that artist can be included in the audio guide. The provider can also provide information about exhibits that the user's followers and friends are interested in. This allows the user to have a personalized viewing experience based on their social network.

[0106] The generation unit can estimate the user's emotions and adjust the tone and tempo of the audio guide based on the estimated emotions. For example, if the user is excited, the tone of the audio guide can be brightened and the tempo can be increased. On the other hand, if the user is relaxed, the tone can be calmed and the tempo can be decreased. Furthermore, the generation unit can dynamically adjust the content of the audio guide according to changes in the user's emotions. This allows the user to receive the optimal audio guide that matches their emotions.

[0107] The reception unit can suggest exhibit options based on the user's past viewing history as well as the user's current mood and physical condition. For example, if the user is tired, it can suggest relaxing exhibits. On the other hand, if the user is energetic, it can suggest stimulating exhibits. Furthermore, the reception unit can track changes in the user's mood and physical condition in real time and dynamically adjust the exhibit options. This allows the user to have an optimal viewing experience that suits their mood and physical condition.

[0108] The VR unit can estimate the user's emotions and adjust the way the VR viewpoint is displayed based on the estimated emotions. For example, if the user is excited, the viewpoint can be switched quickly to provide a more dynamic viewpoint. On the other hand, if the user is relaxed, the viewpoint can be switched slowly to provide a calmer viewpoint. Furthermore, the VR unit can dynamically adjust the way the viewpoint is displayed in response to changes in the user's emotions. This allows the user to experience the optimal VR viewpoint that matches their emotions.

[0109] The eye camera unit can estimate the user's emotions and adjust the accuracy of gaze tracking based on the estimated emotions. For example, if the user is excited, the accuracy of gaze tracking can be increased to provide more detailed information. On the other hand, if the user is relaxed, the accuracy of gaze tracking can be relaxed to provide overall information. Furthermore, the eye camera unit can dynamically adjust the accuracy of gaze tracking according to changes in the user's emotions. This allows the user to have an optimal gaze tracking experience according to their emotions.

[0110] The providing unit may provide audio guides for exhibits related to a region by taking into consideration the user's geographical location information. For example, if the user is in a specific region, audio guides for artworks or exhibits with historical background related to that region may be provided preferentially. Also, if the user is traveling, audio guides for exhibits related to the region the user is visiting may be suggested. This allows the user to have a deeper viewing experience based on their geographical location.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The reception unit accepts the selection of an exhibit from the user. The user can select an exhibit by using a touch screen, voice input, gesture recognition, or other methods. Step 2: The generation unit uses the generation AI to analyze the information about the exhibits received by the reception unit and generate an audio guide. The generation AI analyzes the information about the exhibits using technologies such as natural language processing, image recognition, and data mining. Step 3: The providing unit provides the audio guide generated by the generating unit. The audio guide is provided in the form of, for example, an audio file, text-to-speech, an interactive guide, etc. Step 4: The VR unit allows the user to view the exhibit from all angles based on the audio guide provided by the provider. For example, the user can view the exhibit from various angles using a 360-degree camera, VR goggles, panoramic view, etc. Step 5: The eye camera unit tracks the user's gaze based on the viewpoint provided by the VR unit and provides detailed information on the part of interest. Gaze tracking is performed using various methods, such as eye tracking technology, camera type, and tracking accuracy.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The 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.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] 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.

[0145] 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.

[0146] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] 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.

[0162] 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.

[0163] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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, in order to avoid confusion and to 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives a selection of an exhibit from a user; a generation unit that analyzes information about the exhibits received by the reception unit and generates an audio guide; a providing unit that provides the audio guide generated by the generating unit; a VR unit that enables the user to view exhibits from all directions based on the audio guide provided by the providing unit; and an eye camera unit that tracks the user's line of sight based on the viewpoint provided by the VR unit and provides detailed information on the part of interest. A system characterized by:

2. The generation unit Generate personalized audio guides based on users' past viewing history and interests 2. The system of claim 1.

3. The VR unit is Users can view exhibits from multiple angles using smartphones or VR goggles.

2. The system of claim 1.

4. The eye camera unit Tracks the user's gaze and provides additional information on areas of interest 2. The system of claim 1.

5. The providing unit Providing an audio guide detailing the exhibit's history, background, and artist intent 2. The system of claim 1.

6. The generation unit Collect data about the exhibits and generate audio guides based on that data 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and present a selection of exhibits based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzing the user's past viewing history and suggesting the most suitable exhibit options 2. The system of claim 1.

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