system

Abstract

The system according to this embodiment aims to effectively convey the appeal of tourist destinations, transcending geographical and temporal constraints. [Solution] The system according to the embodiment comprises a list providing unit, a generation unit, a streaming unit, and a providing unit. The list providing unit provides a list of tourist destinations. The generation unit generates VR content for tourist destinations selected from the list provided by the list providing unit. The streaming unit streams the VR content generated by the generation unit. The providing unit provides the VR content streamed by the streaming unit to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that there is a lack of effective countermeasures against the layer that cannot travel even if they want to, and the decrease in the number of tourist visits.

[0005] The system according to the embodiment aims to effectively convey the charm of tourist destinations beyond distance and time constraints.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a list providing unit, a generation unit, a streaming unit, and a providing unit. The list providing unit provides a list of tourist destinations. The generation unit generates VR content for tourist destinations selected from the list provided by the list providing unit. The streaming unit streams the VR content generated by the generation unit. The providing unit provides the VR content streamed by the streaming unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can effectively convey the appeal of tourist destinations, transcending geographical and temporal constraints. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The travel / tourism experience system according to an embodiment of the present invention is a system that provides travel / tourism experiences using VR technology to people who cannot travel or go sightseeing, and to tourist destinations suffering from a decline in tourists. This system allows users to wear a VR device and select their desired tourist destination, providing VR content for the selected destination, enabling users to experience it as if they were actually there. This system makes it possible to effectively reach a wide range of targets, overcoming geographical and temporal constraints. Furthermore, because it can convey the appeal of tourist destinations at any time, regardless of the actual tourist season or weather, it can significantly improve the efficiency of tourism promotion. For example, a user wears a VR device and selects their desired tourist destination. At this time, the user can access the system and choose a place they want to go from a list of provided tourist destinations. For example, if a user selects "the Eiffel Tower in Paris," VR content for that tourist destination is provided. Next, the system provides VR content for the selected tourist destination. The system streams high-quality VR video of the selected tourist destination to the user. For example, it provides video that allows users to view the surroundings of the Eiffel Tower in 360 degrees, or video that allows them to explore the inside of the tower. This allows users to experience it as if they were actually there. Furthermore, this system can effectively reach a wide range of targets, transcending geographical and temporal constraints. For example, people who find it difficult to take vacations due to work or financial reasons, or those who cannot travel due to a combination of factors, can easily experience tourist destinations using this system. In addition, because it can convey the appeal of tourist destinations at any time, regardless of the actual tourist season or weather, it can greatly improve the efficiency of tourism promotion. For example, it can convey the appeal of tourist destinations even outside of the tourist season, and is expected to increase the number of visitors. In this way, travel / tourism experience systems using VR technology can effectively convey the appeal of tourist destinations and improve the efficiency of tourism promotion.

[0029] The travel / tourism experience system according to the embodiment comprises a list provision unit, a generation unit, a streaming unit, and a provision unit. The list provision unit provides a list of tourist destinations. The list provision unit provides a list that includes information such as the name, location, and characteristics of the tourist destinations. The list provision unit displays the list of tourist destinations to the user, allowing the user to select the tourist destination they wish to visit. The generation unit generates VR content for the tourist destination selected from the list provided by the list provision unit. The generation unit generates, for example, high-quality VR video of the selected tourist destination. The generation unit generates the video of the tourist destination in a 360-degree viewable format, providing the user with an experience as if they were actually there. The generation unit generates the video of the tourist destination in high resolution, providing the user with a realistic experience. The streaming unit streams the VR content generated by the generation unit. The streaming unit streams, for example, the generated VR video to the user in real time. The streaming unit adjusts the streaming quality according to the user's network conditions to provide an uninterrupted viewing experience. The streaming unit selects the optimal streaming method based on the user's device information and provides a viewing experience optimized for each device. The provisioning unit provides the user with VR content streamed by the streaming unit. The provisioning unit provides, for example, an interface for the user to wear a VR device and select their desired tourist destination. The provisioning unit provides the user with an experience as if they were actually there, effectively conveying the appeal of the tourist destination. The provisioning unit estimates the user's emotions and adjusts the display method of the interface based on the estimated user emotions. As a result, the travel / tourism experience system according to the embodiment can consistently perform everything from providing a list of tourist destinations to generating, streaming, and providing VR content.

[0030] The list provider provides lists of tourist destinations. These lists include information such as the name, location, and characteristics of each destination. Specifically, the list provider retrieves detailed information about tourist destinations from a database and displays it to the user in a visually easy-to-understand format. This includes not only the name and location of the destination, but also its historical background, cultural significance, major tourist attractions, access methods, and information on nearby accommodations and restaurants. Furthermore, the list provider can provide individually customized lists based on the user's past search history and interests. For example, if a user has previously searched for natural landscapes, the list will prioritize listing tourist destinations with abundant natural scenery. The list provider also has a function to suggest the most suitable tourist destinations based on the user's current location and travel purpose. For example, if a user is on a business trip, the list provider will suggest tourist destinations that can be visited in a short time or those near business hotels. This allows the list provider to provide flexible lists tailored to user needs, making it easy for users to select their desired tourist destinations. Additionally, the list provider updates and provides users with the latest information on tourist destinations and events in real time. This allows users to always select tourist destinations based on the latest information.

[0031] The generation unit generates VR content for tourist destinations selected from a list provided by the list provider unit. For example, the generation unit generates high-quality VR footage of the selected tourist destinations. Specifically, the generation unit creates a 3D model of the tourist destination and uses this to generate 360-degree panoramic VR footage. The 3D model of the tourist destination is created using high-resolution photos and videos taken by drones and ground cameras. This allows users to experience the tourist destinations in detail and with realism. Furthermore, the generation unit simultaneously generates audio guides and background sounds for the tourist destinations, providing users with an immersive experience. For example, it provides audio guides explaining the history and culture of the tourist destination, and reproduces the unique natural sounds and urban noise of the location in the background. This allows users to experience the tourist destinations not only visually but also aurally. The generation unit generates VR content at the optimal resolution and frame rate according to the user's device, providing a smooth viewing experience. For example, it provides 4K resolution footage to users using high-performance VR headsets, and footage adjusted to an appropriate resolution to users using smartphones. This allows the generation unit to create high-quality VR content optimized for the user's device, providing a realistic sightseeing experience.

[0032] The streaming unit streams VR content generated by the generation unit. For example, the streaming unit streams generated VR video to the user in real time. Specifically, the streaming unit dynamically adjusts the streaming quality according to the user's network conditions to provide an uninterrupted viewing experience. For example, if the user has a high-speed internet connection, it streams high-resolution video; if the connection is slow, it automatically lowers the resolution to minimize buffering. The streaming unit also selects the optimal streaming method based on the user's device information, providing an optimized viewing experience for each device. For example, it provides low-latency, high-frame-rate streaming to users using VR headsets, and streaming that conserves battery power to users using smartphones. Furthermore, the streaming unit has a function to recommend VR content of relevant tourist destinations based on the user's viewing history and preferences. This allows users to discover new tourist destinations and enjoy a series of VR experiences. The streaming unit utilizes a cloud-based, scalable infrastructure to provide stable streaming even when multiple users access it simultaneously. This enables the streaming unit to provide users with a high-quality, uninterrupted VR content viewing experience.

[0033] The service provider delivers VR content streamed by the streaming service provider to users. For example, the service provider provides an interface for users to put on a VR device and select their desired tourist destination. Specifically, the service provider designs an intuitive user interface that makes it easy for users to select a tourist destination and start viewing. For example, users can use the VR device's controller to scroll through a list of tourist destinations and select one that interests them. The service provider also puts effort into the interface design and usability to provide users with an experience that makes them feel as if they are actually there and to effectively convey the appeal of the tourist destination. For example, before the video of the tourist destination starts, a brief introduction and highlights of the place are displayed so that users can obtain information in advance. The service provider also estimates the user's emotions and adjusts the way the interface is displayed based on the estimated user emotions. For example, if the user is excited, more dynamic visual effects are added, and if they are relaxed, calmer visual effects are provided. In this way, the service provider can provide an optimal viewing experience that matches the user's emotions and maximize the appeal of the tourist destination. Furthermore, the service provider collects user feedback and uses it to improve the interface and content. This allows the service provider to consistently deliver the optimal experience tailored to the user's needs.

[0034] The service provider provides an interface for users to wear a VR device and select their desired tourist destination. For example, the service provider provides an interface that allows users to wear a VR device, access the system, and select a place they want to go from a list of tourist destinations. The service provider provides an intuitive interface that makes it easy for users to select tourist destinations. The service provider can also estimate the user's emotions and adjust the way the interface is displayed based on the estimated emotions. This enables intuitive operation by providing an interface for users to select tourist destinations through a VR device.

[0035] The generation unit generates high-quality VR footage of selected tourist destinations. For example, the generation unit generates high-resolution VR footage of selected tourist destinations, providing users with a realistic travel experience. The generation unit generates footage of tourist destinations in a 360-degree viewable format, providing users with an experience as if they were actually there. The generation unit generates high-resolution footage of tourist destinations, providing users with a realistic experience. The generation unit can also highlight key elements of tourist destinations when generating footage. This allows for the generation of high-quality VR footage, providing users with a realistic travel experience.

[0036] The streaming unit streams the generated VR video to the user. For example, the streaming unit streams the generated VR video to the user in real time. The streaming unit adjusts the streaming quality according to the user's network conditions to provide an uninterrupted viewing experience. The streaming unit selects the optimal streaming method based on the user's device information to provide an optimized viewing experience for each device. The streaming unit can also apply different streaming technologies depending on the characteristics of the tourist destination. This allows users to enjoy a real-time tourist experience by streaming the generated VR video.

[0037] The service provider offers users an experience that makes them feel as if they are actually there. For example, the service provider provides users with an experience that makes them feel as if they are actually there by having them wear a VR device and view VR content of a selected tourist destination. The service provider provides high-resolution video of the tourist destination to give users a realistic experience. The service provider provides video of the tourist destination in a 360-degree viewable format to give users an experience that makes them feel as if they are actually there. The service provider can also estimate the user's emotions and adjust the interface display method based on the estimated user emotions. This allows the service provider to effectively convey the appeal of the tourist destination by providing users with an experience that makes them feel as if they are actually there.

[0038] The list provider analyzes the user's past selection history and provides an optimal list of tourist destinations. For example, the list provider analyzes the trends of tourist destinations the user has previously selected and displays similar destinations in the list. Based on the user's ratings of tourist destinations they have previously visited, the list provider displays highly-rated tourist destinations in the list. Based on the categories of tourist destinations the user has previously selected, the list provider displays tourist destinations in the same category in the list. The list provider may or may not use generative AI to analyze the user's past selection history. By analyzing past selection history, it is possible to provide the user with an optimal list of tourist destinations.

[0039] The list provider filters the list based on the user's current interests when providing it. For example, the list provider displays tourist destinations based on themes the user is currently interested in. The list provider displays tourist destinations based on keywords the user has recently searched for. The list provider displays tourist destinations related to events the user is currently participating in. The list provider may or may not use generative AI to identify the user's current interests. This allows for the suggestion of more relevant tourist destinations by filtering the list based on the user's current interests.

[0040] The list provider prioritizes displaying highly relevant tourist destinations based on the user's geographical location information when providing a list. For example, the list provider displays tourist destinations close to the user's current location at the top of the list. The list provider displays tourist destinations easily accessible from the user's current location at the top of the list. The list provider displays tourist destinations related to the user's current location at the top of the list. The list provider may or may not use generative AI to obtain the user's geographical location information. By prioritizing the display of highly relevant tourist destinations based on the user's geographical location information, it is possible to suggest tourist destinations that are easily accessible to the user.

[0041] The list provider analyzes the user's social media activity when providing a list and adds relevant tourist destinations to the list. For example, the list provider displays tourist destinations that the user has "liked" on social media. The list provider displays tourist destinations that the user follows on social media. The list provider displays tourist destinations that the user has shared on social media. The list provider may or may not use generative AI to analyze the user's social media activity. By analyzing the user's social media activity, it is possible to suggest tourist destinations based on the user's interests.

[0042] The generation unit adjusts the level of detail of the VR content based on key elements of the tourist destination during generation. For example, the generation unit generates detailed VR content that emphasizes the historical background of the tourist destination. The generation unit generates detailed VR content that emphasizes the natural scenery of the tourist destination. The generation unit generates detailed VR content that emphasizes the cultural elements of the tourist destination. The generation unit may use or may not use generation AI to identify key elements of the tourist destination. This allows information important to the user to be highlighted by adjusting the level of detail of the VR content based on key elements of the tourist destination.

[0043] The generation unit applies different generation algorithms depending on the category of the tourist destination during generation. For example, for tourist destinations with natural landscapes, the generation unit applies a generation algorithm that emphasizes the beauty of the scenery. For tourist destinations with historical buildings, the generation unit applies a generation algorithm that emphasizes the details of the buildings. For tourist destinations with many activities, the generation unit applies a generation algorithm that emphasizes the movement of the activities. The generation unit may or may not use a generation AI to apply different generation algorithms depending on the category of the tourist destination. This makes it possible to generate VR content that is optimal for each tourist destination by applying different generation algorithms depending on the category of the tourist destination.

[0044] The generation unit determines the priority of VR content based on the season and time of day of the tourist destination during generation. For example, the generation unit generates VR content that emphasizes seasonal highlights. The generation unit generates VR content that emphasizes the atmosphere of different times of day. The generation unit generates VR content that includes event information appropriate to the season and time of day. The generation unit may or may not use a generation AI to determine the priority of VR content based on the season and time of day of the tourist destination. By prioritizing VR content based on the season and time of day of the tourist destination, it is possible to provide the optimal timing for the tourist experience.

[0045] The generation unit adjusts the order of VR content based on the relevance of tourist destinations during generation. For example, the generation unit generates VR content that displays the main attractions of a tourist destination first. The generation unit generates VR content that is displayed based on the historical order of tourist destinations. The generation unit generates VR content that is displayed based on the geographical order of tourist destinations. The generation unit may use or may not use generation AI to adjust the order of VR content based on the relevance of tourist destinations. This allows users to experience tourist destinations in the optimal order by adjusting the order of VR content based on the relevance of tourist destinations.

[0046] The streaming unit optimizes the streaming method based on the user's network conditions during streaming. For example, the streaming unit adjusts the video resolution according to the user's network speed. The streaming unit adjusts buffering according to the stability of the user's network. The streaming unit adjusts the streaming bitrate according to the user's network usage. The streaming unit may or may not use generative AI to evaluate the user's network conditions. By optimizing the streaming method based on the user's network conditions, an uninterrupted viewing experience can be provided.

[0047] The streaming unit applies different streaming technologies depending on the characteristics of the tourist destination during streaming. For example, the streaming unit streams high-resolution video to tourist destinations with natural landscapes. The streaming unit streams detailed video to tourist destinations with historical buildings. The streaming unit streams video with a lot of movement to tourist destinations with many activities. The streaming unit may or may not use generative AI to apply different streaming technologies depending on the characteristics of the tourist destination. This allows for the application of the optimal streaming technology according to the characteristics of the tourist destination, thereby providing users with the best possible viewing experience.

[0048] The streaming unit selects the optimal streaming method during streaming, taking into account the user's device information. For example, if the user is using a smartphone, the streaming unit provides a streaming method that matches the screen size. If the user is using a tablet, the streaming unit provides a streaming method optimized for a larger screen. If the user is using a VR headset, the streaming unit streams 360-degree video. The streaming unit may or may not use generative AI to obtain the user's device information. This allows for the selection of the optimal streaming method based on the user's device information, thereby providing a viewing experience optimized for each device.

[0049] The streaming unit improves the accuracy of streaming based on relevant literature on the tourist destination during streaming. For example, the streaming unit streams detailed footage by referring to the historical background of the tourist destination. The streaming unit streams footage that emphasizes the beauty of the scenery by referring to literature on the natural landscape of the tourist destination. The streaming unit streams footage that emphasizes cultural scenes by referring to literature on the cultural elements of the tourist destination. The streaming unit may or may not use generative AI to refer to relevant literature on the tourist destination. This allows for the provision of more detailed information by improving the accuracy of streaming based on relevant literature on the tourist destination.

[0050] The service provider selects the optimal display method when displaying the interface by referring to the user's past operation history. For example, the service provider prioritizes displaying interface designs that the user has previously preferred. The service provider places functions that the user has frequently used in the past in prominent positions. The service provider suggests the most efficient operation procedure based on the user's past operation history. The service provider may or may not use generative AI to refer to the user's past operation history. By selecting the optimal display method based on the user's past operation history, it is possible to provide a user-friendly interface.

[0051] The service provider applies different display algorithms depending on the characteristics of the tourist destination when displaying the interface. For example, for tourist destinations with natural landscapes, the service provider provides an interface that emphasizes the beauty of the scenery. For tourist destinations with historical buildings, the service provider provides an interface that emphasizes the details of the buildings. For tourist destinations with many activities, the service provider provides an interface that emphasizes the movement of the activities. The service provider may use or may not use a generative AI to apply different display algorithms depending on the characteristics of the tourist destination. This makes it possible to provide an optimal interface for each tourist destination by applying a display algorithm that is appropriate for the characteristics of the tourist destination.

[0052] The service provider selects the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the service provider provides a display method that matches the screen size. If the user is using a tablet, the service provider provides a display method optimized for a large screen. If the user is using a VR headset, the service provider provides a display method optimized for 360-degree video. The service provider may or may not use a generative AI to obtain the user's device information. This allows the service provider to provide an interface optimized for each device by selecting the optimal display method based on the user's device information.

[0053] The service provider customizes the displayed content based on relevant information about the tourist destination when displaying the interface. For example, the service provider may refer to the historical background of the tourist destination to display detailed information. The service provider may refer to information about the natural scenery of the tourist destination to provide a display that emphasizes the beauty of the landscape. The service provider may refer to information about the cultural elements of the tourist destination to provide a display that emphasizes cultural scenes. The service provider may use or not use a generative AI to refer to relevant information about the tourist destination. This allows the service provider to provide the user with the most optimal information by customizing the displayed content based on relevant information about the tourist destination.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] A travel / tourism experience system can analyze a user's past selection history and provide a list of optimal tourist destinations. For example, it can analyze the trends of tourist destinations a user has previously selected and display similar destinations in a list. It can also display highly-rated tourist destinations based on the user's ratings of previously visited destinations. Furthermore, it can display tourist destinations in the same category as those a user has previously selected. In this way, by analyzing past selection history, the system can provide a list of tourist destinations that are best suited to the user.

[0056] The travel / tourism experience system can filter a list of tourist destinations based on the user's current interests. For example, it can display a list of tourist destinations based on themes the user is currently interested in. It can also display a list of tourist destinations based on keywords the user has recently searched for. Furthermore, it can display a list of tourist destinations related to events the user is currently participating in. By filtering the list based on the user's current interests, it can suggest more relevant tourist destinations.

[0057] The travel / tourism experience system can prioritize displaying highly relevant tourist destinations based on the user's geographical location. For example, it can display tourist destinations close to the user's current location at the top of the list. It can also display tourist destinations easily accessible from the user's current location at the top of the list. Furthermore, it can display tourist destinations relevant to the user's current location at the top of the list. In this way, by prioritizing the display of highly relevant tourist destinations based on the user's geographical location, it can suggest tourist destinations that are easily accessible to the user.

[0058] The travel / tourism experience system can analyze a user's social media activity and add relevant tourist destinations to a list. For example, it can display tourist destinations that the user has "liked" on social media. It can also display tourist destinations that the user follows on social media. Furthermore, it can display tourist destinations that the user has shared on social media. This allows the system to suggest tourist destinations based on the user's interests by analyzing their social media activity.

[0059] The travel / tourism experience system can select the optimal display method by referring to the user's past operation history. For example, it can prioritize displaying interface designs that the user has previously preferred. It can also place frequently used functions in prominent positions. Furthermore, it can suggest the most efficient operating procedure based on the user's past operation history. In this way, by selecting the optimal display method based on the user's past operation history, it can provide a user-friendly interface.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The list provider provides a list of tourist destinations. The list provider provides a list containing information such as the name, location, and characteristics of the tourist destinations and displays it to the user. The user can select their desired tourist destination from this list. Step 2: The generation unit generates VR content for tourist destinations selected from the list provided by the list provider unit. The generation unit generates high-quality VR footage of the selected tourist destinations in a 360-degree viewable format, providing the user with a realistic experience. Step 3: The streaming unit streams the VR content generated by the generation unit. The streaming unit streams the generated VR video to the user in real time and adjusts the streaming quality according to the user's network conditions. Step 4: The delivery unit provides the user with VR content streamed by the streaming unit. The delivery unit provides an interface for the user to put on a VR device and select their desired tourist destination, giving the user an experience as if they were actually there.

[0062] (Example of form 2) The travel / tourism experience system according to an embodiment of the present invention is a system that provides travel / tourism experiences using VR technology to people who cannot travel or go sightseeing, and to tourist destinations suffering from a decline in tourists. This system allows users to wear a VR device and select their desired tourist destination, providing VR content for the selected destination, enabling users to experience it as if they were actually there. This system makes it possible to effectively reach a wide range of targets, overcoming geographical and temporal constraints. Furthermore, because it can convey the appeal of tourist destinations at any time, regardless of the actual tourist season or weather, it can significantly improve the efficiency of tourism promotion. For example, a user wears a VR device and selects their desired tourist destination. At this time, the user can access the system and choose a place they want to go from a list of provided tourist destinations. For example, if a user selects "the Eiffel Tower in Paris," VR content for that tourist destination is provided. Next, the system provides VR content for the selected tourist destination. The system streams high-quality VR video of the selected tourist destination to the user. For example, it provides video that allows users to view the surroundings of the Eiffel Tower in 360 degrees, or video that allows them to explore the inside of the tower. This allows users to experience it as if they were actually there. Furthermore, this system can effectively reach a wide range of targets, transcending geographical and temporal constraints. For example, people who find it difficult to take vacations due to work or financial reasons, or those who cannot travel due to a combination of factors, can easily experience tourist destinations using this system. In addition, because it can convey the appeal of tourist destinations at any time, regardless of the actual tourist season or weather, it can greatly improve the efficiency of tourism promotion. For example, it can convey the appeal of tourist destinations even outside of the tourist season, and is expected to increase the number of visitors. In this way, travel / tourism experience systems using VR technology can effectively convey the appeal of tourist destinations and improve the efficiency of tourism promotion.

[0063] The travel / tourism experience system according to the embodiment comprises a list provision unit, a generation unit, a streaming unit, and a provision unit. The list provision unit provides a list of tourist destinations. The list provision unit provides a list that includes information such as the name, location, and characteristics of the tourist destinations. The list provision unit displays the list of tourist destinations to the user, allowing the user to select the tourist destination they wish to visit. The generation unit generates VR content for the tourist destination selected from the list provided by the list provision unit. The generation unit generates, for example, high-quality VR video of the selected tourist destination. The generation unit generates the video of the tourist destination in a 360-degree viewable format, providing the user with an experience as if they were actually there. The generation unit generates the video of the tourist destination in high resolution, providing the user with a realistic experience. The streaming unit streams the VR content generated by the generation unit. The streaming unit streams, for example, the generated VR video to the user in real time. The streaming unit adjusts the streaming quality according to the user's network conditions to provide an uninterrupted viewing experience. The streaming unit selects the optimal streaming method based on the user's device information and provides a viewing experience optimized for each device. The provisioning unit provides the user with VR content streamed by the streaming unit. The provisioning unit provides, for example, an interface for the user to wear a VR device and select their desired tourist destination. The provisioning unit provides the user with an experience as if they were actually there, effectively conveying the appeal of the tourist destination. The provisioning unit estimates the user's emotions and adjusts the display method of the interface based on the estimated user emotions. As a result, the travel / tourism experience system according to the embodiment can consistently perform everything from providing a list of tourist destinations to generating, streaming, and providing VR content.

[0064] The list provider provides lists of tourist destinations. These lists include information such as the name, location, and characteristics of each destination. Specifically, the list provider retrieves detailed information about tourist destinations from a database and displays it to the user in a visually easy-to-understand format. This includes not only the name and location of the destination, but also its historical background, cultural significance, major tourist attractions, access methods, and information on nearby accommodations and restaurants. Furthermore, the list provider can provide individually customized lists based on the user's past search history and interests. For example, if a user has previously searched for natural landscapes, the list will prioritize listing tourist destinations with abundant natural scenery. The list provider also has a function to suggest the most suitable tourist destinations based on the user's current location and travel purpose. For example, if a user is on a business trip, the list provider will suggest tourist destinations that can be visited in a short time or those near business hotels. This allows the list provider to provide flexible lists tailored to user needs, making it easy for users to select their desired tourist destinations. Additionally, the list provider updates and provides users with the latest information on tourist destinations and events in real time. This allows users to always select tourist destinations based on the latest information.

[0065] The generation unit generates VR content for tourist destinations selected from a list provided by the list provider unit. For example, the generation unit generates high-quality VR footage of the selected tourist destinations. Specifically, the generation unit creates a 3D model of the tourist destination and uses this to generate 360-degree panoramic VR footage. The 3D model of the tourist destination is created using high-resolution photos and videos taken by drones and ground cameras. This allows users to experience the tourist destinations in detail and with realism. Furthermore, the generation unit simultaneously generates audio guides and background sounds for the tourist destinations, providing users with an immersive experience. For example, it provides audio guides explaining the history and culture of the tourist destination, and reproduces the unique natural sounds and urban noise of the location in the background. This allows users to experience the tourist destinations not only visually but also aurally. The generation unit generates VR content at the optimal resolution and frame rate according to the user's device, providing a smooth viewing experience. For example, it provides 4K resolution footage to users using high-performance VR headsets, and footage adjusted to an appropriate resolution to users using smartphones. This allows the generation unit to create high-quality VR content optimized for the user's device, providing a realistic sightseeing experience.

[0066] The streaming unit streams VR content generated by the generation unit. For example, the streaming unit streams generated VR video to the user in real time. Specifically, the streaming unit dynamically adjusts the streaming quality according to the user's network conditions to provide an uninterrupted viewing experience. For example, if the user has a high-speed internet connection, it streams high-resolution video; if the connection is slow, it automatically lowers the resolution to minimize buffering. The streaming unit also selects the optimal streaming method based on the user's device information, providing an optimized viewing experience for each device. For example, it provides low-latency, high-frame-rate streaming to users using VR headsets, and streaming that conserves battery power to users using smartphones. Furthermore, the streaming unit has a function to recommend VR content of relevant tourist destinations based on the user's viewing history and preferences. This allows users to discover new tourist destinations and enjoy a series of VR experiences. The streaming unit utilizes a cloud-based, scalable infrastructure to provide stable streaming even when multiple users access it simultaneously. This enables the streaming unit to provide users with a high-quality, uninterrupted VR content viewing experience.

[0067] The service provider delivers VR content streamed by the streaming service provider to users. For example, the service provider provides an interface for users to put on a VR device and select their desired tourist destination. Specifically, the service provider designs an intuitive user interface that makes it easy for users to select a tourist destination and start viewing. For example, users can use the VR device's controller to scroll through a list of tourist destinations and select one that interests them. The service provider also puts effort into the interface design and usability to provide users with an experience that makes them feel as if they are actually there and to effectively convey the appeal of the tourist destination. For example, before the video of the tourist destination starts, a brief introduction and highlights of the place are displayed so that users can obtain information in advance. The service provider also estimates the user's emotions and adjusts the way the interface is displayed based on the estimated user emotions. For example, if the user is excited, more dynamic visual effects are added, and if they are relaxed, calmer visual effects are provided. In this way, the service provider can provide an optimal viewing experience that matches the user's emotions and maximize the appeal of the tourist destination. Furthermore, the service provider collects user feedback and uses it to improve the interface and content. This allows the service provider to consistently deliver the optimal experience tailored to the user's needs.

[0068] The service provider provides an interface for users to wear a VR device and select their desired tourist destination. For example, the service provider provides an interface that allows users to wear a VR device, access the system, and select a place they want to go from a list of tourist destinations. The service provider provides an intuitive interface that makes it easy for users to select tourist destinations. The service provider can also estimate the user's emotions and adjust the way the interface is displayed based on the estimated emotions. This enables intuitive operation by providing an interface for users to select tourist destinations through a VR device.

[0069] The generation unit generates high-quality VR footage of selected tourist destinations. For example, the generation unit generates high-resolution VR footage of selected tourist destinations, providing users with a realistic travel experience. The generation unit generates footage of tourist destinations in a 360-degree viewable format, providing users with an experience as if they were actually there. The generation unit generates high-resolution footage of tourist destinations, providing users with a realistic experience. The generation unit can also highlight key elements of tourist destinations when generating footage. This allows for the generation of high-quality VR footage, providing users with a realistic travel experience.

[0070] The streaming unit streams the generated VR video to the user. For example, the streaming unit streams the generated VR video to the user in real time. The streaming unit adjusts the streaming quality according to the user's network conditions to provide an uninterrupted viewing experience. The streaming unit selects the optimal streaming method based on the user's device information to provide an optimized viewing experience for each device. The streaming unit can also apply different streaming technologies depending on the characteristics of the tourist destination. This allows users to enjoy a real-time tourist experience by streaming the generated VR video.

[0071] The service provider offers users an experience that makes them feel as if they are actually there. For example, the service provider provides users with an experience that makes them feel as if they are actually there by having them wear a VR device and view VR content of a selected tourist destination. The service provider provides high-resolution video of the tourist destination to give users a realistic experience. The service provider provides video of the tourist destination in a 360-degree viewable format to give users an experience that makes them feel as if they are actually there. The service provider can also estimate the user's emotions and adjust the interface display method based on the estimated user emotions. This allows the service provider to effectively convey the appeal of the tourist destination by providing users with an experience that makes them feel as if they are actually there.

[0072] The list provider estimates the user's emotions and customizes the list of tourist destinations based on those emotions. For example, if the user is relaxed, the list provider will prioritize displaying relaxing tourist destinations. If the user is excited, the list provider will display tourist destinations with abundant activities. If the user is tired, the list provider will display tourist destinations with a calming effect. The list provider is implemented using emotion estimation functionality, such as an emotion engine or generative AI, to estimate the user's emotions. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more personalized suggestions of tourist destinations by customizing the list based on the user's emotions.

[0073] The list provider analyzes the user's past selection history and provides an optimal list of tourist destinations. For example, the list provider analyzes the trends of tourist destinations the user has previously selected and displays similar destinations in the list. Based on the user's ratings of tourist destinations they have previously visited, the list provider displays highly-rated tourist destinations in the list. Based on the categories of tourist destinations the user has previously selected, the list provider displays tourist destinations in the same category in the list. The list provider may or may not use generative AI to analyze the user's past selection history. By analyzing past selection history, it is possible to provide the user with an optimal list of tourist destinations.

[0074] The list provider filters the list based on the user's current interests when providing it. For example, the list provider displays tourist destinations based on themes the user is currently interested in. The list provider displays tourist destinations based on keywords the user has recently searched for. The list provider displays tourist destinations related to events the user is currently participating in. The list provider may or may not use generative AI to identify the user's current interests. This allows for the suggestion of more relevant tourist destinations by filtering the list based on the user's current interests.

[0075] The list provider estimates the user's emotions and adjusts the display order of the list based on the estimated emotions. For example, if the user is relaxed, the list provider will display relaxing tourist destinations at the top of the list. If the user is excited, the list provider will display tourist destinations with abundant activities at the top of the list. If the user is tired, the list provider will display tourist destinations with a calming effect at the top of the list. The list provider is implemented using an emotion estimation function that utilizes an emotion engine or generative AI to estimate the user's emotions. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the list provider to prioritize displaying the most suitable tourist destinations for the user by adjusting the display order of the list based on the user's emotions.

[0076] The list provider prioritizes displaying highly relevant tourist destinations based on the user's geographical location information when providing a list. For example, the list provider displays tourist destinations close to the user's current location at the top of the list. The list provider displays tourist destinations easily accessible from the user's current location at the top of the list. The list provider displays tourist destinations related to the user's current location at the top of the list. The list provider may or may not use generative AI to obtain the user's geographical location information. By prioritizing the display of highly relevant tourist destinations based on the user's geographical location information, it is possible to suggest tourist destinations that are easily accessible to the user.

[0077] The list provider analyzes the user's social media activity when providing a list and adds relevant tourist destinations to the list. For example, the list provider displays tourist destinations that the user has "liked" on social media. The list provider displays tourist destinations that the user follows on social media. The list provider displays tourist destinations that the user has shared on social media. The list provider may or may not use generative AI to analyze the user's social media activity. By analyzing the user's social media activity, it is possible to suggest tourist destinations based on the user's interests.

[0078] The generation unit estimates the user's emotions and adjusts the VR content generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates VR content that progresses at a leisurely pace. If the user is excited, the generation unit generates VR content that includes many active scenes. If the user is tired, the generation unit generates VR content that includes many calming scenes. The generation unit is implemented using emotion estimation functionality, such as an emotion engine or generation AI, to estimate the user's emotions. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for a more personalized experience by adjusting the VR content generation method based on the user's emotions.

[0079] The generation unit adjusts the level of detail of the VR content based on key elements of the tourist destination during generation. For example, the generation unit generates detailed VR content that emphasizes the historical background of the tourist destination. The generation unit generates detailed VR content that emphasizes the natural scenery of the tourist destination. The generation unit generates detailed VR content that emphasizes the cultural elements of the tourist destination. The generation unit may use or may not use generation AI to identify key elements of the tourist destination. This allows information important to the user to be highlighted by adjusting the level of detail of the VR content based on key elements of the tourist destination.

[0080] The generation unit applies different generation algorithms depending on the category of the tourist destination during generation. For example, for tourist destinations with natural landscapes, the generation unit applies a generation algorithm that emphasizes the beauty of the scenery. For tourist destinations with historical buildings, the generation unit applies a generation algorithm that emphasizes the details of the buildings. For tourist destinations with many activities, the generation unit applies a generation algorithm that emphasizes the movement of the activities. The generation unit may or may not use a generation AI to apply different generation algorithms depending on the category of the tourist destination. This makes it possible to generate VR content that is optimal for each tourist destination by applying different generation algorithms depending on the category of the tourist destination.

[0081] The generation unit estimates the user's emotions and adjusts the length of the VR content based on the estimated emotions. For example, if the user is relaxed, the generation unit generates longer VR content. If the user is in a hurry, the generation unit generates shorter VR content. If the user is excited, the generation unit generates VR content of an appropriate length. The generation unit is implemented using emotion estimation functionality, such as an emotion engine or generation AI, to estimate the user's emotions. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the provision of an optimal experience tailored to the user's situation by adjusting the length of the VR content based on the user's emotions.

[0082] The generation unit determines the priority of VR content based on the season and time of day of the tourist destination during generation. For example, the generation unit generates VR content that emphasizes seasonal highlights. The generation unit generates VR content that emphasizes the atmosphere of different times of day. The generation unit generates VR content that includes event information appropriate to the season and time of day. The generation unit may or may not use a generation AI to determine the priority of VR content based on the season and time of day of the tourist destination. By prioritizing VR content based on the season and time of day of the tourist destination, it is possible to provide the optimal timing for the tourist experience.

[0083] The generation unit adjusts the order of VR content based on the relevance of tourist destinations during generation. For example, the generation unit generates VR content that displays the main attractions of a tourist destination first. The generation unit generates VR content that is displayed based on the historical order of tourist destinations. The generation unit generates VR content that is displayed based on the geographical order of tourist destinations. The generation unit may use or may not use generation AI to adjust the order of VR content based on the relevance of tourist destinations. This allows users to experience tourist destinations in the optimal order by adjusting the order of VR content based on the relevance of tourist destinations.

[0084] The streaming unit estimates the user's emotions and adjusts the streaming quality based on the estimated emotions. For example, if the user is relaxed, the streaming unit streams high-quality video. If the user is excited, the streaming unit prioritizes streaming scenes with a lot of movement. If the user is tired, the streaming unit prioritizes streaming scenes that are visually calming. The streaming unit uses emotion estimation functionality, such as an emotion engine or generative AI, to estimate the user's emotions. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for a more comfortable viewing experience by adjusting the streaming quality based on the user's emotions.

[0085] The streaming unit optimizes the streaming method based on the user's network conditions during streaming. For example, the streaming unit adjusts the video resolution according to the user's network speed. The streaming unit adjusts buffering according to the stability of the user's network. The streaming unit adjusts the streaming bitrate according to the user's network usage. The streaming unit may or may not use generative AI to evaluate the user's network conditions. By optimizing the streaming method based on the user's network conditions, an uninterrupted viewing experience can be provided.

[0086] The streaming unit applies different streaming technologies depending on the characteristics of the tourist destination during streaming. For example, the streaming unit streams high-resolution video to tourist destinations with natural landscapes. The streaming unit streams detailed video to tourist destinations with historical buildings. The streaming unit streams video with a lot of movement to tourist destinations with many activities. The streaming unit may or may not use generative AI to apply different streaming technologies depending on the characteristics of the tourist destination. This allows for the application of the optimal streaming technology according to the characteristics of the tourist destination, thereby providing users with the best possible viewing experience.

[0087] The streaming unit estimates the user's emotions and adjusts the streaming order based on the estimated emotions. For example, if the user is relaxed, the streaming unit streams relaxing scenes first. If the user is excited, the streaming unit streams active scenes first. If the user is tired, the streaming unit streams calming scenes first. The streaming unit is implemented using emotion estimation functionality, such as an emotion engine or generative AI, to estimate the user's emotions. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for a more personalized viewing experience by adjusting the streaming order based on the user's emotions.

[0088] The streaming unit selects the optimal streaming method during streaming, taking into account the user's device information. For example, if the user is using a smartphone, the streaming unit provides a streaming method that matches the screen size. If the user is using a tablet, the streaming unit provides a streaming method optimized for a larger screen. If the user is using a VR headset, the streaming unit streams 360-degree video. The streaming unit may or may not use generative AI to obtain the user's device information. This allows for the selection of the optimal streaming method based on the user's device information, thereby providing a viewing experience optimized for each device.

[0089] The streaming unit improves the accuracy of streaming based on relevant literature on the tourist destination during streaming. For example, the streaming unit streams detailed footage by referring to the historical background of the tourist destination. The streaming unit streams footage that emphasizes the beauty of the scenery by referring to literature on the natural landscape of the tourist destination. The streaming unit streams footage that emphasizes cultural scenes by referring to literature on the cultural elements of the tourist destination. The streaming unit may or may not use generative AI to refer to relevant literature on the tourist destination. This allows for the provision of more detailed information by improving the accuracy of streaming based on relevant literature on the tourist destination.

[0090] The service provider estimates the user's emotions and adjusts the interface display based on the estimated emotions. For example, if the user is relaxed, the service provider provides an interface with calming colors. If the user is excited, the service provider provides an interface with bright colors. If the user is tired, the service provider provides a simple and highly visible interface. The service provider uses emotion estimation functionality, such as an emotion engine or generative AI, to estimate the user's emotions. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for a more comfortable user experience by adjusting the interface display based on the user's emotions.

[0091] The service provider selects the optimal display method when displaying the interface by referring to the user's past operation history. For example, the service provider prioritizes displaying interface designs that the user has previously preferred. The service provider places functions that the user has frequently used in the past in prominent positions. The service provider suggests the most efficient operation procedure based on the user's past operation history. The service provider may or may not use generative AI to refer to the user's past operation history. By selecting the optimal display method based on the user's past operation history, it is possible to provide a user-friendly interface.

[0092] The service provider applies different display algorithms depending on the characteristics of the tourist destination when displaying the interface. For example, for tourist destinations with natural landscapes, the service provider provides an interface that emphasizes the beauty of the scenery. For tourist destinations with historical buildings, the service provider provides an interface that emphasizes the details of the buildings. For tourist destinations with many activities, the service provider provides an interface that emphasizes the movement of the activities. The service provider may use or may not use a generative AI to apply different display algorithms depending on the characteristics of the tourist destination. This makes it possible to provide an optimal interface for each tourist destination by applying a display algorithm that is appropriate for the characteristics of the tourist destination.

[0093] The service provider estimates the user's emotions and adjusts the interface's operation procedures based on the estimated emotions. For example, if the user is relaxed, the service provider provides detailed instructions. If the user is in a hurry, the service provider provides concise instructions. If the user is excited, the service provider provides visually stimulating instructions. The service provider is implemented using emotion estimation functionality, such as an emotion engine or generative AI, to estimate the user's emotions. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for a more comfortable user experience by adjusting the interface's operation procedures based on the user's emotions.

[0094] The service provider selects the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the service provider provides a display method that matches the screen size. If the user is using a tablet, the service provider provides a display method optimized for a large screen. If the user is using a VR headset, the service provider provides a display method optimized for 360-degree video. The service provider may or may not use a generative AI to obtain the user's device information. This allows the service provider to provide an interface optimized for each device by selecting the optimal display method based on the user's device information.

[0095] The service provider customizes the displayed content based on relevant information about the tourist destination when displaying the interface. For example, the service provider may refer to the historical background of the tourist destination to display detailed information. The service provider may refer to information about the natural scenery of the tourist destination to provide a display that emphasizes the beauty of the landscape. The service provider may refer to information about the cultural elements of the tourist destination to provide a display that emphasizes cultural scenes. The service provider may use or not use a generative AI to refer to relevant information about the tourist destination. This allows the service provider to provide the user with the most optimal information by customizing the displayed content based on relevant information about the tourist destination.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The travel / tourism experience system can estimate the user's emotions and customize the list of tourist destinations based on those emotions. For example, if the user is relaxed, relaxing tourist destinations can be prioritized in the list. If the user is excited, tourist destinations with plenty of activities can be displayed. Furthermore, if the user is tired, tourist destinations with a calming effect can be displayed. By customizing the list based on the user's emotions, it becomes possible to suggest more personalized tourist destinations.

[0098] A travel / tourism experience system can analyze a user's past selection history and provide a list of optimal tourist destinations. For example, it can analyze the trends of tourist destinations a user has previously selected and display similar destinations in a list. It can also display highly-rated tourist destinations based on the user's ratings of previously visited destinations. Furthermore, it can display tourist destinations in the same category as those a user has previously selected. In this way, by analyzing past selection history, the system can provide a list of tourist destinations that are best suited to the user.

[0099] The travel / tourism experience system can filter a list of tourist destinations based on the user's current interests. For example, it can display a list of tourist destinations based on themes the user is currently interested in. It can also display a list of tourist destinations based on keywords the user has recently searched for. Furthermore, it can display a list of tourist destinations related to events the user is currently participating in. By filtering the list based on the user's current interests, it can suggest more relevant tourist destinations.

[0100] The travel / tourism experience system can estimate the user's emotions and adjust how VR content is generated based on those emotions. For example, if the user is relaxed, it can generate VR content that progresses at a leisurely pace. If the user is excited, it can generate VR content that includes many active scenes. Furthermore, if the user is tired, it can generate VR content that includes many scenes with a calming effect. In this way, by adjusting how VR content is generated based on the user's emotions, a more personalized experience can be provided.

[0101] The travel / tourism experience system can estimate the user's emotions and adjust the streaming quality based on those emotions. For example, if the user is relaxed, it can stream high-quality video. If the user is excited, it can prioritize streaming scenes with a lot of movement. Furthermore, if the user is tired, it can prioritize streaming scenes that are visually calming. By adjusting the streaming quality based on the user's emotions, it can provide a more comfortable viewing experience.

[0102] The travel / tourism experience system can prioritize displaying highly relevant tourist destinations based on the user's geographical location. For example, it can display tourist destinations close to the user's current location at the top of the list. It can also display tourist destinations easily accessible from the user's current location at the top of the list. Furthermore, it can display tourist destinations relevant to the user's current location at the top of the list. In this way, by prioritizing the display of highly relevant tourist destinations based on the user's geographical location, it can suggest tourist destinations that are easily accessible to the user.

[0103] The travel / tourism experience system can analyze a user's social media activity and add relevant tourist destinations to a list. For example, it can display tourist destinations that the user has "liked" on social media. It can also display tourist destinations that the user follows on social media. Furthermore, it can display tourist destinations that the user has shared on social media. This allows the system to suggest tourist destinations based on the user's interests by analyzing their social media activity.

[0104] The travel / tourism experience system can estimate the user's emotions and adjust the interface display based on those emotions. For example, if the user is relaxed, it can provide an interface with calming colors. If the user is excited, it can provide an interface with bright colors. Furthermore, if the user is tired, it can provide a simple and highly visible interface. By adjusting the interface display based on the user's emotions, a more comfortable user experience can be provided.

[0105] The travel / tourism experience system can select the optimal display method by referring to the user's past operation history. For example, it can prioritize displaying interface designs that the user has previously preferred. It can also place frequently used functions in prominent positions. Furthermore, it can suggest the most efficient operating procedure based on the user's past operation history. In this way, by selecting the optimal display method based on the user's past operation history, it can provide a user-friendly interface.

[0106] A travel / tourism experience system can estimate the user's emotions and adjust the interface's operation procedures based on those emotions. For example, if the user is relaxed, detailed instructions can be provided. If the user is in a hurry, concise instructions can be provided. Furthermore, if the user is excited, visually stimulating instructions can be provided. In this way, by adjusting the interface's operation procedures based on the user's emotions, a more comfortable user experience can be provided.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The list provider provides a list of tourist destinations. The list provider provides a list containing information such as the name, location, and characteristics of the tourist destinations and displays it to the user. The user can select their desired tourist destination from this list. Step 2: The generation unit generates VR content for tourist destinations selected from the list provided by the list provider unit. The generation unit generates high-quality VR footage of the selected tourist destinations in a 360-degree viewable format, providing the user with a realistic experience. Step 3: The streaming unit streams the VR content generated by the generation unit. The streaming unit streams the generated VR video to the user in real time and adjusts the streaming quality according to the user's network conditions. Step 4: The delivery unit provides the user with VR content streamed by the streaming unit. The delivery unit provides an interface for the user to put on a VR device and select their desired tourist destination, giving the user an experience as if they were actually there.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the above-described list providing unit, generation unit, streaming unit, and multiple elements including the providing unit is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the list providing unit is implemented by the control unit 46A of the smart device 14 and displays a list of tourist destinations to the user. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates high-quality VR images of selected tourist destinations. The streaming unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and streams the generated VR images to the user in real time. The providing unit is implemented, for example, by the control unit 46A of the smart device 14 and provides the user with an experience as if they were actually there. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the above-mentioned list providing unit, generation unit, streaming unit, and multiple elements including the providing unit is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the list providing unit is implemented by the control unit 46A of the smart glasses 214 and displays a list of tourist destinations to the user. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates high-quality VR images of selected tourist destinations. The streaming unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and streams the generated VR images to the user in real time. The providing unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the user with an experience as if they were actually there. The correspondence between each unit and the device or control unit is not limited to the example above and can be changed in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the above-mentioned list provision unit, generation unit, streaming unit, and multiple elements including the provision unit is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the list provision unit is implemented by the control unit 46A of the headset terminal 314 and displays a list of tourist destinations to the user. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates high-quality VR images of selected tourist destinations. The streaming unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and streams the generated VR images to the user in real time. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides the user with an experience as if they were actually there. The correspondence between each unit and the device or control unit is not limited to the example above and can be changed in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the above-mentioned list providing unit, generation unit, streaming unit, and multiple elements including the providing unit is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the list providing unit is implemented by the control unit 46A of the robot 414 and displays a list of tourist destinations to the user. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates high-quality VR images of selected tourist destinations. The streaming unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and streams the generated VR images to the user in real time. The providing unit is implemented, for example, by the control unit 46A of the robot 414 and provides the user with an experience as if they were actually there. The correspondence between each unit and the device or control unit is not limited to the example above and can be changed in various ways.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A list provision department that provides a list of tourist destinations, A generation unit that generates VR content for tourist destinations selected from the list provided by the list provision unit, A streaming unit that streams the VR content generated by the generation unit, The system includes a providing unit that provides the VR content streamed by the aforementioned streaming unit to the user. A system characterized by the following features. (Note 2) The aforementioned supply unit is, It provides an interface for users to wear VR devices and select their desired tourist destinations. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate high-quality VR videos of selected tourist destinations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned streaming unit, The generated VR video is streamed to the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, To provide users with an experience that makes them feel as if they are actually there. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned list providing unit, It estimates the user's emotions and customizes the list of tourist destinations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned list providing unit, It analyzes the user's past selection history and provides a list of the most suitable tourist destinations. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned list providing unit, When providing lists, filter them based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned list providing unit, It estimates the user's sentiment and adjusts the display order of the list based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned list providing unit, When providing lists, the system prioritizes displaying highly relevant tourist destinations based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned list providing unit, When providing the list, we analyze the user's social media activity and add relevant tourist destinations to the list. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts the VR content generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During generation, the level of detail in the VR content is adjusted based on key elements of the tourist destination. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, different generation algorithms are applied depending on the category of the tourist destination. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts the length of the VR content based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, VR content is prioritized based on the season and time of day of the tourist destination. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the order of VR content is adjusted based on the relevance of tourist destinations. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned streaming unit, It estimates the user's emotions and adjusts the streaming quality based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned streaming unit, During streaming, the streaming method is optimized based on the user's network conditions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned streaming unit, When streaming, different streaming technologies are applied depending on the characteristics of the tourist destination. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned streaming unit, It estimates the user's emotions and adjusts the streaming order based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned streaming unit, During streaming, the system selects the optimal streaming method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned streaming unit, During streaming, improve streaming accuracy based on relevant literature on tourist destinations. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts the interface display based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When displaying the interface, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When displaying the interface, different display algorithms are applied depending on the characteristics of the tourist destination. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts the interface operation procedures based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When displaying the interface, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When displaying the interface, customize the displayed content based on relevant information about the tourist destination. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A list provision department that provides a list of tourist destinations, A generation unit that generates VR content for tourist destinations selected from the list provided by the list provision unit, A streaming unit that streams the VR content generated by the generation unit, The system includes a providing unit that provides the VR content streamed by the aforementioned streaming unit to the user. A system characterized by the following features.

2. The aforementioned supply unit is, It provides an interface for users to wear VR devices and select their desired tourist destinations. The system according to feature 1.

3. The generating unit is Generate high-quality VR videos of selected tourist destinations. The system according to feature 1.

4. The aforementioned streaming unit, The generated VR video is streamed to the user. The system according to feature 1.

5. The aforementioned supply unit is, To provide users with an experience that makes them feel as if they are actually there. The system according to feature 1.

6. The aforementioned list providing unit, It estimates the user's emotions and customizes the list of tourist destinations based on those estimated emotions. The system according to feature 1.

7. The aforementioned list providing unit, It analyzes the user's past selection history and provides a list of the most suitable tourist destinations. The system according to feature 1.

8. The aforementioned list providing unit, When providing lists, filter them based on the user's current interests and preferences. The system according to feature 1.

9. The aforementioned list providing unit, It estimates the user's sentiment and adjusts the display order of the list based on the estimated user sentiment. The system according to feature 1.

10. The aforementioned list providing unit, When providing lists, the system prioritizes displaying highly relevant tourist destinations based on the user's geographical location. The system according to feature 1.

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