system

Abstract

The system according to this embodiment aims to recreate places and situations that elderly people would like to experience but are unable to do so, using VR. [Solution] The system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives input from the user regarding their desired experience. The generation unit analyzes the information received from the reception unit and generates content to recreate the desired experience in VR. The provision unit provides the VR content generated by the generation 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, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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, it is difficult to reproduce places and situations that even elderly people cannot experience if they want to, and there is room for improvement.

[0005] The system according to the embodiment aims to reproduce, in VR, places and situations that even elderly people cannot experience if they want to.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit inputs the experience desired by the user. The generation unit analyzes the information input by the reception unit and generates content for reproducing the desired experience in VR. The provision unit provides the VR content generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can use VR to recreate places and situations that elderly people would like to experience but are unable to. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 VR experience system according to an embodiment of the present invention is a system that recreates places and situations in VR that users would like to experience but are unable to do so otherwise. This VR experience system allows the elderly, people who cannot travel abroad for physical reasons, and their families to enjoy new experiences. Specifically, it consists of the following steps. First, the user inputs the experience they desire. For example, they might input a desire such as, "I want to go to Paris on a family trip." This information is input into the generating AI. Next, the generating AI analyzes the input information and generates content to recreate the desired experience in VR. The generating AI creates realistic VR content based on past travel data, photos, videos, etc. For example, it generates VR content that recreates the streets and tourist spots of Paris. The generated VR content is provided to the user using robotic technology. The user can wear a VR headset and experience the generated content. For example, they can walk around the streets of Paris or look up at the Eiffel Tower. This mechanism allows the elderly and people who cannot travel abroad for physical reasons to enjoy new experiences. It also enables family trips and the creation of memories. For example, by enjoying a VR trip together as a family, family bonds can be strengthened. Furthermore, by expanding rich content using generative AI, it becomes possible to offer it at a low cost. This enables individual rentals, short-term rentals, and provision to facilities, and is expected to penetrate a wide market. For example, with the cooperation of local governments, short-term experiences can be offered in shops. It is also possible to offer it as an experiential plan using hometown tax donations to connect regular supply and demand. This ensures regular demand and can meet continuous needs. In this way, by combining robotic technology and generative AI, it is possible to provide new experiences to the elderly and those who cannot try new experiences due to physical reasons, and improve their quality of life. As a result, the VR experience system can reproduce and provide the user's desired experience in VR.

[0029] The VR experience system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit inputs the user's desired experience. The user's desired experience includes, but is not limited to, travel experiences, sports experiences, and educational experiences. For example, the user can input a desire such as "I want to go to Paris on a family trip" into the reception unit. The generation unit analyzes the information input by the reception unit and generates content to recreate the desired experience in VR. For example, the generation unit uses a generation AI to generate realistic VR content based on past travel data, photos, videos, etc. The generation AI uses, for example, a text generation AI (e.g., LLM) to analyze the user's desired experience and generate VR content. The generation unit can also use the generation AI to generate realistic VR content by making full use of 3D modeling and simulation technologies. For example, the generation AI generates VR content that recreates the streets and tourist spots of Paris based on past travel data, photos, videos, etc. The provision unit provides the VR content generated by the generation unit to the user. For example, the provision unit provides the generated content to the user through a VR headset. Users can wear a VR headset and experience the generated content. For example, a user can walk around the streets of Paris or look up at the Eiffel Tower. The service provider can also use robotic technology to deliver the generated VR content to the user. For example, the service provider can use a robotic arm or a mobile robot to deliver the VR content to the user. In this way, the VR experience system according to the embodiment can reproduce and deliver the user's desired experience in VR.

[0030] The reception desk allows users to input their desired experiences. These experiences include, but are not limited to, travel, sports, and educational activities. For example, a user might input, "I want to go to Paris on a family trip." Specifically, the reception desk is designed to allow users to input details of their desired experiences through a user interface. The user interface supports various input methods, including touchscreen, voice input, and keyboard input, and is designed for intuitive operation. For example, if a user desires a travel experience, they can input details such as the destination, duration of the trip, number of companions, and specific tourist spots. Furthermore, the reception desk learns the user's past experience history and preferences to suggest more personalized experiences. For example, it can suggest new experiences based on places the user has visited and activities they have been interested in in the past. This allows users to easily select experiences that suit their preferences. The reception desk also analyzes user input in real time and requests additional information as needed. For example, if a user desires "visiting museums in Paris," it can confirm details such as the names of specific museums and desired dates and times. This allows the reception department to accurately understand the user's desired experience and smoothly provide that information to the next generation department.

[0031] The generation unit analyzes the information entered by the reception unit and generates content to recreate the desired experience in VR. For example, the generation unit uses a generation AI to generate realistic VR content based on past travel data, photos, videos, etc. The generation AI uses a text generation AI (e.g., LLM) to analyze the user's desired experience and generate VR content. Specifically, the generation AI analyzes the text information entered by the user using natural language processing technology and extracts the necessary elements. For example, if the user enters the desire to "go to Paris on a family trip," the generation AI will extract keywords such as "family trip," "Paris," and "tourist destinations," and generate content based on them. The generation unit can also use the generation AI to generate realistic VR content by utilizing 3D modeling and simulation technologies. For example, the generation AI can generate VR content that recreates the streets and tourist destinations of Paris based on past travel data, photos, videos, etc. Specifically, it can accurately recreate major tourist spots such as the Eiffel Tower, the Louvre Museum, and the Champs-Élysées, providing the user with an experience as if they were actually visiting those places. Furthermore, the generation unit can create more personalized content by taking into account the user's preferences and past experience history. For example, it can add new experiential elements based on places the user has visited in the past and activities they have been interested in. This allows the generation unit to reproduce and deliver the user's desired experience in a highly accurate and personalized way.

[0032] The delivery unit provides the user with VR content generated by the generation unit. The delivery unit provides the generated content to the user, for example, through a VR headset. The user can wear the VR headset and experience the generated content. Specifically, the delivery unit provides an interface for the user to start the VR experience, and is designed to allow the user to start the experience with simple operations. For example, when the user puts on the VR headset, the automatically generated content is played, and the user can intuitively enjoy the experience. The user can walk around the streets of Paris or look up at the Eiffel Tower. The delivery unit can also provide the user with generated VR content using robotic technology. For example, the delivery unit can provide VR content to the user using a robotic arm or a mobile robot. Specifically, the robotic arm can adjust the position of the VR headset according to the user's movements, or the mobile robot can guide the user to a specific location. This allows the user to enjoy a more immersive experience. Furthermore, the delivery unit also has a function to collect user feedback and continuously improve the quality of the experience. For example, it can collect feedback on dissatisfaction and areas for improvement that the user felt during the experience and reflect them in the next experience. This allows the service provider to consistently deliver high-quality VR experiences to users.

[0033] The generation unit can generate realistic VR content based on past travel data, photos, videos, etc. For example, the generation unit can collect past travel data and generate VR content based on it. For example, the generation unit can recreate the scenery and tourist spots of a travel destination based on travel records and GPS data. The generation unit can also analyze past photos and videos and generate VR content based on them. For example, the generation unit can generate a realistic 3D model based on past travel photos. The generation unit can also generate realistic VR scenes based on past travel videos. For example, the generation unit can analyze travel videos and recreate the scenery and buildings in the video. In this way, realistic VR content can be generated based on past data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the generation unit can input past travel data, photos, and videos into a generation AI and have the generation AI execute the generation of realistic VR content.

[0034] The service provider can provide generated content to users through a VR headset. For example, the service provider can provide generated VR content to users using a VR headset. For example, the service provider can enable users to experience the generated VR content by having them wear a VR headset. The service provider can also select the optimal content delivery method depending on the type and specifications of the VR headset. For example, the service provider can provide content compatible with VR headsets such as Oculus Rift and HTC Vive. This allows the service provider to deliver content to users through a VR headset. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the generated VR content into a generation AI and have the generation AI select the optimal delivery method.

[0035] The generation unit can generate content to reproduce the desired experience using a generative AI. For example, the generation unit can use a generative AI to analyze the user's desired experience and generate VR content based on that analysis. The generative AI can use technologies such as GANs (Generative Opposite Networks) or reinforcement learning to generate realistic VR content. For example, the generative AI can receive the user's desired experience as input and generate VR content based on that. The generation unit can also use a generative AI to simulate the user's desired experience and generate VR content based on that simulation. For example, the generative AI can simulate the user's desired experience and generate VR content based on the results. This allows the generation unit to generate content to reproduce the desired experience using a generative AI. Some or all of the above-described processes in the generation unit may be performed using a generative AI or not. For example, the generation unit can input the user's desired experience into the generative AI and have the generative AI generate VR content.

[0036] The service provider can deliver the generated VR content to the user using robotic technology. For example, the service provider can deliver the generated VR content to the user using a robotic arm or a mobile robot. For example, the service provider can use a robotic arm to put a VR headset on the user. The service provider can also deliver the VR content to the user using a mobile robot. For example, the service provider can use a mobile robot to carry the VR headset to the user. In this way, VR content can be delivered using robotic technology. Some or all of the above-described processes in the service provider may be performed using a generation AI, or they may be performed without a generation AI. For example, the service provider can input robotic technology into a generation AI and have the generation AI select the optimal delivery method.

[0037] The generation unit can enhance rich content using a generation AI. For example, the generation unit uses the generation AI to generate rich content to improve the quality of VR content. The generation AI can generate rich content that includes, for example, high-resolution video and interactive elements. For example, the generation AI can generate high-resolution video based on the user's desired experience. The generation AI can also generate VR content that includes interactive elements. For example, the generation AI can add elements that allow the user to interact within the VR content. This allows for the enhancement of rich content using the generation AI. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit can have the generation AI perform the generation of rich content.

[0038] The reception desk can analyze a user's past experience history and suggest the most suitable experience. For example, the reception desk can collect a user's past experience history and suggest the most suitable experience based on it. For example, the reception desk can suggest new experiences related to places and situations the user has experienced in the past. The reception desk can also suggest experiences based on specific themes from a user's past experience history. For example, the reception desk can analyze a user's past experience history and suggest experiences tailored to the season or event. This allows the reception desk to suggest the most suitable experience based on past experience history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input a user's past experience history into an AI and have the AI ​​suggest the most suitable experience.

[0039] The reception desk can filter the user's desired experiences based on their current health status and interests. For example, the reception desk might prioritize suggesting experiences that do not require physical exertion, taking into account the user's health status. For instance, it might suggest relaxing experiences based on the user's heart rate and blood pressure. The reception desk can also filter and suggest relevant experiences based on the user's interests. For example, it might suggest experiences based on a specific theme. The reception desk can also suggest relaxing experiences based on the user's current health status. For example, it might suggest low-impact experiences based on the user's activity level. By filtering experiences based on the user's health status and interests, a more appropriate experience can be provided. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's health status and interests into an AI and have the AI ​​perform the experience filtering.

[0040] The reception desk can prioritize suggesting highly relevant experiences by considering the user's geographical location when the user inputs their desired experience. For example, the reception desk can suggest nearby tourist attractions or events based on the user's current location. For example, the reception desk can suggest nearby tourist attractions based on the user's GPS data. The reception desk can also filter and suggest relevant experiences based on the user's geographical location. For example, the reception desk can suggest relevant experiences based on the user's location-based services. The reception desk can also prioritize suggesting experiences that are easily accessible from the user's current location. For example, the reception desk can suggest tourist attractions close to the user's current location. By suggesting experiences while considering the user's geographical location, it is possible to provide more relevant experiences. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into AI and have the AI ​​suggest experiences.

[0041] The reception desk can analyze the user's social media activity when they input their desired experience and suggest relevant experiences. For example, the reception desk can suggest relevant experiences based on the user's interests and preferences on social media. For example, it can suggest relevant experiences based on the user's posts and the number of likes. The reception desk can also analyze the user's social media activity history and suggest experiences they have shown interest in in the past. For example, it can suggest relevant experiences based on the user's number of followers and comments. The reception desk can also suggest relevant experiences by referring to the activities of the user's friends on social media. For example, it can suggest experiences that the user's friends have shown interest in. In this way, by suggesting relevant experiences based on social media activity, more appropriate experiences can be provided. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity into AI and have the AI ​​perform experience suggestions.

[0042] The generation unit can adjust the level of detail of the VR content based on the importance of the experience during generation. For example, the generation unit can evaluate the importance of the experience and adjust the level of detail of the VR content accordingly. For example, in the case of an important experience, the generation unit can generate VR content that includes detailed scenes and high-resolution graphics. The generation unit can also generate VR content with a standard level of detail for a general experience. For example, the generation unit can generate VR content that includes graphics with a standard resolution. The generation unit can also generate VR content that includes only basic elements for a simple experience. For example, the generation unit can generate VR content that includes basic scenes and low-resolution graphics. This allows for a more appropriate experience to be provided by adjusting the level of detail of the VR content based on the importance of the experience. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the importance of the experience into the generation AI and have the generation AI perform the adjustment of the level of detail of the VR content.

[0043] The generation unit can apply different generation algorithms depending on the category of the experience during generation. For example, the generation unit evaluates the category of the experience and selects an appropriate generation algorithm based on that evaluation. For example, in the case of a travel experience, the generation unit can apply an algorithm that realistically reproduces tourist spots and landmarks. In the case of a nature experience, the generation unit can also apply an algorithm that realistically reproduces landscapes and flora and fauna. For example, the generation unit can select an algorithm for nature experiences and realistically reproduce landscapes and flora and fauna. In the case of a history experience, the generation unit can also apply an algorithm that realistically reproduces historical buildings and events. For example, the generation unit can select an algorithm for history experiences and realistically reproduce historical buildings and events. By applying different generation algorithms depending on the category of the experience, a more appropriate experience can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the category of the experience into the generation AI and have the generation AI select an appropriate generation algorithm.

[0044] The generation unit can determine the priority of VR content based on the submission date of the experience during generation. For example, the generation unit evaluates the submission date of the experience and determines the priority of VR content based on that. For example, the generation unit prioritizes generating the most recently submitted experience. The generation unit can also postpone older submitted experiences. For example, the generation unit adjusts the generation order based on the submission date. This allows for the provision of more appropriate experiences by prioritizing VR content based on the submission date of the experience. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the submission date of the experience into the generation AI and have the generation AI perform the determination of the VR content priority.

[0045] The generation unit can adjust the order of VR content based on the relevance of the experiences during generation. For example, the generation unit can evaluate the relevance of the experiences and adjust the order of the VR content based on that evaluation. For example, the generation unit can prioritize generating highly relevant experiences. The generation unit can also postpone less relevant experiences. For example, the generation unit adjusts the generation order based on relevance. By adjusting the order of VR content based on the relevance of the experiences, a more appropriate experience can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the relevance of the experiences into a generation AI and have the generation AI perform the adjustment of the order of the VR content.

[0046] The service delivery unit can select the optimal delivery method by referring to the user's past experience history at the time of delivery. For example, the service delivery unit can collect the user's past experience history and select the optimal delivery method based on it. For example, the service delivery unit can prioritize selecting delivery methods that the user has preferred in the past. The service delivery unit can also suggest the optimal delivery method based on the user's past experience history. For example, the service delivery unit can analyze the user's past experience history and customize the delivery method. By selecting the optimal delivery method based on past experience history, a more appropriate experience can be provided. Some or all of the above processes in the service delivery unit may be performed using AI or not. For example, the service delivery unit can input the user's past experience history into AI and have the AI ​​perform the selection of the delivery method.

[0047] The service provider can customize the delivery method based on the user's current health condition at the time of delivery. For example, the service provider can evaluate the user's health condition and customize the delivery method based on that evaluation. For example, if the user is tired, the service provider can select a delivery method that promotes relaxation. Alternatively, if the user is healthy, the service provider can select an active delivery method. For example, based on the user's health condition, the service provider can provide an active experience. Alternatively, if the user is unwell, the service provider can select a delivery method that minimizes burden. For example, based on the user's health condition, the service provider can provide a relaxing experience. By customizing the delivery method based on the user's health condition, a more appropriate experience can be provided. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's health condition into AI and have AI perform the customization of the delivery method.

[0048] The service provider can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, the service provider can suggest nearby tourist attractions or events based on the user's current location. For example, the service provider can suggest nearby tourist attractions based on the user's GPS data. The service provider can also filter and suggest relevant experiences based on the user's geographical location information. For example, the service provider can suggest relevant experiences based on the user's location-based services. The service provider can also prioritize suggesting experiences that are easily accessible from the user's current location. For example, the service provider can suggest tourist attractions close to the user's current location. By selecting a delivery method that takes geographical location information into account, a more appropriate experience can be provided. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's geographical location information into AI and have the AI ​​select the delivery method.

[0049] The service provider can analyze the user's social media activity and propose delivery methods at the time of delivery. For example, the service provider can propose relevant experiences based on the user's interests and preferences on social media. For example, the service provider can propose relevant experiences based on the user's posts and the number of likes. The service provider can also analyze the user's social media activity history and propose experiences that the user has shown interest in in the past. For example, the service provider can propose relevant experiences based on the user's number of followers and comments. The service provider can also propose relevant experiences by referring to the activities of the user's friends on social media. For example, the service provider can propose experiences that the user's friends have shown interest in. In this way, by proposing delivery methods based on social media activity, a more appropriate experience can be provided. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity into AI and have the AI ​​execute the proposal of delivery methods.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The generation unit can analyze a user's past experience history and customize VR content based on that analysis. For example, it can suggest new experiences relevant to the user based on places the user has visited or events they have experienced in the past. Furthermore, if a user is interested in a particular theme, it can prioritize generating content related to that theme. It can also generate content tailored to specific seasons or events based on the user's past experience history. This allows for the provision of more personalized VR experiences by leveraging the user's past experience history.

[0052] The service provider can monitor the user's health status in real time and adjust the way VR content is delivered based on that information. For example, if the user's heart rate or blood pressure is high, it can provide relaxing content. If the user is tired, it can provide content that can be enjoyed in a short amount of time. Furthermore, if the user is in good health, it can provide an active experience. This allows for the provision of an optimal VR experience tailored to the user's health status.

[0053] The service provider can prioritize suggesting highly relevant experiences by considering the user's geographical location. For example, it can suggest tourist destinations or events near the user's current location. It can also prioritize easily accessible experiences based on the user's geographical location. Furthermore, it can utilize location-based services to filter and suggest relevant experiences. This allows for the provision of an optimal experience that takes the user's geographical location into consideration.

[0054] The reception desk can analyze users' social media activity and suggest relevant experiences based on that analysis. For example, it can suggest new experiences based on places and events that users have shown interest in on social media. It can also analyze users' posts and the number of likes they receive to prioritize suggesting relevant experiences. Furthermore, it can suggest relevant experiences based on experiences that users' friends have shown interest in. This allows the system to provide the most optimal experience based on users' social media activity.

[0055] The service delivery unit can refer to the user's past experience history and select the optimal delivery method based on that. For example, it can prioritize delivery methods that the user has preferred in the past. It can also suggest the optimal delivery method based on the user's past experience history. Furthermore, it is possible to analyze the user's past experience history and customize the delivery method. This allows for the provision of the optimal delivery method based on past experience history.

[0056] The generation unit can apply different generation algorithms depending on the category of the experience. For example, in the case of a travel experience, an algorithm that realistically reproduces tourist destinations and landmarks can be applied. In the case of a nature experience, an algorithm that realistically reproduces landscapes and flora and fauna can be applied. Furthermore, in the case of a history experience, it is possible to apply an algorithm that realistically reproduces historical buildings and events. This allows for the provision of the optimal generation algorithm according to the category of the experience.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The reception desk enters the user's desired experience. This could include, for example, travel experiences, sports experiences, or educational experiences. The user might enter a request such as, "I want to go to Paris on a family trip." Step 2: The generation unit analyzes the information entered by the reception unit and generates content to recreate the desired experience in VR. The generation unit uses generation AI to generate realistic VR content based on past travel data, photos, videos, etc. The generation AI uses text generation AI (e.g., LLM) to analyze the user's desired experience and generate VR content. The generation unit can also generate realistic VR content by making full use of 3D modeling and simulation technologies. Step 3: The provider unit provides the VR content generated by the generator unit to the user. The provider unit provides the generated content to the user through a VR headset. The user can wear the VR headset and experience the generated content. For example, the user can walk around the streets of Paris or look up at the Eiffel Tower. The provider unit can also provide the generated VR content to the user using robotic technology. For example, the provider unit can provide the VR content to the user using a robotic arm or a mobile robot.

[0059] (Example of form 2) The VR experience system according to an embodiment of the present invention is a system that recreates places and situations in VR that users would like to experience but are unable to do so otherwise. This VR experience system allows the elderly, people who cannot travel abroad for physical reasons, and their families to enjoy new experiences. Specifically, it consists of the following steps. First, the user inputs the experience they desire. For example, they might input a desire such as, "I want to go to Paris on a family trip." This information is input into the generating AI. Next, the generating AI analyzes the input information and generates content to recreate the desired experience in VR. The generating AI creates realistic VR content based on past travel data, photos, videos, etc. For example, it generates VR content that recreates the streets and tourist spots of Paris. The generated VR content is provided to the user using robotic technology. The user can wear a VR headset and experience the generated content. For example, they can walk around the streets of Paris or look up at the Eiffel Tower. This mechanism allows the elderly and people who cannot travel abroad for physical reasons to enjoy new experiences. It also enables family trips and the creation of memories. For example, by enjoying a VR trip together as a family, family bonds can be strengthened. Furthermore, by expanding rich content using generative AI, it becomes possible to offer it at a low cost. This enables individual rentals, short-term rentals, and provision to facilities, and is expected to penetrate a wide market. For example, with the cooperation of local governments, short-term experiences can be offered in shops. It is also possible to offer it as an experiential plan using hometown tax donations to connect regular supply and demand. This ensures regular demand and can meet continuous needs. In this way, by combining robotic technology and generative AI, it is possible to provide new experiences to the elderly and those who cannot try new experiences due to physical reasons, and improve their quality of life. As a result, the VR experience system can reproduce and provide the user's desired experience in VR.

[0060] The VR experience system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit inputs the user's desired experience. The user's desired experience includes, but is not limited to, travel experiences, sports experiences, and educational experiences. For example, the user can input a desire such as "I want to go to Paris on a family trip" into the reception unit. The generation unit analyzes the information input by the reception unit and generates content to recreate the desired experience in VR. For example, the generation unit uses a generation AI to generate realistic VR content based on past travel data, photos, videos, etc. The generation AI uses, for example, a text generation AI (e.g., LLM) to analyze the user's desired experience and generate VR content. The generation unit can also use the generation AI to generate realistic VR content by making full use of 3D modeling and simulation technologies. For example, the generation AI generates VR content that recreates the streets and tourist spots of Paris based on past travel data, photos, videos, etc. The provision unit provides the VR content generated by the generation unit to the user. For example, the provision unit provides the generated content to the user through a VR headset. Users can wear a VR headset and experience the generated content. For example, a user can walk around the streets of Paris or look up at the Eiffel Tower. The service provider can also use robotic technology to deliver the generated VR content to the user. For example, the service provider can use a robotic arm or a mobile robot to deliver the VR content to the user. In this way, the VR experience system according to the embodiment can reproduce and deliver the user's desired experience in VR.

[0061] The reception desk takes input from the user regarding their desired experience. This includes, but is not limited to, travel, sports, and educational experiences. For example, the reception desk can take input such as, "I want to go to Paris on a family trip." Specifically, the reception desk is designed to allow users to input details of their desired experience through a user interface. The user interface supports various input methods, including touchscreen, voice input, and keyboard input, and is designed for intuitive operation. For example, if a user desires a travel experience, they can input details such as the destination, duration of the trip, number of companions, and specific tourist spots. Furthermore, the reception desk learns the user's past experience history and preferences to suggest more personalized experiences. For example, it can suggest new experiences based on places the user has visited and activities they have been interested in in the past. This allows users to easily select experiences that suit their preferences. The reception desk also analyzes user input in real time and requests additional information as needed. For example, if a user desires to "visit museums in Paris," it can confirm details such as the names of specific museums and desired dates and times. This allows the reception department to accurately understand the user's desired experience and smoothly provide that information to the next generation department.

[0062] The generation unit analyzes the information entered by the reception unit and generates content to recreate the desired experience in VR. For example, the generation unit uses a generation AI to generate realistic VR content based on past travel data, photos, videos, etc. The generation AI uses a text generation AI (e.g., LLM) to analyze the user's desired experience and generate VR content. Specifically, the generation AI analyzes the text information entered by the user using natural language processing technology and extracts the necessary elements. For example, if the user enters the desire to "go to Paris on a family trip," the generation AI will extract keywords such as "family trip," "Paris," and "tourist destinations," and generate content based on them. The generation unit can also use the generation AI to generate realistic VR content by utilizing 3D modeling and simulation technologies. For example, the generation AI can generate VR content that recreates the streets and tourist destinations of Paris based on past travel data, photos, videos, etc. Specifically, it can accurately recreate major tourist spots such as the Eiffel Tower, the Louvre Museum, and the Champs-Élysées, providing the user with an experience as if they were actually visiting those places. Furthermore, the generation unit can create more personalized content by taking into account the user's preferences and past experience history. For example, it can add new experiential elements based on places the user has visited in the past and activities they have been interested in. This allows the generation unit to reproduce and deliver the user's desired experience in a highly accurate and personalized way.

[0063] The delivery unit provides the user with VR content generated by the generation unit. The delivery unit provides the generated content to the user, for example, through a VR headset. The user can wear the VR headset and experience the generated content. Specifically, the delivery unit provides an interface for the user to start the VR experience, and is designed to allow the user to start the experience with simple operations. For example, when the user puts on the VR headset, the automatically generated content is played, and the user can intuitively enjoy the experience. The user can walk around the streets of Paris or look up at the Eiffel Tower. The delivery unit can also provide the user with generated VR content using robotic technology. For example, the delivery unit can provide VR content to the user using a robotic arm or a mobile robot. Specifically, the robotic arm can adjust the position of the VR headset according to the user's movements, or the mobile robot can guide the user to a specific location. This allows the user to enjoy a more immersive experience. Furthermore, the delivery unit also has a function to collect user feedback and continuously improve the quality of the experience. For example, it can collect feedback on dissatisfaction and areas for improvement that the user felt during the experience and reflect them in the next experience. This allows the service provider to consistently deliver high-quality VR experiences to users.

[0064] The generation unit can generate realistic VR content based on past travel data, photos, videos, etc. For example, the generation unit can collect past travel data and generate VR content based on it. For example, the generation unit can recreate the scenery and tourist spots of a travel destination based on travel records and GPS data. The generation unit can also analyze past photos and videos and generate VR content based on them. For example, the generation unit can generate a realistic 3D model based on past travel photos. The generation unit can also generate realistic VR scenes based on past travel videos. For example, the generation unit can analyze travel videos and recreate the scenery and buildings in the video. In this way, realistic VR content can be generated based on past data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the generation unit can input past travel data, photos, and videos into a generation AI and have the generation AI execute the generation of realistic VR content.

[0065] The service provider can provide generated content to users through a VR headset. For example, the service provider can provide generated VR content to users using a VR headset. For example, the service provider can enable users to experience the generated VR content by having them wear a VR headset. The service provider can also select the optimal content delivery method depending on the type and specifications of the VR headset. For example, the service provider can provide content compatible with VR headsets such as Oculus Rift and HTC Vive. This allows the service provider to deliver content to users through a VR headset. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the generated VR content into a generation AI and have the generation AI select the optimal delivery method.

[0066] The generation unit can generate content to reproduce the desired experience using a generative AI. For example, the generation unit can use a generative AI to analyze the user's desired experience and generate VR content based on that analysis. The generative AI can use technologies such as GANs (Generative Opposite Networks) or reinforcement learning to generate realistic VR content. For example, the generative AI can receive the user's desired experience as input and generate VR content based on that. The generation unit can also use a generative AI to simulate the user's desired experience and generate VR content based on that simulation. For example, the generative AI can simulate the user's desired experience and generate VR content based on the results. This allows the generation unit to generate content to reproduce the desired experience using a generative AI. Some or all of the above-described processes in the generation unit may be performed using a generative AI or not. For example, the generation unit can input the user's desired experience into the generative AI and have the generative AI generate VR content.

[0067] The service provider can deliver the generated VR content to the user using robotic technology. For example, the service provider can deliver the generated VR content to the user using a robotic arm or a mobile robot. For example, the service provider can use a robotic arm to put a VR headset on the user. The service provider can also deliver the VR content to the user using a mobile robot. For example, the service provider can use a mobile robot to carry the VR headset to the user. In this way, VR content can be delivered using robotic technology. Some or all of the above-described processes in the service provider may be performed using a generation AI, or they may be performed without a generation AI. For example, the service provider can input robotic technology into a generation AI and have the generation AI select the optimal delivery method.

[0068] The generation unit can enhance rich content using a generation AI. For example, the generation unit uses the generation AI to generate rich content to improve the quality of VR content. The generation AI can generate rich content that includes, for example, high-resolution video and interactive elements. For example, the generation AI can generate high-resolution video based on the user's desired experience. The generation AI can also generate VR content that includes interactive elements. For example, the generation AI can add elements that allow the user to interact within the VR content. This allows for the enhancement of rich content using the generation AI. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit can have the generation AI perform the generation of rich content.

[0069] The reception unit can estimate the user's emotions and adjust the input method for the desired experience based on the estimated emotions. For example, the reception unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on changes in facial expressions. The reception unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice and calculate an emotion score. The reception unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on fluctuations in heart rate. This allows for a more appropriate experience to be provided by adjusting the input method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception desk can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0070] The reception desk can analyze a user's past experience history and suggest the most suitable experience. For example, the reception desk can collect a user's past experience history and suggest the most suitable experience based on it. For example, the reception desk can suggest new experiences related to places and situations the user has experienced in the past. The reception desk can also suggest experiences based on specific themes from a user's past experience history. For example, the reception desk can analyze a user's past experience history and suggest experiences tailored to the season or event. This allows the reception desk to suggest the most suitable experience based on past experience history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input a user's past experience history into an AI and have the AI ​​suggest the most suitable experience.

[0071] The reception desk can filter the user's desired experiences based on their current health status and interests. For example, the reception desk might prioritize suggesting experiences that do not require physical exertion, taking into account the user's health status. For instance, it might suggest relaxing experiences based on the user's heart rate and blood pressure. The reception desk can also filter and suggest relevant experiences based on the user's interests. For example, it might suggest experiences based on a specific theme. The reception desk can also suggest relaxing experiences based on the user's current health status. For example, it might suggest low-impact experiences based on the user's activity level. By filtering experiences based on the user's health status and interests, a more appropriate experience can be provided. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's health status and interests into an AI and have the AI ​​perform the experience filtering.

[0072] The reception unit can estimate the user's emotions and determine the priority of the input experience based on the estimated user emotions. For example, the reception unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on changes in facial expressions. The reception unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice and calculate an emotion score. The reception unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on fluctuations in heart rate. This allows for the provision of a more appropriate experience by determining the priority of experiences based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the AI ​​determine the priority of the user experience.

[0073] The reception desk can prioritize suggesting highly relevant experiences by considering the user's geographical location when the user inputs their desired experience. For example, the reception desk can suggest nearby tourist attractions or events based on the user's current location. For example, the reception desk can suggest nearby tourist attractions based on the user's GPS data. The reception desk can also filter and suggest relevant experiences based on the user's geographical location. For example, the reception desk can suggest relevant experiences based on the user's location-based services. The reception desk can also prioritize suggesting experiences that are easily accessible from the user's current location. For example, the reception desk can suggest tourist attractions close to the user's current location. By suggesting experiences while considering the user's geographical location, it is possible to provide more relevant experiences. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into AI and have the AI ​​suggest experiences.

[0074] The reception desk can analyze the user's social media activity when they input their desired experience and suggest relevant experiences. For example, the reception desk can suggest relevant experiences based on the user's interests and preferences on social media. For example, it can suggest relevant experiences based on the user's posts and the number of likes. The reception desk can also analyze the user's social media activity history and suggest experiences they have shown interest in in the past. For example, it can suggest relevant experiences based on the user's number of followers and comments. The reception desk can also suggest relevant experiences by referring to the activities of the user's friends on social media. For example, it can suggest experiences that the user's friends have shown interest in. In this way, by suggesting relevant experiences based on social media activity, more appropriate experiences can be provided. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity into AI and have the AI ​​perform experience suggestions.

[0075] The generation unit can estimate the user's emotions and adjust the way the generated VR content is presented based on the estimated emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on changes in facial expressions. The generation unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the voice and calculate an emotion score. The generation unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on fluctuations in heart rate. This allows for a more appropriate experience to be provided by adjusting the way the VR content is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the way the VR content is expressed.

[0076] The generation unit can adjust the level of detail of the VR content based on the importance of the experience during generation. For example, the generation unit can evaluate the importance of the experience and adjust the level of detail of the VR content accordingly. For example, in the case of an important experience, the generation unit can generate VR content that includes detailed scenes and high-resolution graphics. The generation unit can also generate VR content with a standard level of detail for a general experience. For example, the generation unit can generate VR content that includes graphics with a standard resolution. The generation unit can also generate VR content that includes only basic elements for a simple experience. For example, the generation unit can generate VR content that includes basic scenes and low-resolution graphics. This allows for a more appropriate experience to be provided by adjusting the level of detail of the VR content based on the importance of the experience. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the importance of the experience into the generation AI and have the generation AI perform the adjustment of the level of detail of the VR content.

[0077] The generation unit can apply different generation algorithms depending on the category of the experience during generation. For example, the generation unit evaluates the category of the experience and selects an appropriate generation algorithm based on that evaluation. For example, in the case of a travel experience, the generation unit can apply an algorithm that realistically reproduces tourist spots and landmarks. In the case of a nature experience, the generation unit can also apply an algorithm that realistically reproduces landscapes and flora and fauna. For example, the generation unit can select an algorithm for nature experiences and realistically reproduce landscapes and flora and fauna. In the case of a history experience, the generation unit can also apply an algorithm that realistically reproduces historical buildings and events. For example, the generation unit can select an algorithm for history experiences and realistically reproduce historical buildings and events. By applying different generation algorithms depending on the category of the experience, a more appropriate experience can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the category of the experience into the generation AI and have the generation AI select an appropriate generation algorithm.

[0078] The generation unit can estimate the user's emotions and adjust the length of the generated VR content based on the estimated emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on changes in facial expressions. The generation unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the voice and calculate an emotion score. The generation unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on fluctuations in heart rate. This allows for a more appropriate experience by adjusting the length of the VR content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the VR content.

[0079] The generation unit can determine the priority of VR content based on the submission date of the experience during generation. For example, the generation unit evaluates the submission date of the experience and determines the priority of VR content based on that. For example, the generation unit prioritizes generating the most recently submitted experience. The generation unit can also postpone older submitted experiences. For example, the generation unit adjusts the generation order based on the submission date. This allows for the provision of more appropriate experiences by prioritizing VR content based on the submission date of the experience. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the submission date of the experience into the generation AI and have the generation AI perform the determination of the VR content priority.

[0080] The generation unit can adjust the order of VR content based on the relevance of the experiences during generation. For example, the generation unit can evaluate the relevance of the experiences and adjust the order of the VR content based on that evaluation. For example, the generation unit can prioritize generating highly relevant experiences. The generation unit can also postpone less relevant experiences. For example, the generation unit adjusts the generation order based on relevance. By adjusting the order of VR content based on the relevance of the experiences, a more appropriate experience can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the relevance of the experiences into a generation AI and have the generation AI perform the adjustment of the order of the VR content.

[0081] The service provider can estimate the user's emotions and adjust the method of delivering VR content based on the estimated user emotions. For example, the service provider can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on changes in facial expressions. The service provider can also record the user's voice and estimate emotions using voice analysis technology. For example, the service provider can analyze the tone and speed of the voice and calculate an emotion score. The service provider can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on fluctuations in heart rate. This allows for a more appropriate experience to be provided by adjusting the method of delivering VR content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generating AI and have the AI ​​adjust the method of delivering VR content.

[0082] The service delivery unit can select the optimal delivery method by referring to the user's past experience history at the time of delivery. For example, the service delivery unit can collect the user's past experience history and select the optimal delivery method based on it. For example, the service delivery unit can prioritize selecting delivery methods that the user has preferred in the past. The service delivery unit can also suggest the optimal delivery method based on the user's past experience history. For example, the service delivery unit can analyze the user's past experience history and customize the delivery method. By selecting the optimal delivery method based on past experience history, a more appropriate experience can be provided. Some or all of the above processes in the service delivery unit may be performed using AI or not. For example, the service delivery unit can input the user's past experience history into AI and have the AI ​​perform the selection of the delivery method.

[0083] The service provider can customize the delivery method based on the user's current health condition at the time of delivery. For example, the service provider can evaluate the user's health condition and customize the delivery method based on that evaluation. For example, if the user is tired, the service provider can select a delivery method that promotes relaxation. Alternatively, if the user is healthy, the service provider can select an active delivery method. For example, based on the user's health condition, the service provider can provide an active experience. Alternatively, if the user is unwell, the service provider can select a delivery method that minimizes burden. For example, based on the user's health condition, the service provider can provide a relaxing experience. By customizing the delivery method based on the user's health condition, a more appropriate experience can be provided. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's health condition into AI and have AI perform the customization of the delivery method.

[0084] The service provider can estimate the user's emotions and determine the order in which VR content is delivered based on the estimated emotions. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on changes in facial expressions. The service provider can also record the user's voice and estimate their emotions using voice analysis technology. For example, the service provider can analyze the tone and speed of the voice and calculate an emotion score. The service provider can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on fluctuations in heart rate. This allows for a more appropriate experience to be provided by determining the order of delivery based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generating AI and have the AI ​​determine the order in which VR content should be provided.

[0085] The service provider can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, the service provider can suggest nearby tourist attractions or events based on the user's current location. For example, the service provider can suggest nearby tourist attractions based on the user's GPS data. The service provider can also filter and suggest relevant experiences based on the user's geographical location information. For example, the service provider can suggest relevant experiences based on the user's location-based services. The service provider can also prioritize suggesting experiences that are easily accessible from the user's current location. For example, the service provider can suggest tourist attractions close to the user's current location. By selecting a delivery method that takes geographical location information into account, a more appropriate experience can be provided. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's geographical location information into AI and have the AI ​​select the delivery method.

[0086] The service provider can analyze the user's social media activity and propose delivery methods at the time of delivery. For example, the service provider can propose relevant experiences based on the user's interests and preferences on social media. For example, the service provider can propose relevant experiences based on the user's posts and the number of likes. The service provider can also analyze the user's social media activity history and propose experiences that the user has shown interest in in the past. For example, the service provider can propose relevant experiences based on the user's number of followers and comments. The service provider can also propose relevant experiences by referring to the activities of the user's friends on social media. For example, the service provider can propose experiences that the user's friends have shown interest in. In this way, by proposing delivery methods based on social media activity, a more appropriate experience can be provided. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity into AI and have the AI ​​execute the proposal of delivery methods.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The reception desk can estimate the user's emotions and adjust the input method for the desired experience based on those estimates. For example, if the user is nervous, the reception desk can provide a relaxing interface. If the user is excited, it can provide an interface that prompts for more detailed input. Furthermore, if the user is sad, it can display comforting messages to support their input. This allows the system to provide the most appropriate input method according to the user's emotions.

[0089] The generation unit can analyze a user's past experience history and customize VR content based on that analysis. For example, it can suggest new experiences relevant to the user based on places the user has visited or events they have experienced in the past. Furthermore, if a user is interested in a particular theme, it can prioritize generating content related to that theme. It can also generate content tailored to specific seasons or events based on the user's past experience history. This allows for the provision of more personalized VR experiences by leveraging the user's past experience history.

[0090] The service provider can monitor the user's health status in real time and adjust the way VR content is delivered based on that information. For example, if the user's heart rate or blood pressure is high, it can provide relaxing content. If the user is tired, it can provide content that can be enjoyed in a short amount of time. Furthermore, if the user is in good health, it can provide an active experience. This allows for the provision of an optimal VR experience tailored to the user's health status.

[0091] The generation unit can estimate the user's emotions and adjust the way VR content is presented based on those estimated emotions. For example, if the user is happy, bright and cheerful expressions can be emphasized. If the user is depressed, comforting expressions can be incorporated. Furthermore, if the user is excited, stimulating expressions can be increased. This allows VR content to be delivered using the most appropriate expression method according to the user's emotions.

[0092] The service provider can prioritize suggesting highly relevant experiences by considering the user's geographical location. For example, it can suggest tourist destinations or events near the user's current location. It can also prioritize easily accessible experiences based on the user's geographical location. Furthermore, it can utilize location-based services to filter and suggest relevant experiences. This allows for the provision of an optimal experience that takes the user's geographical location into consideration.

[0093] The reception desk can analyze users' social media activity and suggest relevant experiences based on that analysis. For example, it can suggest new experiences based on places and events that users have shown interest in on social media. It can also analyze users' posts and the number of likes they receive to prioritize suggesting relevant experiences. Furthermore, it can suggest relevant experiences based on experiences that users' friends have shown interest in. This allows the system to provide the most optimal experience based on users' social media activity.

[0094] The generation unit can estimate the user's emotions and adjust the length of the VR content based on those emotions. For example, if the user is tired, it can provide content that can be enjoyed in a short amount of time. Conversely, if the user is excited, it can provide a longer experience. Furthermore, if the user is relaxed, it can provide content of an appropriate length. This allows for the provision of the optimal content length according to the user's emotions.

[0095] The service delivery unit can refer to the user's past experience history and select the optimal delivery method based on that. For example, it can prioritize delivery methods that the user has preferred in the past. It can also suggest the optimal delivery method based on the user's past experience history. Furthermore, it is possible to analyze the user's past experience history and customize the delivery method. This allows for the provision of the optimal delivery method based on past experience history.

[0096] The service provider can estimate the user's emotions and determine the order in which VR content is delivered based on those emotions. For example, if the user is excited, stimulating content can be prioritized. If the user is relaxed, calming content can be prioritized. Furthermore, if the user is sad, comforting content can be prioritized. This allows for the provision of the optimal order of content delivery according to the user's emotions.

[0097] The generation unit can apply different generation algorithms depending on the category of the experience. For example, in the case of a travel experience, an algorithm that realistically reproduces tourist destinations and landmarks can be applied. In the case of a nature experience, an algorithm that realistically reproduces landscapes and flora and fauna can be applied. Furthermore, in the case of a history experience, it is possible to apply an algorithm that realistically reproduces historical buildings and events. This allows for the provision of the optimal generation algorithm according to the category of the experience.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The reception desk enters the user's desired experience. This could include, for example, travel experiences, sports experiences, or educational experiences. The user might enter a request such as, "I want to go to Paris on a family trip." Step 2: The generation unit analyzes the information entered by the reception unit and generates content to recreate the desired experience in VR. The generation unit uses generation AI to generate realistic VR content based on past travel data, photos, videos, etc. The generation AI uses text generation AI (e.g., LLM) to analyze the user's desired experience and generate VR content. The generation unit can also generate realistic VR content by making full use of 3D modeling and simulation technologies. Step 3: The provider unit provides the VR content generated by the generator unit to the user. The provider unit provides the generated content to the user through a VR headset. The user can wear the VR headset and experience the generated content. For example, the user can walk around the streets of Paris or look up at the Eiffel Tower. The provider unit can also provide the generated VR content to the user using robotic technology. For example, the provider unit can provide the VR content to the user using a robotic arm or a mobile robot.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, which allows the user to input their desired experience. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates VR content using a generation AI. The provision unit is implemented, for example, by the output device 40 of the smart device 14, which provides the generated content to the user through a VR headset. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing the user to input their desired experience by voice. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, generating VR content using a generation AI. The provision unit is implemented by the speaker 240 of the smart glasses 214, providing the generated content to the user through a VR headset. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, allowing the user to input their desired experience by voice. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates VR content using a generation AI. The provision unit is implemented by, for example, the display 343 of the headset terminal 314, which provides the generated content to the user through the VR headset. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0150] 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.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0152] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, allowing the user to input their desired experience by voice. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates VR content using a generation AI. The provision unit is implemented by, for example, the speaker 240 and the controlled object 443 of the robot 414, which provides the generated content to the user through a VR headset. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] (Note 1) A reception desk where users input their desired experience, A generation unit analyzes the information entered by the reception unit and generates content to recreate the desired experience in VR, The system includes a providing unit that provides the VR content generated by the generation unit to the user. A system characterized by the following features. (Note 2) The generating unit is Create realistic VR content based on past travel data, photos, videos, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Provides users with generated content through a VR headset. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generative AI generates content to recreate desired experiences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, The generated VR content is delivered to the user using robotic technology. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Enhancing rich content through generative AI The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts how they input their desired experience based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the user's past experience history and suggest the optimal experience. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter their desired experiences, filtering is performed based on their current health status and interests. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the input experience based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users input their desired experiences, the system prioritizes suggesting highly relevant experiences by taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users input their desired experiences, the system analyzes their social media activity and suggests relevant experiences. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the way VR content is generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the level of detail in VR content is adjusted based on the importance of the experience. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, different generation algorithms are applied depending on the experience category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the generated VR content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, VR content is prioritized based on when the experience was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the order of VR content is adjusted based on the relevance of the experience. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts how VR content is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the user's past experience history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, the delivery method will be customized based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates the user's emotions and determines the order in which VR content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and propose a delivery method. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0172] 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 reception desk where users input their desired experience, A generation unit analyzes the information entered by the reception unit and generates content to recreate the desired experience in VR, The system includes a providing unit that provides the VR content generated by the generation unit to the user. A system characterized by the following features.

2. The generating unit is Create realistic VR content based on past travel data, photos, videos, etc. The system according to feature 1.

3. The aforementioned supply unit is, Provides users with generated content through a VR headset. The system according to feature 1.

4. The generating unit is Generative AI generates content to recreate desired experiences. The system according to feature 1.

5. The aforementioned supply unit is, The generated VR content is delivered to the user using robotic technology. The system according to feature 1.

6. The generating unit is Enhancing rich content through generative AI The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts how they input their desired experience based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze the user's past experience history and suggest the optimal experience. The system according to feature 1.

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