system

Abstract

The system according to this embodiment aims to allow users to obtain a personalized virtual travel experience without leaving their homes. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives virtual travel requests from users. The generation unit analyzes the user's interests and past travel history based on the requests received by the reception unit and generates an optimal travel plan. The provision unit provides a real-time travel experience using VR technology based on the travel plan generated by the generation unit.

Description

Technical Field

[0006] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for a user to obtain a personalized virtual travel experience without leaving home.

[0005] The system according to the embodiment aims to enable a user to obtain a personalized virtual travel experience without leaving home.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives virtual travel requests from users. Based on the requests received by the reception unit, the generation unit analyzes the user's interests and past travel history and generates an optimal travel plan. Based on the travel plan generated by the generation unit, the provision unit provides a real-time travel experience using VR technology. [Effects of the Invention]

[0007] The system according to this embodiment allows users to obtain a personalized virtual travel experience without leaving their homes. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The virtual travel system according to an embodiment of the present invention is a system that allows users to virtually travel to various parts of the world from their homes. This virtual travel system analyzes the user's interests and past travel history to provide a personalized travel experience. For example, the user enters a request to start a virtual trip. For example, the user might enter a request such as, "I want to see the Eiffel Tower in Paris." This request is input into a generating AI. Next, the generating AI analyzes the input request and generates an optimal travel plan based on the user's interests and past travel history. For example, if the user has shown interest in French cuisine in the past, a travel plan including famous French restaurants near the Eiffel Tower will be generated. Based on the generated travel plan, a real-time travel experience is provided using VR technology. The user can wear a VR headset and virtually visit the Eiffel Tower. Furthermore, a travel experience customized to the user's preferences is provided. For example, if the user likes visiting museums, a plan to visit the Louvre Museum after the Eiffel Tower will be provided. This allows users to virtually travel to various parts of the world without leaving their homes. This alleviates frustration caused by travel restrictions and fulfills the longing for travel. Furthermore, the spread of VR technology and market expansion are expected. For example, it is expected to attract new customer segments to the travel industry and improve user satisfaction. This will allow the virtual travel system to analyze users' interests and past travel history and provide personalized travel experiences.

[0029] The virtual travel system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives virtual travel requests from users. The reception unit receives, for example, a request from the user to start a virtual trip. For example, a user may enter a request such as, "I want to see the Eiffel Tower in Paris." The generation unit analyzes the user's interests and past travel history based on the request received by the reception unit and generates an optimal travel plan. For example, if the user has shown interest in French cuisine in the past, the generation unit will generate a travel plan that includes famous French restaurants around the Eiffel Tower. The generation unit uses a generation AI to analyze the user's interests and past travel history and generates an optimal travel plan. For example, the generation AI takes the user's past travel history and interests as input and outputs an optimal travel plan. The provision unit provides a real-time travel experience using VR technology based on the travel plan generated by the generation unit. For example, the provision unit allows the user to wear a VR headset and virtually visit the Eiffel Tower. The provision unit provides a travel experience customized to the user's preferences based on the generated travel plan. The service provider, for example, if a user prefers visiting art museums, can provide a plan that includes visiting the Louvre Museum after the Eiffel Tower. This allows the virtual travel system, according to the embodiment, to receive and analyze the user's virtual travel request, generate an optimal travel plan, and provide a real-time travel experience.

[0030] The reception desk receives virtual travel requests from users. Users can enter their virtual travel requests through a dedicated application or website. For example, a user can enter a specific request such as "I want to see the Eiffel Tower in Paris." The reception desk not only receives user requests but also collects data such as user profile information, past travel history, and interests. This allows the reception desk to gather basic data to provide customized travel experiences based on the user's preferences and desires. Furthermore, when a user enters a request, the reception desk can use voice input and natural language processing technology to accurately understand the user's intent and process it as an appropriate request. For example, if a user requests by voice, "I want to see the Eiffel Tower at night," the reception desk can accurately analyze that request and transmit it to the generation unit. This allows the reception desk to flexibly respond to a variety of user requests and meet user expectations.

[0031] The generation unit analyzes the user's interests and past travel history based on requests received by the reception unit and generates the optimal travel plan. The generation unit uses a generation AI to analyze the user's interests and past travel history and generate the optimal travel plan. For example, the generation AI takes the user's past travel history and interests as input and outputs the optimal travel plan. Specifically, the generation AI analyzes data such as places the user has visited in the past, activities they have shown interest in, and their food preferences to propose the optimal travel plan. For example, if the user has shown interest in French cuisine in the past, it will generate a travel plan that includes famous French restaurants near the Eiffel Tower. Furthermore, the generation AI can also consider factors such as the user's current mood, the season, and the weather to generate the optimal travel plan. For example, if the user wants to relax, it can suggest a plan that includes quiet cafes and parks. In addition, the generation unit can provide more accurate travel plans by collecting user feedback and continuously improving the generation AI's algorithm. This allows the generation unit to generate customized travel plans based on the user's interests and desires, providing the user with a satisfying virtual travel experience.

[0032] The service provider uses VR technology to deliver a real-time travel experience based on the travel plan generated by the generation unit. For example, the service provider allows a user to virtually visit the Eiffel Tower by wearing a VR headset. Based on the generated travel plan, the service provider provides a travel experience customized to the user's preferences. Specifically, when a user visits the Eiffel Tower, the service provider realistically reproduces the surrounding scenery, sounds, and atmosphere, providing the user with an experience as if they were actually there. Furthermore, the service provider allows users to enjoy interactive elements during their virtual trip. For example, when a user climbs to the observation deck of the Eiffel Tower, information about surrounding tourist attractions and historical background can be displayed. Social features can also be provided to allow users to interact with other users during their virtual trip. This allows the service provider to provide users with a real-time, immersive virtual travel experience, increasing user satisfaction. In addition, the service provider can collect user feedback and continuously improve the quality of the travel experience it provides. This allows the service provider to always provide users with the latest and best virtual travel experience.

[0033] The generation unit can analyze the user's interests and past travel history to generate an optimal travel plan. For example, the generation unit can identify the user's interests from past behavioral history and survey results. The generation unit can also obtain the user's past travel history from destinations, travel dates, and travel purposes. Based on the user's interests and past travel history, the generation unit generates an optimal travel plan. For example, it can generate a plan that includes places the user has visited in the past and their favorite activities. In this way, the generation unit can generate an optimal travel plan by analyzing the user's interests and past travel history. 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 generate a travel plan using a generation AI model that takes the user's interests and past travel history as input and outputs an optimal travel plan.

[0034] The service provider can provide a real-time travel experience using VR technology based on the generated travel plan. For example, the service provider can allow a user to virtually visit the Eiffel Tower by wearing a VR headset. Based on the generated travel plan, the service provider can provide a travel experience customized to the user's preferences. For example, if the user prefers visiting museums, the service provider can provide a plan to visit the Louvre Museum after the Eiffel Tower. In this way, the service provider can provide a real-time travel experience using VR technology based on the generated travel plan. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider can provide a travel experience using a generative AI model that takes a generated travel plan as input and outputs a real-time travel experience.

[0035] The service provider can offer a travel experience customized to the user's preferences. For example, if the user enjoys visiting art museums, the service provider can offer a plan to visit art museums. If the user enjoys nature, the service provider can offer a plan to visit nature parks. Furthermore, if the user enjoys visiting historical sites, the service provider can offer a plan to visit historical landmarks. In this way, the service provider can offer a travel experience customized to the user's preferences. Some or all of the above processing in the service provider may be performed using generative AI, or not. For example, the service provider can offer a travel experience using a generative AI model that takes the user's preferences as input and outputs a customized travel experience.

[0036] The service provider can enable users to virtually travel around the world without leaving their homes. For example, the service provider can enable users to virtually visit the Eiffel Tower by wearing a VR headset. The service provider can also enable users to virtually travel around the world without leaving their homes. For example, the service provider can enable users to virtually visit the Statue of Liberty in New York by wearing a VR headset. Furthermore, the service provider can enable users to virtually visit Senso-ji Temple in Tokyo without leaving their homes. In this way, the service provider can enable users to virtually travel around the world without leaving their homes. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider can provide a travel experience using a generative AI model that takes a user's request as input and outputs a virtual travel experience.

[0037] The reception unit can analyze the user's past request history and select the optimal reception method. For example, the reception unit may prioritize suggesting request methods that the user has frequently used in the past. For example, the reception unit may suggest the optimal reception method for a specific time period based on the user's past request history. For example, the reception unit may analyze the user's past request history and select the most efficient reception method. In this way, the reception unit can select the optimal reception method by analyzing the user's past request history. Some or all of the above processing in the reception unit may be performed using generative AI, or not. For example, the reception unit can select a reception method using a generative AI model that takes the user's past request history as input and outputs the optimal reception method.

[0038] The reception unit can filter requests based on the user's current interests and areas of interest when receiving them. For example, the reception unit can filter requests based on themes the user is currently interested in. For example, the reception unit can prioritize requests related to the user's areas of interest. For example, the reception unit can suggest highly relevant requests based on the user's current interests. In this way, the reception unit can receive highly relevant requests by filtering requests based on the user's current interests and areas of interest. Some or all of the above processing in the reception unit may be performed using generative AI, or not. For example, the reception unit can filter requests using a generative AI model that takes the user's current interests and areas of interest as input and outputs filtered requests.

[0039] The reception unit can prioritize receiving requests that are highly relevant, taking into account the user's geographical location information. For example, the reception unit prioritizes receiving requests related to the user's current location. For example, the reception unit suggests the most relevant requests based on the user's geographical location information. For example, the reception unit prioritizes receiving requests that include events or locations related to the user's current location. In this way, the reception unit can prioritize receiving requests that are highly relevant by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using generative AI, or not. For example, the reception unit can receive requests using a generative AI model that takes the user's geographical location information as input and outputs highly relevant requests.

[0040] The reception unit can analyze the user's social media activity when receiving a request and accept relevant requests. For example, the reception unit can analyze the content of the user's social media posts and suggest relevant requests. For example, the reception unit can filter requests based on the user's interests and preferences on social media. For example, the reception unit can accept the most suitable requests based on the user's social media activity history. In this way, the reception unit can accept relevant requests by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using generative AI, or not. For example, the reception unit can accept requests using a generative AI model that takes the user's social media activity as input and outputs relevant requests.

[0041] The generation unit can generate the optimal travel plan by referring to the user's past travel history when generating a travel plan. For example, the generation unit can suggest new places related to the user's past visits. For example, the generation unit can generate a plan that includes the user's preferred activities from the user's past travel history. For example, the generation unit can analyze the user's past travel history and generate the plan that will provide the highest level of satisfaction. In this way, the generation unit can generate the optimal travel plan by referring to the user's past travel history. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the generation unit can generate a travel plan using a generation AI model that takes the user's past travel history as input and outputs the optimal travel plan.

[0042] The generation unit can customize travel plans based on the user's current interests and areas of interest when generating them. For example, the generation unit can customize travel plans based on themes the user is currently interested in. For example, the generation unit can generate plans that include activities related to the user's areas of interest. For example, the generation unit can generate plans that visit highly relevant places based on the user's current interests. In this way, the generation unit can generate more relevant travel plans by customizing them based on the user's current interests and areas of interest. 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 generate travel plans using a generation AI model that takes the user's current interests and areas of interest as input and outputs a customized travel plan.

[0043] The generation unit can generate the optimal travel plan by considering the user's geographical location information when generating a travel plan. For example, the generation unit can generate a travel plan related to the user's current location. For example, the generation unit can suggest the optimal travel destination based on the user's geographical location information. For example, the generation unit can generate a plan that includes events and places related to the user's current location. In this way, the generation unit can generate a more relevant travel plan by considering the user's geographical location information when generating a travel plan. 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 generate a travel plan using a generation AI model that takes the user's geographical location information as input and outputs the optimal travel plan.

[0044] The generation unit can analyze the user's social media activity and customize the travel plan when generating it. For example, the generation unit can analyze the user's social media posts and suggest a relevant travel plan. For example, the generation unit can customize the plan based on the user's interests and preferences on social media. For example, the generation unit can generate the optimal travel plan based on the user's social media activity history. In this way, the generation unit can generate a more relevant travel plan by analyzing the user's social media activity. 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 generate a travel plan using a generation AI model that takes the user's social media activity as input and outputs a customized travel plan.

[0045] The service provider can provide the optimal travel experience by referring to the user's past travel history when providing a travel experience. For example, the service provider can suggest new places related to the user based on places the user has visited in the past. For example, the service provider can provide an experience that includes the user's preferred activities based on the user's past travel history. For example, the service provider can analyze the user's past travel history and provide the most satisfying experience. In this way, the service provider can provide the optimal travel experience by referring to the user's past travel history. Some or all of the above processing in the service provider may be performed using generative AI, or not. For example, the service provider can provide a travel experience using a generative AI model that takes the user's past travel history as input and outputs the optimal travel experience.

[0046] The service provider can customize the travel experience based on the user's current interests and areas of interest when providing the travel experience. For example, the service provider can customize the travel experience based on themes the user is currently interested in. For example, the service provider can provide an experience that includes activities related to the user's areas of interest. For example, the service provider can provide an experience that visits highly relevant places based on the user's current interests. In this way, the service provider can provide a more relevant travel experience by customizing the experience based on the user's current interests and areas of interest. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider can provide a travel experience using a generative AI model that takes the user's current interests and areas of interest as input and outputs a customized travel experience.

[0047] The service provider can provide the optimal travel experience by considering the user's geographical location information when providing travel experiences. For example, the service provider can provide travel experiences related to the user's current location. For example, the service provider can suggest the optimal travel destination based on the user's geographical location information. For example, the service provider can provide experiences that include events and places related to the user's current location. In this way, the service provider can provide more relevant travel experiences by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using generative AI, or not. For example, the service provider can provide travel experiences using a generative AI model that takes the user's geographical location information as input and outputs the optimal travel experience.

[0048] The service provider can analyze the user's social media activity and customize the travel experience when providing it. For example, the service provider can analyze the user's social media posts and suggest relevant travel experiences. For example, the service provider can customize the experience based on the user's interests and preferences on social media. For example, the service provider can provide the optimal travel experience based on the user's social media activity history. In this way, the service provider can provide a more relevant travel experience by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using generative AI, or not. For example, the service provider can provide a travel experience using a generative AI model that takes the user's social media activity as input and outputs a customized travel experience.

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

[0050] The reception desk can monitor the user's health status and adjust the acceptance of virtual travel requests accordingly. For example, if the user is feeling unwell, it can prioritize suggesting relaxing travel plans. If the user is in good health, it can suggest more active travel plans. It can also adjust the content of the travel plan based on the user's health status. For example, if the user is tired, it can suggest a short, enjoyable travel plan. In this way, the reception desk can provide the optimal travel plan according to the user's health condition.

[0051] The generation unit can customize travel plans based on the user's hobbies and skills. For example, if the user enjoys photography, it can generate a travel plan that includes spots suitable for photography. If the user enjoys cooking, it can generate a travel plan that includes local cooking classes. Furthermore, if the user's skill lies in sports, it can generate a travel plan that includes sports events and activities. This allows the generation unit to provide more personalized travel plans based on the user's hobbies and skills.

[0052] The service provider can collect real-time feedback from users during their travel experience and dynamically adjust travel plans. For example, if a user likes a particular place, they can extend their stay there. If a user shows no interest in a particular activity, alternative activities can be suggested. Furthermore, based on user feedback, future travel plans can be more appropriately customized. This allows the service provider to offer a more flexible travel experience that reflects real-time user feedback.

[0053] The service provider can offer features that allow users to share their travel experiences with other users. For example, it can provide a function that allows users to post photos and videos taken during their virtual trip to social media. It can also provide a function that allows users to stream their travel experience in real time. Furthermore, it can provide a function that allows users to enjoy virtual trips together with other users. In this way, the service provider can expand the enjoyment of travel experiences by sharing users' travel experiences with other users.

[0054] The service provider can offer users information about local culture and history during their travel experience. For example, it can introduce the historical background and cultural anecdotes of the places the user visits. It can also provide information about the local language and customs. Furthermore, if the user is interested, it can offer a feature that allows them to virtually interact with a local guide. In this way, the service provider can make the user's travel experience deeper and more educational.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The reception desk receives the user's virtual travel request. For example, the user can enter a request such as "I want to see the Eiffel Tower in Paris." Step 2: The generation unit analyzes the user's interests and past travel history based on the request received by the reception unit and generates the optimal travel plan. For example, if the user has previously shown interest in French cuisine, the generation unit will generate a travel plan that includes famous French restaurants around the Eiffel Tower. The generation unit uses a generation AI to analyze the user's interests and past travel history and generate the optimal travel plan. The generation AI takes the user's past travel history and interests as input and outputs the optimal travel plan. Step 3: The service provider uses VR technology to deliver a real-time travel experience based on the travel plan generated by the generation unit. For example, the user can wear a VR headset and virtually visit the Eiffel Tower. Based on the generated travel plan, the service provider provides a travel experience customized to the user's preferences. For example, if the user prefers visiting museums, the service provider will offer a plan to visit the Louvre Museum after the Eiffel Tower.

[0057] (Example of form 2) The virtual travel system according to an embodiment of the present invention is a system that allows users to virtually travel to various parts of the world from their homes. This virtual travel system analyzes the user's interests and past travel history to provide a personalized travel experience. For example, the user enters a request to start a virtual trip. For example, the user might enter a request such as, "I want to see the Eiffel Tower in Paris." This request is input into a generating AI. Next, the generating AI analyzes the input request and generates an optimal travel plan based on the user's interests and past travel history. For example, if the user has shown interest in French cuisine in the past, a travel plan including famous French restaurants near the Eiffel Tower will be generated. Based on the generated travel plan, a real-time travel experience is provided using VR technology. The user can wear a VR headset and virtually visit the Eiffel Tower. Furthermore, a travel experience customized to the user's preferences is provided. For example, if the user likes visiting museums, a plan to visit the Louvre Museum after the Eiffel Tower will be provided. This allows users to virtually travel to various parts of the world without leaving their homes. This alleviates frustration caused by travel restrictions and fulfills the longing for travel. Furthermore, the spread of VR technology and market expansion are expected. For example, it is expected to attract new customer segments to the travel industry and improve user satisfaction. This will allow the virtual travel system to analyze users' interests and past travel history and provide personalized travel experiences.

[0058] The virtual travel system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives virtual travel requests from users. The reception unit receives, for example, a request from the user to start a virtual trip. For example, a user may enter a request such as, "I want to see the Eiffel Tower in Paris." The generation unit analyzes the user's interests and past travel history based on the request received by the reception unit and generates an optimal travel plan. For example, if the user has shown interest in French cuisine in the past, the generation unit will generate a travel plan that includes famous French restaurants around the Eiffel Tower. The generation unit uses a generation AI to analyze the user's interests and past travel history and generates an optimal travel plan. For example, the generation AI takes the user's past travel history and interests as input and outputs an optimal travel plan. The provision unit provides a real-time travel experience using VR technology based on the travel plan generated by the generation unit. For example, the provision unit allows the user to wear a VR headset and virtually visit the Eiffel Tower. The provision unit provides a travel experience customized to the user's preferences based on the generated travel plan. The service provider, for example, if a user prefers visiting art museums, can provide a plan that includes visiting the Louvre Museum after the Eiffel Tower. This allows the virtual travel system, according to the embodiment, to receive and analyze the user's virtual travel request, generate an optimal travel plan, and provide a real-time travel experience.

[0059] The reception desk receives virtual travel requests from users. Users can enter their virtual travel requests through a dedicated application or website. For example, a user can enter a specific request such as "I want to see the Eiffel Tower in Paris." The reception desk not only receives user requests but also collects data such as user profile information, past travel history, and interests. This allows the reception desk to gather basic data to provide customized travel experiences based on the user's preferences and desires. Furthermore, when a user enters a request, the reception desk can use voice input and natural language processing technology to accurately understand the user's intent and process it as an appropriate request. For example, if a user requests by voice, "I want to see the Eiffel Tower at night," the reception desk can accurately analyze that request and transmit it to the generation unit. This allows the reception desk to flexibly respond to a variety of user requests and meet user expectations.

[0060] The generation unit analyzes the user's interests and past travel history based on requests received by the reception unit and generates the optimal travel plan. The generation unit uses a generation AI to analyze the user's interests and past travel history and generate the optimal travel plan. For example, the generation AI takes the user's past travel history and interests as input and outputs the optimal travel plan. Specifically, the generation AI analyzes data such as places the user has visited in the past, activities they have shown interest in, and their food preferences to propose the optimal travel plan. For example, if the user has shown interest in French cuisine in the past, it will generate a travel plan that includes famous French restaurants near the Eiffel Tower. Furthermore, the generation AI can also consider factors such as the user's current mood, the season, and the weather to generate the optimal travel plan. For example, if the user wants to relax, it can suggest a plan that includes quiet cafes and parks. In addition, the generation unit can provide more accurate travel plans by collecting user feedback and continuously improving the generation AI's algorithm. This allows the generation unit to generate customized travel plans based on the user's interests and desires, providing the user with a satisfying virtual travel experience.

[0061] The service provider uses VR technology to deliver a real-time travel experience based on the travel plan generated by the generation unit. For example, the service provider allows a user to virtually visit the Eiffel Tower by wearing a VR headset. Based on the generated travel plan, the service provider provides a travel experience customized to the user's preferences. Specifically, when a user visits the Eiffel Tower, the service provider realistically reproduces the surrounding scenery, sounds, and atmosphere, providing the user with an experience as if they were actually there. Furthermore, the service provider allows users to enjoy interactive elements during their virtual trip. For example, when a user climbs to the observation deck of the Eiffel Tower, information about surrounding tourist attractions and historical background can be displayed. Social features can also be provided to allow users to interact with other users during their virtual trip. This allows the service provider to provide users with a real-time, immersive virtual travel experience, increasing user satisfaction. In addition, the service provider can collect user feedback and continuously improve the quality of the travel experience it provides. This allows the service provider to always provide users with the latest and best virtual travel experience.

[0062] The generation unit can analyze the user's interests and past travel history to generate an optimal travel plan. For example, the generation unit can identify the user's interests from past behavioral history and survey results. The generation unit can also obtain the user's past travel history from destinations, travel dates, and travel purposes. Based on the user's interests and past travel history, the generation unit generates an optimal travel plan. For example, it can generate a plan that includes places the user has visited in the past and their favorite activities. In this way, the generation unit can generate an optimal travel plan by analyzing the user's interests and past travel history. 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 generate a travel plan using a generation AI model that takes the user's interests and past travel history as input and outputs an optimal travel plan.

[0063] The service provider can provide a real-time travel experience using VR technology based on the generated travel plan. For example, the service provider can allow a user to virtually visit the Eiffel Tower by wearing a VR headset. Based on the generated travel plan, the service provider can provide a travel experience customized to the user's preferences. For example, if the user prefers visiting museums, the service provider can provide a plan to visit the Louvre Museum after the Eiffel Tower. In this way, the service provider can provide a real-time travel experience using VR technology based on the generated travel plan. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider can provide a travel experience using a generative AI model that takes a generated travel plan as input and outputs a real-time travel experience.

[0064] The service provider can offer a travel experience customized to the user's preferences. For example, if the user enjoys visiting art museums, the service provider can offer a plan to visit art museums. If the user enjoys nature, the service provider can offer a plan to visit nature parks. Furthermore, if the user enjoys visiting historical sites, the service provider can offer a plan to visit historical landmarks. In this way, the service provider can offer a travel experience customized to the user's preferences. Some or all of the above processing in the service provider may be performed using generative AI, or not. For example, the service provider can offer a travel experience using a generative AI model that takes the user's preferences as input and outputs a customized travel experience.

[0065] The service provider can enable users to virtually travel around the world without leaving their homes. For example, the service provider can enable users to virtually visit the Eiffel Tower by wearing a VR headset. The service provider can also enable users to virtually travel around the world without leaving their homes. For example, the service provider can enable users to virtually visit the Statue of Liberty in New York by wearing a VR headset. Furthermore, the service provider can enable users to virtually visit Senso-ji Temple in Tokyo without leaving their homes. In this way, the service provider can enable users to virtually travel around the world without leaving their homes. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider can provide a travel experience using a generative AI model that takes a user's request as input and outputs a virtual travel experience.

[0066] The reception desk can estimate the user's emotions and adjust the timing of accepting virtual travel requests based on the estimated emotions. For example, if the user is stressed, the reception desk will accept the request during a time when the user can relax. For example, if the user is excited, the reception desk will accept the request immediately and provide a quick travel experience. For example, if the user is tired, the reception desk will adjust the timing to accept the request after the user has rested. In this way, the reception desk can accept requests at a more appropriate time by adjusting the timing of accepting virtual travel requests 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 desk may be performed using generative AI or not. For example, the reception desk can adjust the timing of acceptance using a generative AI model that takes user emotion data as input and outputs the timing of acceptance.

[0067] The reception unit can analyze the user's past request history and select the optimal reception method. For example, the reception unit may prioritize suggesting request methods that the user has frequently used in the past. For example, the reception unit may suggest the optimal reception method for a specific time period based on the user's past request history. For example, the reception unit may analyze the user's past request history and select the most efficient reception method. In this way, the reception unit can select the optimal reception method by analyzing the user's past request history. Some or all of the above processing in the reception unit may be performed using generative AI, or not. For example, the reception unit can select a reception method using a generative AI model that takes the user's past request history as input and outputs the optimal reception method.

[0068] The reception unit can filter requests based on the user's current interests and areas of interest when receiving them. For example, the reception unit can filter requests based on themes the user is currently interested in. For example, the reception unit can prioritize requests related to the user's areas of interest. For example, the reception unit can suggest highly relevant requests based on the user's current interests. In this way, the reception unit can receive highly relevant requests by filtering requests based on the user's current interests and areas of interest. Some or all of the above processing in the reception unit may be performed using generative AI, or not. For example, the reception unit can filter requests using a generative AI model that takes the user's current interests and areas of interest as input and outputs filtered requests.

[0069] The reception unit can estimate the user's emotions and determine the priority of requests to be received based on the estimated emotions. For example, if the user is excited, the reception unit will receive the request with the highest priority. If the user is relaxed, the reception unit will receive the request with the normal priority. If the user is stressed, the reception unit will receive the request with priority and provide a relaxing experience. In this way, the reception unit can prioritize requests that are more appropriate by determining the priority of requests 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 generative AI or not. For example, the reception unit can determine the priority using a generative AI model that takes user emotion data as input and outputs the priority of requests.

[0070] The reception unit can prioritize receiving requests that are highly relevant, taking into account the user's geographical location information. For example, the reception unit prioritizes receiving requests related to the user's current location. For example, the reception unit suggests the most relevant requests based on the user's geographical location information. For example, the reception unit prioritizes receiving requests that include events or locations related to the user's current location. In this way, the reception unit can prioritize receiving requests that are highly relevant by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using generative AI, or not. For example, the reception unit can receive requests using a generative AI model that takes the user's geographical location information as input and outputs highly relevant requests.

[0071] The reception unit can analyze the user's social media activity when receiving a request and accept relevant requests. For example, the reception unit can analyze the content of the user's social media posts and suggest relevant requests. For example, the reception unit can filter requests based on the user's interests and preferences on social media. For example, the reception unit can accept the most suitable requests based on the user's social media activity history. In this way, the reception unit can accept relevant requests by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using generative AI, or not. For example, the reception unit can accept requests using a generative AI model that takes the user's social media activity as input and outputs relevant requests.

[0072] The generation unit can estimate the user's emotions and adjust the travel plan generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit will generate a travel plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit will generate a plan that visits many places in a short amount of time. If the user is excited, the generation unit will generate a plan that includes exciting activities. In this way, the generation unit can generate a more appropriate travel plan by adjusting the travel plan generation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI or not. For example, the generation unit can adjust the generation method using a generative AI model that takes user emotion data as input and outputs a travel plan generation method.

[0073] The generation unit can generate the optimal travel plan by referring to the user's past travel history when generating a travel plan. For example, the generation unit can suggest new places related to the user's past visits. For example, the generation unit can generate a plan that includes the user's preferred activities from the user's past travel history. For example, the generation unit can analyze the user's past travel history and generate the plan that will provide the highest level of satisfaction. In this way, the generation unit can generate the optimal travel plan by referring to the user's past travel history. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the generation unit can generate a travel plan using a generation AI model that takes the user's past travel history as input and outputs the optimal travel plan.

[0074] The generation unit can customize travel plans based on the user's current interests and areas of interest when generating them. For example, the generation unit can customize travel plans based on themes the user is currently interested in. For example, the generation unit can generate plans that include activities related to the user's areas of interest. For example, the generation unit can generate plans that visit highly relevant places based on the user's current interests. In this way, the generation unit can generate more relevant travel plans by customizing them based on the user's current interests and areas of interest. 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 generate travel plans using a generation AI model that takes the user's current interests and areas of interest as input and outputs a customized travel plan.

[0075] The generation unit can estimate the user's emotions and determine the priority of the travel plan to generate based on the estimated user emotions. For example, if the user is excited, the generation unit will generate a plan that prioritizes stimulating activities. For example, if the user is relaxed, the generation unit will generate a plan that prioritizes relaxing activities. For example, if the user is stressed, the generation unit will generate a plan that prioritizes visits to relaxing places. In this way, the generation unit can generate a more appropriate travel plan by determining the priority of the travel plan based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI or not. For example, the generation unit can determine the priority using a generative AI model that takes user emotion data as input and outputs the priority of the travel plan.

[0076] The generation unit can generate the optimal travel plan by considering the user's geographical location information when generating a travel plan. For example, the generation unit can generate a travel plan related to the user's current location. For example, the generation unit can suggest the optimal travel destination based on the user's geographical location information. For example, the generation unit can generate a plan that includes events and places related to the user's current location. In this way, the generation unit can generate a more relevant travel plan by considering the user's geographical location information when generating a travel plan. 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 generate a travel plan using a generation AI model that takes the user's geographical location information as input and outputs the optimal travel plan.

[0077] The generation unit can analyze the user's social media activity and customize the travel plan when generating it. For example, the generation unit can analyze the user's social media posts and suggest a relevant travel plan. For example, the generation unit can customize the plan based on the user's interests and preferences on social media. For example, the generation unit can generate the optimal travel plan based on the user's social media activity history. In this way, the generation unit can generate a more relevant travel plan by analyzing the user's social media activity. 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 generate a travel plan using a generation AI model that takes the user's social media activity as input and outputs a customized travel plan.

[0078] The service provider can estimate the user's emotions and adjust how the travel experience is delivered based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a travel experience that proceeds at a leisurely pace. For example, if the user is in a hurry, the service provider can provide an experience that visits many places in a short amount of time. For example, if the user is excited, the service provider can provide an experience that includes exciting activities. In this way, the service provider can provide a more appropriate travel experience by adjusting how the travel experience is delivered 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 generative AI or not. For example, the service provider can adjust the delivery method using a generative AI model that takes user emotion data as input and outputs a method for delivering the travel experience.

[0079] The service provider can provide the optimal travel experience by referring to the user's past travel history when providing a travel experience. For example, the service provider can suggest new places related to the user based on places the user has visited in the past. For example, the service provider can provide an experience that includes the user's preferred activities based on the user's past travel history. For example, the service provider can analyze the user's past travel history and provide the most satisfying experience. In this way, the service provider can provide the optimal travel experience by referring to the user's past travel history. Some or all of the above processing in the service provider may be performed using generative AI, or not. For example, the service provider can provide a travel experience using a generative AI model that takes the user's past travel history as input and outputs the optimal travel experience.

[0080] The service provider can customize the travel experience based on the user's current interests and areas of interest when providing the travel experience. For example, the service provider can customize the travel experience based on themes the user is currently interested in. For example, the service provider can provide an experience that includes activities related to the user's areas of interest. For example, the service provider can provide an experience that visits highly relevant places based on the user's current interests. In this way, the service provider can provide a more relevant travel experience by customizing the experience based on the user's current interests and areas of interest. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider can provide a travel experience using a generative AI model that takes the user's current interests and areas of interest as input and outputs a customized travel experience.

[0081] The service provider can estimate the user's emotions and determine the priority of the travel experiences offered based on the estimated emotions. For example, if the user is excited, the service provider will offer experiences that prioritize exciting activities. For example, if the user is relaxed, the service provider will offer experiences that prioritize relaxing activities. For example, if the user is stressed, the service provider will offer experiences that prioritize visits to relaxing places. In this way, the service provider can provide a more appropriate travel experience by determining the priority of travel 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 service provider may be performed using generative AI or not. For example, the service provider can determine the priority using a generative AI model that takes user emotion data as input and outputs a priority of travel experiences.

[0082] The service provider can provide the optimal travel experience by considering the user's geographical location information when providing travel experiences. For example, the service provider can provide travel experiences related to the user's current location. For example, the service provider can suggest the optimal travel destination based on the user's geographical location information. For example, the service provider can provide experiences that include events and places related to the user's current location. In this way, the service provider can provide more relevant travel experiences by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using generative AI, or not. For example, the service provider can provide travel experiences using a generative AI model that takes the user's geographical location information as input and outputs the optimal travel experience.

[0083] The service provider can analyze the user's social media activity and customize the travel experience when providing it. For example, the service provider can analyze the user's social media posts and suggest relevant travel experiences. For example, the service provider can customize the experience based on the user's interests and preferences on social media. For example, the service provider can provide the optimal travel experience based on the user's social media activity history. In this way, the service provider can provide a more relevant travel experience by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using generative AI, or not. For example, the service provider can provide a travel experience using a generative AI model that takes the user's social media activity as input and outputs a customized travel experience.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The reception desk can monitor the user's health status and adjust the acceptance of virtual travel requests accordingly. For example, if the user is feeling unwell, it can prioritize suggesting relaxing travel plans. If the user is in good health, it can suggest more active travel plans. It can also adjust the content of the travel plan based on the user's health status. For example, if the user is tired, it can suggest a short, enjoyable travel plan. In this way, the reception desk can provide the optimal travel plan according to the user's health condition.

[0086] The generation unit can customize travel plans based on the user's hobbies and skills. For example, if the user enjoys photography, it can generate a travel plan that includes spots suitable for photography. If the user enjoys cooking, it can generate a travel plan that includes local cooking classes. Furthermore, if the user's skill lies in sports, it can generate a travel plan that includes sports events and activities. This allows the generation unit to provide more personalized travel plans based on the user's hobbies and skills.

[0087] The service provider can collect real-time feedback from users during their travel experience and dynamically adjust travel plans. For example, if a user likes a particular place, they can extend their stay there. If a user shows no interest in a particular activity, alternative activities can be suggested. Furthermore, based on user feedback, future travel plans can be more appropriately customized. This allows the service provider to offer a more flexible travel experience that reflects real-time user feedback.

[0088] The service provider can offer features that allow users to share their travel experiences with other users. For example, it can provide a function that allows users to post photos and videos taken during their virtual trip to social media. It can also provide a function that allows users to stream their travel experience in real time. Furthermore, it can provide a function that allows users to enjoy virtual trips together with other users. In this way, the service provider can expand the enjoyment of travel experiences by sharing users' travel experiences with other users.

[0089] The service provider can offer users information about local culture and history during their travel experience. For example, it can introduce the historical background and cultural anecdotes of the places the user visits. It can also provide information about the local language and customs. Furthermore, if the user is interested, it can offer a feature that allows them to virtually interact with a local guide. In this way, the service provider can make the user's travel experience deeper and more educational.

[0090] The reception desk can estimate the user's emotions and adjust the suggested travel plans based on those emotions. For example, if the user is feeling stressed, it can suggest a relaxing travel plan. If the user is excited, it can suggest an active travel plan. Furthermore, if the user is sad, it can suggest a travel plan to lift their spirits. This allows the reception desk to provide the most suitable travel plan according to the user's emotions.

[0091] The generation unit can estimate the user's emotions and adjust the travel plan based on those emotions. For example, if the user is relaxed, it can generate a travel plan that proceeds at a leisurely pace. If the user is in a hurry, it can generate a plan that visits many places in a short amount of time. If the user is excited, it can generate a plan that includes stimulating activities. In this way, the generation unit can provide a more appropriate travel plan based on the user's emotions.

[0092] The service provider can estimate the user's emotions and adjust how the travel experience is delivered based on those emotions. For example, if the user is relaxed, they can provide a travel experience that proceeds at a leisurely pace. If the user is in a hurry, they can provide an experience that visits many places in a short amount of time. If the user is excited, they can provide an experience that includes stimulating activities. In this way, the service provider can deliver a more appropriate travel experience based on the user's emotions.

[0093] The service provider can estimate the user's emotions and prioritize the travel experience offered based on those emotions. For example, if the user is excited, the service provider can prioritize offering an experience that includes stimulating activities. If the user is relaxed, the service provider can prioritize offering an experience that includes relaxing activities. Also, if the user is stressed, the service provider can prioritize offering an experience that visits relaxing places. In this way, the service provider can provide a more appropriate travel experience based on the user's emotions.

[0094] The service provider can estimate the user's emotions and adjust how the travel experience is delivered based on those emotions. For example, if the user is relaxed, they can provide a travel experience that proceeds at a leisurely pace. If the user is in a hurry, they can provide an experience that visits many places in a short amount of time. If the user is excited, they can provide an experience that includes stimulating activities. In this way, the service provider can deliver a more appropriate travel experience based on the user's emotions.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The reception desk receives the user's virtual travel request. For example, the user can enter a request such as "I want to see the Eiffel Tower in Paris." Step 2: The generation unit analyzes the user's interests and past travel history based on the request received by the reception unit and generates the optimal travel plan. For example, if the user has previously shown interest in French cuisine, the generation unit will generate a travel plan that includes famous French restaurants around the Eiffel Tower. The generation unit uses a generation AI to analyze the user's interests and past travel history and generate the optimal travel plan. The generation AI takes the user's past travel history and interests as input and outputs the optimal travel plan. Step 3: The service provider uses VR technology to deliver a real-time travel experience based on the travel plan generated by the generation unit. For example, the user can wear a VR headset and virtually visit the Eiffel Tower. Based on the generated travel plan, the service provider provides a travel experience customized to the user's preferences. For example, if the user prefers visiting museums, the service provider will offer a plan to visit the Louvre Museum after the Eiffel Tower.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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 control unit 46A of the smart device 14, where the user can input a request to start a virtual trip. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the user's interests and past travel history to generate an optimal travel plan. The provision unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12, and can provide a real-time travel experience using VR technology based on the generated travel plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements, including the reception unit, generation unit, and provision unit described above, is implemented in at least one of the smart glasses 2214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 2214, the generation unit where the user can input a request to start a virtual trip is implemented by the specific processing unit 290 of the data processing unit 12, and the provision unit which analyzes the user's interests and past travel history and generates an optimal travel plan is implemented by the control unit 46A of the smart glasses 2214 and the specific processing unit 290 of the data processing unit 12. Based on the generated travel plan, a real-time travel experience can be provided using VR technology. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements, including the reception unit, generation unit, and provision unit described above, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, the generation unit where the user can input a request to start a virtual trip is implemented by the specific processing unit 290 of the data processing unit 12, and the provision unit which analyzes the user's interests and past travel history and generates an optimal travel plan is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12. Based on the generated travel plan, a real-time travel experience can be provided using VR technology. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements, including the reception unit, generation unit, and provision unit described above, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, the generation unit where the user can input a request to start a virtual trip is implemented by the specific processing unit 290 of the data processing unit 12, and the provision unit which analyzes the user's interests and past travel history and generates an optimal travel plan is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12. Based on the generated travel plan, a real-time travel experience can be provided using VR technology. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] (Note 1) A reception desk that accepts users' virtual travel requests, Based on the request received by the reception unit, the generation unit analyzes the user's interests and past travel history to generate an optimal travel plan. The system includes a provisioning unit that provides a real-time travel experience using VR technology based on the travel plan generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is It analyzes the user's interests and past travel history to generate the optimal travel plan. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Based on the generated travel plan, VR technology is used to provide a real-time travel experience. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We provide a travel experience customized to the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, This will allow users to virtually travel to various parts of the world without leaving their homes. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of accepting virtual travel requests based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past request history and select the optimal method of processing requests. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When a request is received, it is filtered based on the user's current interests and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of requests to be accepted based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving a request, the system prioritizes requests that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When a request is received, the system analyzes the user's social media activity and accepts relevant requests. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is We estimate the user's emotions and adjust the travel plan generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating a travel plan, the system references the user's past travel history to create the most suitable plan. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating travel plans, customize the plans based on the user's current interests and areas of concern. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and determines the priority of travel plans generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating travel plans, the system takes the user's geographical location into consideration to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating travel plans, the system analyzes the user's social media activity to customize the plan. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, We estimate the user's emotions and adjust how the travel experience is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing travel experiences, we refer to the user's past travel history to deliver the most optimal experience. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing travel experiences, customize the experience based on the user's current interests and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the travel experience offered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing travel experiences, we take the user's geographical location into consideration to deliver the optimal experience. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing travel experiences, analyze users' social media activity to customize the experience. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0169] 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 that accepts users' virtual travel requests, Based on the request received by the reception unit, the generation unit analyzes the user's interests and past travel history to generate an optimal travel plan. The system includes a provisioning unit that provides a real-time travel experience using VR technology based on the travel plan generated by the generation unit. A system characterized by the following features.

2. The generating unit is It analyzes the user's interests and past travel history to generate the optimal travel plan. The system according to feature 1.

3. The aforementioned supply unit is, Based on the generated travel plan, VR technology is used to provide a real-time travel experience. The system according to feature 1.

4. The aforementioned supply unit is, We provide a travel experience customized to the user's preferences. The system according to feature 1.

5. The aforementioned supply unit is, This will allow users to virtually travel to various parts of the world without leaving their homes. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of accepting virtual travel requests based on those emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past request history and select the optimal method of processing requests. The system according to feature 1.

8. The aforementioned reception unit is When a request is received, it is filtered based on the user's current interests and areas of interest. The system according to feature 1.

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