system

Abstract

The system according to this embodiment aims to create a personalized itinerary based on the user's interests and preferences and to provide virtual tourism. [Solution] The system according to the embodiment comprises a generation unit, a sightseeing unit, and a guide unit. The generation unit creates an itinerary based on the user's interests and preferences. The sightseeing unit provides a virtual tour based on the itinerary created by the generation unit. The guide unit answers the user's questions during the virtual tour provided by the sightseeing unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to provide a personalized journey based on the interests and preferences of users, and the virtual tourism experience is limited.

[0005] The system according to the embodiment aims to create a personalized journey based on the interests and preferences of users and provide virtual tourism.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a generation unit, a sightseeing unit, and a guide unit. The generation unit creates an itinerary based on the user's interests and preferences. The sightseeing unit provides a virtual tour based on the itinerary created by the generation unit. The guide unit answers the user's questions during the virtual tour provided by the sightseeing unit. [Effects of the Invention]

[0007] The system according to this embodiment can create a personalized itinerary based on the user's interests and preferences and provide virtual sightseeing. [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 controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The virtual travel system according to an embodiment of the present invention is a system that provides a personalized itinerary based on the user's interests and preferences, thereby realizing virtual tourism. This virtual travel system is started when the user accesses "MetaTravel" and inputs their interests and preferences. The generating AI analyzes the input information and creates an optimal itinerary tailored to the user's interests and preferences. Based on the generated itinerary, the user begins virtual tourism. The generating AI generates a high-quality 3D space in real time and provides it to the user. The user can freely explore tourist destinations in the virtual space and experience local culture and activities. Furthermore, the generating AI acts as a guide, leading the user. The generating AI uses natural language processing to answer the user's questions and provides information about tourist destinations and culture. In addition, this virtual travel system is multi-user compatible, allowing users to enjoy virtual travel with family and friends. Finally, this virtual travel system allows users to collect and exchange digital items as travel memories. For example, users can save digital items acquired during virtual travel as NFTs and exchange them with other users. In this way, the virtual travel system can provide a personalized itinerary based on the user's interests and preferences, thereby realizing virtual tourism.

[0029] The virtual travel system according to the embodiment comprises a generation unit, a sightseeing unit, and a guide unit. The generation unit creates an itinerary based on the user's interests and preferences. For example, the generation unit analyzes information on the user's interests and preferences and generates an optimal itinerary. The generation unit can create an itinerary based on the user's interests and preferences using a generation AI. For example, the generation unit inputs information entered by the user as a prompt to the generation AI, and the generation AI generates an optimal itinerary. The sightseeing unit provides virtual sightseeing based on the itinerary created by the generation unit. For example, the sightseeing unit uses a generation AI to generate a high-quality 3D space in real time and provides it to the user. The sightseeing unit uses a generation AI to enable the user to freely explore tourist destinations in the virtual space and experience local culture and activities. For example, the sightseeing unit inputs the prompt "Generate a 3D model of this tourist destination" to the generation AI, and the generation AI generates a high-quality 3D space. The guide unit answers the user's questions during the virtual sightseeing provided by the sightseeing unit. For example, the guide unit uses natural language processing to answer the user's questions and provides information about tourist destinations and culture. The guide unit can use a generative AI to generate appropriate answers to user questions. For example, the guide unit inputs a user question as a prompt to the generative AI, which then generates an appropriate answer. As a result, the virtual travel system according to this embodiment can provide a personalized itinerary based on the user's interests and preferences, thereby realizing virtual tourism.

[0030] The generation unit creates itineraries based on the user's interests and preferences. For example, the generation unit analyzes the information on the user's interests and preferences and generates the optimal itinerary. Specifically, the information entered by the user includes tourist destinations they want to visit, activities they want to experience, and their preferred types of food and accommodation. The generation unit collects this information and analyzes the user's preferences in detail. The generation unit can create itineraries based on the user's interests and preferences using a generation AI. The generation AI receives the user's input information as prompts and generates the optimal itinerary while referring to past data and related information. For example, the generation unit inputs the information entered by the user as prompts to the generation AI, and the generation AI generates the optimal itinerary. The generation AI selects tourist destinations and activities based on the user's interests and creates a detailed itinerary including dates, transportation, and accommodation. The generation unit presents the generated itinerary to the user and allows adjustments until the user is satisfied. In this way, the generation unit can provide a personalized itinerary that perfectly matches the user's interests and preferences. Furthermore, the generation unit can collect user feedback and utilize it as training data for the generation AI, enabling it to generate more accurate itineraries in the future. This allows the generation unit to provide high-quality virtual travel experiences that meet user expectations.

[0031] The Ministry of Tourism provides virtual tourism based on itineraries created by the Generator Department. For example, the Ministry of Tourism uses a generative AI to generate high-quality 3D spaces in real time and provide them to users. Specifically, the Ministry of Tourism inputs a prompt to the generative AI, such as "Generate a 3D model of this tourist destination," and the generative AI generates a high-quality 3D space. The generated 3D space is designed so that users can freely explore the tourist destination in the virtual space. Users can experience the tourist destination realistically using virtual reality (VR) or augmented reality (AR) devices. The Ministry of Tourism uses the generative AI to enable users to freely explore the tourist destination in the virtual space and experience the local culture and activities. For example, users can visit famous landmarks in the tourist destination or participate in local events in the virtual space. The Ministry of Tourism can also use the generative AI to provide detailed information about the tourist destination, such as its historical background and cultural characteristics. This allows users to deepen their understanding of the local culture and history through virtual tourism. Furthermore, by collecting user behavior data and utilizing it as training data for generating AI, the Ministry of Tourism can provide more realistic and engaging virtual tourism experiences in the future. This will enable the Ministry of Tourism to provide users with high-quality virtual tourism experiences and improve user satisfaction.

[0032] The guide unit answers user questions during virtual tours provided by the tourism unit. The guide unit uses natural language processing, for example, to answer user questions and provide information about tourist destinations and culture. Specifically, the guide unit receives questions entered by the user during the virtual tour in real time and inputs them as prompts into the generative AI. The generative AI generates appropriate answers to the questions, which the guide unit then provides to the user. For example, if a user asks, "What is the history of this building?", the guide unit inputs this as a prompt into the generative AI, which then generates information about the building's history. The generated answer is provided to the user in natural language. The guide unit can use the generative AI to generate appropriate answers to user questions. This allows users to obtain information they wonder about or want to know in real time during their virtual tour. Furthermore, the guide unit can collect user question data and use it as training data for the generative AI, enabling it to provide more accurate answers in the future. This allows the guide unit to provide users with high-quality information and enhance the virtual tour experience. Additionally, the guide unit can suggest relevant information and recommended tourist destinations based on the user's interests. This allows the guide section to make the user's virtual travel experience more personalized and engaging.

[0033] The Tourism Department can provide features for users to communicate with other users. For example, the Tourism Department can provide a chat function. The Tourism Department can enable users to exchange text messages with other users. For example, the Tourism Department can display a chat window, allowing users to exchange messages with other users in real time. The Tourism Department can also provide a video call function. The Tourism Department can enable users to make video calls with other users. For example, the Tourism Department can display a video call window, allowing users to communicate with other users face-to-face. This allows users to communicate with other users. Some or all of the above processing in the Tourism Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Tourism Department can input a user's message into a generative AI, which can then generate an appropriate reply.

[0034] The Ministry of Tourism can provide a function to store digital items acquired by users during virtual travel as NFTs and exchange them with other users. For example, the Ministry of Tourism can store digital items acquired by users during virtual travel as NFTs using blockchain technology. The Ministry of Tourism can ensure that users can securely store and exchange digital items with other users. For example, the Ministry of Tourism can record digital items acquired by users on the blockchain and store them as NFTs. The Ministry of Tourism can also provide a platform for users to exchange digital items with other users. The Ministry of Tourism can provide a marketplace for users to exchange digital items acquired by other users. For example, the Ministry of Tourism can list digital items acquired by users on the marketplace, allowing other users to purchase them. This allows users to store digital items acquired during virtual travel and exchange them with other users. Some or all of the above processes in the Ministry of Tourism may be performed using, for example, a generative AI, or not using a generative AI. For example, the Ministry of Tourism can input digital items acquired by users into a generative AI, which can then perform the procedure for storing them as NFTs.

[0035] The generation unit can analyze the user's past travel history and propose an optimal itinerary. For example, the generation unit can propose similar tourist destinations based on the tourist destinations the user has visited in the past. The generation unit can propose similar activities based on the activities the user has participated in in the past. The generation unit can propose highly-rated tourist destinations based on the user's ratings of tourist destinations they have visited in the past. For example, the generation unit can collect data on tourist destinations the user has visited in the past and generate an optimal itinerary using a generation AI. The generation unit inputs the user's past travel history as a prompt into the generation AI, and the generation AI generates an optimal itinerary. This allows the generation unit to propose an optimal itinerary based on the user's past travel history. Some or all of the above-described 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 input the user's past travel history data into a generation AI, and the generation AI can generate an optimal itinerary.

[0036] The generation unit can collect user feedback on the generated itinerary and reflect it in the generation of the next itinerary. For example, the generation unit can improve the next itinerary based on user evaluations. The generation unit can improve the next itinerary based on user comments. The generation unit can improve the next itinerary based on user responses to questionnaires about the itinerary. For example, the generation unit can collect user feedback data and generate the next itinerary using a generation AI. The generation unit can input user feedback data as a prompt into the generation AI, and the generation AI will generate the next itinerary. This allows for improvements to the next itinerary based on user feedback. Some or all of the above processes in the generation unit may be performed using a generation AI, or without using a generation AI. For example, the generation unit can input user feedback data into a generation AI, and the generation AI can generate the next itinerary.

[0037] The generation unit can suggest highly relevant tourist destinations based on the user's geographical location information. For example, the generation unit can suggest tourist destinations close to the user's current location. The generation unit can suggest tourist destinations that are easily accessible from the user's current location. The generation unit can suggest the optimal tourist destination considering the travel time from the user's current location. For example, the generation unit can collect the user's geographical location information and suggest highly relevant tourist destinations using a generation AI. The generation unit inputs the user's geographical location information as a prompt into the generation AI, and the generation AI suggests highly relevant tourist destinations. This allows the generation unit to suggest highly relevant tourist destinations based on the user's geographical location information. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or without using a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI, and the generation AI can suggest highly relevant tourist destinations.

[0038] The generation unit can analyze a user's social media activity and generate itineraries based on their interests. For example, the generation unit can suggest similar tourist destinations based on the tourist destinations the user has "liked" on social media. The generation unit can suggest similar activities based on the activities the user has shared on social media. The generation unit can suggest itineraries based on the tourist destinations and activities the user follows on social media. For example, the generation unit can collect the user's social media activity data and generate an itinerary based on their interests using a generation AI. The generation unit inputs the user's social media activity data as a prompt into the generation AI, and the generation AI generates an itinerary based on their interests. This allows the generation of an itinerary based on the user's social media activity. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the user's social media activity data into a generation AI, and the generation AI can generate an itinerary based on their interests.

[0039] The Ministry of Tourism can analyze user behavior in real time during sightseeing and provide engaging content. For example, if a user stays at a particular location for an extended period, the Ministry of Tourism can provide content related to that location. If a user participates in a particular activity, the Ministry of Tourism can provide content related to that activity. If a user visits a particular tourist destination, the Ministry of Tourism can provide content related to that tourist destination. For example, the Ministry of Tourism can collect user behavior data and use generative AI to provide engaging content. The Ministry of Tourism inputs user behavior data as prompts into the generative AI, which then generates engaging content. This allows the Ministry to provide engaging content by analyzing user behavior in real time. Some or all of the above processes in the Ministry of Tourism may be performed using generative AI, or without using generative AI. For example, the Ministry of Tourism can input user behavior data into a generative AI, which then generates engaging content.

[0040] The Ministry of Tourism can provide detailed information about the historical background and culture of tourist destinations. For example, the Ministry of Tourism can provide detailed information about the historical background of tourist destinations. The Ministry of Tourism can provide detailed information about the culture of tourist destinations. The Ministry of Tourism can provide detailed information about the traditions and customs of tourist destinations. For example, the Ministry of Tourism can collect information about the historical background and culture of tourist destinations and provide detailed information using a generative AI. The Ministry of Tourism inputs information about the historical background and culture of tourist destinations as prompts into the generative AI, and the generative AI generates detailed information. This deepens the user's understanding by providing detailed information about the historical background and culture of tourist destinations. Some or all of the above processing by the Ministry of Tourism may be performed using a generative AI, for example, or without a generative AI. For example, the Ministry of Tourism can input information about the historical background and culture of tourist destinations into a generative AI, and the generative AI can generate detailed information.

[0041] The Tourism Department can select the optimal 3D space display method based on the user's device information. For example, if the user is using a smartphone, the Tourism Department will provide a 3D space display method adapted to the screen size. If the user is using a tablet, the Tourism Department will provide a 3D space display method optimized for a larger screen. If the user is using a VR headset, the Tourism Department will provide a highly immersive 3D space display method. For example, the Tourism Department collects the user's device information and uses a generative AI to select the optimal 3D space display method. The Tourism Department inputs the user's device information as a prompt into the generative AI, and the generative AI selects the optimal 3D space display method. This allows the Tourism Department to provide the optimal 3D space display method based on the user's device information. Some or all of the above processing in the Tourism Department may be performed using a generative AI, or without using a generative AI. For example, the Tourism Department can input the user's device information into a generative AI, and the generative AI can select the optimal 3D space display method.

[0042] The Tourism Department can suggest relevant tourist destinations by referring to the user's past travel history. For example, the Tourism Department can suggest similar tourist destinations based on the tourist destinations the user has visited in the past. The Tourism Department can suggest similar activities based on the activities the user has participated in in the past. The Tourism Department can suggest highly-rated tourist destinations based on the user's ratings of tourist destinations they have visited in the past. For example, the Tourism Department can collect the user's past travel history data and suggest relevant tourist destinations using a generative AI. The Tourism Department inputs the user's past travel history data as a prompt into the generative AI, and the generative AI suggests relevant tourist destinations. This allows the Tourism Department to suggest relevant tourist destinations based on the user's past travel history. Some or all of the above processes in the Tourism Department may be performed using a generative AI, or not. For example, the Tourism Department can input the user's past travel history data into a generative AI, and the generative AI can suggest relevant tourist destinations.

[0043] The guide unit can analyze the user's question history and provide more appropriate answers. For example, the guide unit provides relevant information based on the user's past questions. The guide unit provides detailed information based on the user's past questions. The guide unit provides additional information based on the user's past questions. For example, the guide unit collects the user's question history data and uses a generative AI to provide more appropriate answers. The guide unit inputs the user's question history data as a prompt into the generative AI, and the generative AI generates an appropriate answer. This allows the guide unit to provide more appropriate answers based on the user's question history. Some or all of the above processing in the guide unit may be performed using a generative AI, or without using a generative AI. For example, the guide unit can input the user's question history data into a generative AI, and the generative AI can generate an appropriate answer.

[0044] The guide unit can collect user feedback on the information provided by the guide and reflect it in the next guide. For example, the guide unit can improve the next guide based on user evaluations of the guide's information. The guide unit can improve the next guide based on user comments on the guide's information. The guide unit can improve the next guide based on user responses to questionnaires about the guide's information. For example, the guide unit can collect user feedback data and generate the next guide using a generative AI. The guide unit can input user feedback data as prompts into the generative AI, which then generates the next guide. This allows the next guide to be improved based on user feedback. Some or all of the above processes in the guide unit may be performed using a generative AI, or not. For example, the guide unit can input user feedback data into a generative AI, which then generates the next guide.

[0045] The guide unit can provide a guide in the most suitable language based on the user's language settings. For example, the guide unit can automatically set the guide language based on the language settings of the user's device. The guide unit provides a language switching function when the user uses multiple languages. When the user selects a specific language, the guide unit provides a guide in that language. For example, the guide unit collects the user's language setting data and uses a generative AI to provide a guide in the most suitable language. The guide unit inputs the user's language setting data as a prompt to the generative AI, and the generative AI provides a guide in the most suitable language. This allows the guide to be provided in the most suitable language based on the user's language settings. Some or all of the above processing in the guide unit may be performed using a generative AI, or not using a generative AI. For example, the guide unit can input the user's language setting data to a generative AI, and the generative AI can provide a guide in the most suitable language.

[0046] The guide unit can provide relevant additional information based on the user's interests. For example, if the user shows interest in a particular tourist destination, the guide unit will provide additional information related to that destination. If the user shows interest in a particular activity, the guide unit will provide additional information related to that activity. If the user shows interest in a particular culture, the guide unit will provide additional information related to that culture. For example, the guide unit can collect user interest data and provide relevant additional information using a generative AI. The guide unit inputs the user's interest data as a prompt into the generative AI, and the generative AI generates the relevant additional information. This allows the guide unit to provide relevant additional information based on the user's interests. Some or all of the above processing in the guide unit may be performed using a generative AI, or without using a generative AI. For example, the guide unit can input user interest data into a generative AI, and the generative AI can generate the relevant additional information.

[0047] The communication function can analyze the communication history between users and suggest the most suitable communication partner. For example, the communication function can suggest the most suitable communication partner based on who the user has frequently communicated with in the past. The communication function can suggest users with common interests based on the history of group chats the user has participated in in the past. The communication function can suggest compatible users by analyzing the content of past communications the user has had. For example, the communication function can collect the user's communication history data and suggest the most suitable communication partner using a generative AI. The communication function inputs the user's communication history data as a prompt into the generative AI, and the generative AI suggests the most suitable communication partner. This allows for the suggestion of the most suitable communication partner based on the communication history between users. Some or all of the above processing in the communication function may be performed using a generative AI, or not using a generative AI. For example, the communication function can input the user's communication history data into a generative AI, and the generative AI can suggest the most suitable communication partner.

[0048] The communication function can provide the optimal means of communication based on the user's device information. For example, if the user is using a smartphone, the communication function can provide chat or voice calls. If the user is using a tablet, the communication function can provide video calls or chat. If the user is using a desktop, the communication function can provide video calls or chat. For example, the communication function collects the user's device information and uses a generative AI to provide the optimal means of communication. The communication function inputs the user's device information as a prompt into the generative AI, and the generative AI provides the optimal means of communication. This allows the optimal means of communication to be provided based on the user's device information. Some or all of the above processing in the communication function may be performed using a generative AI, or it may be performed without using a generative AI. For example, the communication function can input the user's device information into a generative AI, and the generative AI can provide the optimal means of communication.

[0049] The digital item saving and exchange function can analyze a user's past digital item collection history and suggest the most suitable items. For example, the digital item saving and exchange function can suggest similar items based on digital items the user has collected in the past. The digital item saving and exchange function can suggest highly rated items based on digital items the user has given high ratings to in the past. The digital item saving and exchange function can suggest exchangeable items based on digital items the user has exchanged in the past. For example, the digital item saving and exchange function collects the user's past digital item collection history data and suggests the most suitable items using a generating AI. The digital item saving and exchange function inputs the user's past digital item collection history data as a prompt into the generating AI, and the generating AI suggests the most suitable items. This allows the system to suggest the most suitable items based on the user's past digital item collection history. Some or all of the above processes in the digital item saving and exchange function may be performed using a generating AI, or not using a generating AI. For example, the digital item saving and exchange function can input the user's past digital item collection history data into the generating AI, and the generating AI can suggest the most suitable items.

[0050] The digital item saving and exchange function can suggest highly relevant digital items based on the user's geographical location information. For example, the digital item saving and exchange function can suggest digital items related to tourist destinations close to the user's current location. The digital item saving and exchange function can suggest digital items related to tourist destinations easily accessible from the user's current location. The digital item saving and exchange function can suggest the most relevant digital items considering the user's travel time from their current location. For example, the digital item saving and exchange function collects the user's geographical location information and suggests highly relevant digital items using a generating AI. The digital item saving and exchange function inputs the user's geographical location information as a prompt into the generating AI, and the generating AI suggests highly relevant digital items. This allows the system to suggest highly relevant digital items based on the user's geographical location information. Some or all of the above-described processes in the digital item saving and exchange function may be performed using a generating AI, or without using a generating AI. For example, the digital item saving and exchange function can input the user's geographical location information into a generating AI, and the generating AI can suggest highly relevant digital items.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The generation unit can monitor the user's health status and adjust the itinerary based on that status. For example, the generation unit can measure the user's heart rate and blood pressure to assess their health status. The generation unit can then suggest relaxing tourist destinations and activities based on the user's health status. For example, if the user's heart rate is high, the generation unit will prioritize suggesting relaxing tourist destinations. If the user's health status is good, the generation unit can suggest active activities. This allows for the provision of a more appropriate itinerary based on the user's health status.

[0053] The Tourism Department can provide virtual tours tailored to specific themes based on user interests. For example, it can offer virtual tours of historical sites or nature-based tours. Based on the user's chosen theme, the Tourism Department can suggest relevant tourist destinations and activities. For instance, if a user is interested in history, the Tourism Department can suggest a tour of historical sites. If a user wants to enjoy nature, the Tourism Department can suggest a nature-based tour. This allows the Tourism Department to provide virtual tours tailored to specific themes based on user interests.

[0054] The generation unit can analyze a user's past travel history and propose the optimal itinerary. For example, the generation unit can suggest similar tourist destinations based on the tourist destinations the user has visited in the past. The generation unit can suggest similar activities based on the activities the user has participated in in the past. The generation unit can suggest highly-rated tourist destinations based on the user's ratings of tourist destinations they have visited in the past. For example, the generation unit collects data on tourist destinations the user has visited in the past and generates the optimal itinerary using a generation AI. The generation unit inputs the user's past travel history as a prompt into the generation AI, and the generation AI generates the optimal itinerary. In this way, it can propose the optimal itinerary based on the user's past travel history.

[0055] The generation unit can collect user feedback in real time and instantly adjust the itinerary. For example, the generation unit can improve the itinerary based on user evaluations. The generation unit can improve the itinerary based on user comments. The generation unit can improve the itinerary based on user responses to questionnaires. For example, the generation unit collects user feedback data and instantly adjusts the itinerary using a generation AI. The generation unit inputs user feedback data as a prompt into the generation AI, which then instantly adjusts the itinerary. This allows for instant adjustment of the itinerary content based on user feedback.

[0056] The guide unit can analyze the user's question history and provide more appropriate answers. For example, the guide unit provides relevant information based on the user's past questions. The guide unit provides detailed information based on the user's past questions. The guide unit provides additional information based on the user's past questions. For example, the guide unit collects the user's question history data and uses generative AI to provide more appropriate answers. The guide unit inputs the user's question history data as a prompt into the generative AI, which generates an appropriate answer. This allows the guide unit to provide more appropriate answers based on the user's question history.

[0057] The guide unit can provide guides in the most suitable language based on the user's language settings. For example, the guide unit can automatically set the guide language based on the language settings of the user's device. The guide unit provides a language switching function if the user uses multiple languages. If the user selects a specific language, the guide unit will provide the guide in that language. For example, the guide unit can collect the user's language setting data and use a generative AI to provide the guide in the most suitable language. The guide unit inputs the user's language setting data as a prompt to the generative AI, and the generative AI provides the guide in the most suitable language. This allows the guide to be provided in the most suitable language based on the user's language settings.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The generation unit creates an itinerary based on the user's interests and preferences. The generation unit analyzes the information on the user's interests and preferences and generates the optimal itinerary. The generation unit can create an itinerary based on the user's interests and preferences using a generation AI. For example, the generation unit inputs information from the user as a prompt into the generation AI, and the generation AI generates the optimal itinerary. Step 2: The Tourism Department provides virtual tourism based on the itinerary created by the Generation Department. The Tourism Department uses a generation AI to generate high-quality 3D spaces in real time and provide them to users. The Tourism Department allows users to freely explore tourist destinations in the virtual space and experience local culture and activities. For example, the Tourism Department inputs a prompt to the generation AI, "Generate a 3D model of this tourist destination," and the generation AI generates a high-quality 3D space. Step 3: The guide unit answers user questions during the virtual tour provided by the tourism unit. The guide unit uses natural language processing to answer user questions and provides information about tourist destinations and culture. The guide unit can use generative AI to generate appropriate answers to user questions. For example, the guide unit inputs the user's question as a prompt into the generative AI, which then generates an appropriate answer.

[0060] (Example of form 2) The virtual travel system according to an embodiment of the present invention is a system that provides a personalized itinerary based on the user's interests and preferences, thereby realizing virtual tourism. This virtual travel system is started when the user accesses "MetaTravel" and inputs their interests and preferences. The generating AI analyzes the input information and creates an optimal itinerary tailored to the user's interests and preferences. Based on the generated itinerary, the user begins virtual tourism. The generating AI generates a high-quality 3D space in real time and provides it to the user. The user can freely explore tourist destinations in the virtual space and experience local culture and activities. Furthermore, the generating AI acts as a guide, leading the user. The generating AI uses natural language processing to answer the user's questions and provides information about tourist destinations and culture. In addition, this virtual travel system is multi-user compatible, allowing users to enjoy virtual travel with family and friends. Finally, this virtual travel system allows users to collect and exchange digital items as travel memories. For example, users can save digital items acquired during virtual travel as NFTs and exchange them with other users. In this way, the virtual travel system can provide a personalized itinerary based on the user's interests and preferences, thereby realizing virtual tourism.

[0061] The virtual travel system according to the embodiment comprises a generation unit, a sightseeing unit, and a guide unit. The generation unit creates an itinerary based on the user's interests and preferences. For example, the generation unit analyzes information on the user's interests and preferences and generates an optimal itinerary. The generation unit can create an itinerary based on the user's interests and preferences using a generation AI. For example, the generation unit inputs information entered by the user as a prompt to the generation AI, and the generation AI generates an optimal itinerary. The sightseeing unit provides virtual sightseeing based on the itinerary created by the generation unit. For example, the sightseeing unit uses a generation AI to generate a high-quality 3D space in real time and provides it to the user. The sightseeing unit uses a generation AI to enable the user to freely explore tourist destinations in the virtual space and experience local culture and activities. For example, the sightseeing unit inputs the prompt "Generate a 3D model of this tourist destination" to the generation AI, and the generation AI generates a high-quality 3D space. The guide unit answers the user's questions during the virtual sightseeing provided by the sightseeing unit. For example, the guide unit uses natural language processing to answer the user's questions and provides information about tourist destinations and culture. The guide unit can use a generative AI to generate appropriate answers to user questions. For example, the guide unit inputs a user question as a prompt to the generative AI, which then generates an appropriate answer. As a result, the virtual travel system according to this embodiment can provide a personalized itinerary based on the user's interests and preferences, thereby realizing virtual tourism.

[0062] The generation unit creates itineraries based on the user's interests and preferences. For example, the generation unit analyzes the information on the user's interests and preferences and generates the optimal itinerary. Specifically, the information entered by the user includes tourist destinations they want to visit, activities they want to experience, and their preferred types of food and accommodation. The generation unit collects this information and analyzes the user's preferences in detail. The generation unit can create itineraries based on the user's interests and preferences using a generation AI. The generation AI receives the user's input information as prompts and generates the optimal itinerary while referring to past data and related information. For example, the generation unit inputs the information entered by the user as prompts to the generation AI, and the generation AI generates the optimal itinerary. The generation AI selects tourist destinations and activities based on the user's interests and creates a detailed itinerary including dates, transportation, and accommodation. The generation unit presents the generated itinerary to the user and allows adjustments until the user is satisfied. In this way, the generation unit can provide a personalized itinerary that perfectly matches the user's interests and preferences. Furthermore, the generation unit can collect user feedback and utilize it as training data for the generation AI, enabling it to generate more accurate itineraries in the future. This allows the generation unit to provide high-quality virtual travel experiences that meet user expectations.

[0063] The Ministry of Tourism provides virtual tourism based on itineraries created by the Generator Department. For example, the Ministry of Tourism uses a generative AI to generate high-quality 3D spaces in real time and provide them to users. Specifically, the Ministry of Tourism inputs a prompt to the generative AI, such as "Generate a 3D model of this tourist destination," and the generative AI generates a high-quality 3D space. The generated 3D space is designed so that users can freely explore the tourist destination in the virtual space. Users can experience the tourist destination realistically using virtual reality (VR) or augmented reality (AR) devices. The Ministry of Tourism uses the generative AI to enable users to freely explore the tourist destination in the virtual space and experience the local culture and activities. For example, users can visit famous landmarks in the tourist destination or participate in local events in the virtual space. The Ministry of Tourism can also use the generative AI to provide detailed information about the tourist destination, such as its historical background and cultural characteristics. This allows users to deepen their understanding of the local culture and history through virtual tourism. Furthermore, by collecting user behavior data and utilizing it as training data for generating AI, the Ministry of Tourism can provide more realistic and engaging virtual tourism experiences in the future. This will enable the Ministry of Tourism to provide users with high-quality virtual tourism experiences and improve user satisfaction.

[0064] The guide unit answers user questions during virtual tours provided by the tourism unit. The guide unit uses natural language processing, for example, to answer user questions and provide information about tourist destinations and culture. Specifically, the guide unit receives questions entered by the user during the virtual tour in real time and inputs them as prompts into the generative AI. The generative AI generates appropriate answers to the questions, which the guide unit then provides to the user. For example, if a user asks, "What is the history of this building?", the guide unit inputs this as a prompt into the generative AI, which then generates information about the building's history. The generated answer is provided to the user in natural language. The guide unit can use the generative AI to generate appropriate answers to user questions. This allows users to obtain information they wonder about or want to know in real time during their virtual tour. Furthermore, the guide unit can collect user question data and use it as training data for the generative AI, enabling it to provide more accurate answers in the future. This allows the guide unit to provide users with high-quality information and enhance the virtual tour experience. Additionally, the guide unit can suggest relevant information and recommended tourist destinations based on the user's interests. This allows the guide section to make the user's virtual travel experience more personalized and engaging.

[0065] The Tourism Department can provide features for users to communicate with other users. For example, the Tourism Department can provide a chat function. The Tourism Department can enable users to exchange text messages with other users. For example, the Tourism Department can display a chat window, allowing users to exchange messages with other users in real time. The Tourism Department can also provide a video call function. The Tourism Department can enable users to make video calls with other users. For example, the Tourism Department can display a video call window, allowing users to communicate with other users face-to-face. This allows users to communicate with other users. Some or all of the above processing in the Tourism Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Tourism Department can input a user's message into a generative AI, which can then generate an appropriate reply.

[0066] The Ministry of Tourism can provide a function to store digital items acquired by users during virtual travel as NFTs and exchange them with other users. For example, the Ministry of Tourism can store digital items acquired by users during virtual travel as NFTs using blockchain technology. The Ministry of Tourism can ensure that users can securely store and exchange digital items with other users. For example, the Ministry of Tourism can record digital items acquired by users on the blockchain and store them as NFTs. The Ministry of Tourism can also provide a platform for users to exchange digital items with other users. The Ministry of Tourism can provide a marketplace for users to exchange digital items acquired by other users. For example, the Ministry of Tourism can list digital items acquired by users on the marketplace, allowing other users to purchase them. This allows users to store digital items acquired during virtual travel and exchange them with other users. Some or all of the above processes in the Ministry of Tourism may be performed using, for example, a generative AI, or not using a generative AI. For example, the Ministry of Tourism can input digital items acquired by users into a generative AI, which can then perform the procedure for storing them as NFTs.

[0067] The generation unit can estimate the user's emotions and adjust the itinerary based on those emotions. For example, the generation unit can estimate the user's emotions using facial recognition technology. The generation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in the user's facial expressions. The generation unit can also estimate the user's emotions using voice analysis technology. The generation unit records the user's voice and estimates the emotions using a voice analysis algorithm. For example, the generation unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the generation unit adjusts the itinerary based on the estimated user emotions. For example, if the user is relaxed, the generation unit will prioritize suggesting relaxing tourist destinations and activities. If the user is excited, the generation unit will suggest adrenaline-pumping activities and tourist destinations. If the user is tired, the generation unit will suggest relaxing tourist destinations and activities. In this way, by adjusting the itinerary based on the user's emotions, a more appropriate itinerary can be provided. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using the generative AI or not. For example, the generation unit can input user emotion data into the generative AI, which can then adjust the itinerary.

[0068] The generation unit can analyze the user's past travel history and propose an optimal itinerary. For example, the generation unit can propose similar tourist destinations based on the tourist destinations the user has visited in the past. The generation unit can propose similar activities based on the activities the user has participated in in the past. The generation unit can propose highly-rated tourist destinations based on the user's ratings of tourist destinations they have visited in the past. For example, the generation unit can collect data on tourist destinations the user has visited in the past and generate an optimal itinerary using a generation AI. The generation unit inputs the user's past travel history as a prompt into the generation AI, and the generation AI generates an optimal itinerary. This allows the generation unit to propose an optimal itinerary based on the user's past travel history. Some or all of the above-described 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 input the user's past travel history data into a generation AI, and the generation AI can generate an optimal itinerary.

[0069] The generation unit can collect user feedback on the generated itinerary and reflect it in the generation of the next itinerary. For example, the generation unit can improve the next itinerary based on user evaluations. The generation unit can improve the next itinerary based on user comments. The generation unit can improve the next itinerary based on user responses to questionnaires about the itinerary. For example, the generation unit can collect user feedback data and generate the next itinerary using a generation AI. The generation unit can input user feedback data as a prompt into the generation AI, and the generation AI will generate the next itinerary. This allows for improvements to the next itinerary based on user feedback. Some or all of the above processes in the generation unit may be performed using a generation AI, or without using a generation AI. For example, the generation unit can input user feedback data into a generation AI, and the generation AI can generate the next itinerary.

[0070] The generation unit can estimate the user's emotions and determine the priority of the itinerary based on the estimated emotions. For example, the generation unit can estimate the user's emotions using facial recognition technology. The generation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in the user's facial expressions. The generation unit can also estimate the user's emotions using voice analysis technology. The generation unit records the user's voice and estimates the emotions using a voice analysis algorithm. For example, the generation unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the generation unit determines the priority of the itinerary based on the estimated emotions. For example, if the user is relaxed, the generation unit will prioritize suggesting relaxing tourist destinations and activities. If the user is excited, the generation unit will prioritize suggesting adrenaline-pumping activities and tourist destinations. If the user is tired, the generation unit will prioritize suggesting relaxing tourist destinations and activities. In this way, by determining the priority of the itinerary based on the user's emotions, a more appropriate itinerary can be provided. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using the generative AI or not. For example, the generation unit can input user emotion data into the generative AI, which can then determine the priority of the itinerary.

[0071] The generation unit can suggest highly relevant tourist destinations based on the user's geographical location information. For example, the generation unit can suggest tourist destinations close to the user's current location. The generation unit can suggest tourist destinations that are easily accessible from the user's current location. The generation unit can suggest the optimal tourist destination considering the travel time from the user's current location. For example, the generation unit can collect the user's geographical location information and suggest highly relevant tourist destinations using a generation AI. The generation unit inputs the user's geographical location information as a prompt into the generation AI, and the generation AI suggests highly relevant tourist destinations. This allows the generation unit to suggest highly relevant tourist destinations based on the user's geographical location information. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or without using a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI, and the generation AI can suggest highly relevant tourist destinations.

[0072] The generation unit can analyze a user's social media activity and generate itineraries based on their interests. For example, the generation unit can suggest similar tourist destinations based on the tourist destinations the user has "liked" on social media. The generation unit can suggest similar activities based on the activities the user has shared on social media. The generation unit can suggest itineraries based on the tourist destinations and activities the user follows on social media. For example, the generation unit can collect the user's social media activity data and generate an itinerary based on their interests using a generation AI. The generation unit inputs the user's social media activity data as a prompt into the generation AI, and the generation AI generates an itinerary based on their interests. This allows the generation of an itinerary based on the user's social media activity. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the user's social media activity data into a generation AI, and the generation AI can generate an itinerary based on their interests.

[0073] The Ministry of Tourism can estimate a user's emotions and adjust how tourist attractions are displayed based on those emotions. For example, the Ministry of Tourism might use facial recognition technology to estimate a user's emotions. This could involve capturing the user's facial expressions with a camera and using an emotion estimation algorithm to estimate their emotions. For instance, the Ministry could calculate an emotion score based on changes in the user's facial expressions. The Ministry of Tourism could also use voice analysis technology to estimate a user's emotions. This could involve recording the user's voice and using a voice analysis algorithm to estimate their emotions. For example, the Ministry could analyze the tone and speed of the user's voice to calculate an emotion score. Furthermore, the Ministry of Tourism could adjust how tourist attractions are displayed based on the estimated emotions. For example, if the user is relaxed, the Ministry could provide a display method with calming colors. If the user is excited, the Ministry could provide a display method with vibrant colors. If the user is tired, the Ministry could provide a display method with high visibility. By adjusting the display method of tourist attractions based on the user's emotions, a more appropriate display can be provided. Emotion estimation can be achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may include, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the above-described processes in the Ministry of Tourism may be performed using, for example, a generative AI, or not using a generative AI. For example, the Ministry of Tourism may input user sentiment data into a generative AI, which can then adjust how tourist destinations are displayed.

[0074] The Ministry of Tourism can analyze user behavior in real time during sightseeing and provide engaging content. For example, if a user stays at a particular location for an extended period, the Ministry of Tourism can provide content related to that location. If a user participates in a particular activity, the Ministry of Tourism can provide content related to that activity. If a user visits a particular tourist destination, the Ministry of Tourism can provide content related to that tourist destination. For example, the Ministry of Tourism can collect user behavior data and use generative AI to provide engaging content. The Ministry of Tourism inputs user behavior data as prompts into the generative AI, which then generates engaging content. This allows the Ministry to provide engaging content by analyzing user behavior in real time. Some or all of the above processes in the Ministry of Tourism may be performed using generative AI, or without using generative AI. For example, the Ministry of Tourism can input user behavior data into a generative AI, which then generates engaging content.

[0075] The Ministry of Tourism can provide detailed information about the historical background and culture of tourist destinations. For example, the Ministry of Tourism can provide detailed information about the historical background of tourist destinations. The Ministry of Tourism can provide detailed information about the culture of tourist destinations. The Ministry of Tourism can provide detailed information about the traditions and customs of tourist destinations. For example, the Ministry of Tourism can collect information about the historical background and culture of tourist destinations and provide detailed information using a generative AI. The Ministry of Tourism inputs information about the historical background and culture of tourist destinations as prompts into the generative AI, and the generative AI generates detailed information. This deepens the user's understanding by providing detailed information about the historical background and culture of tourist destinations. Some or all of the above processing by the Ministry of Tourism may be performed using a generative AI, for example, or without a generative AI. For example, the Ministry of Tourism can input information about the historical background and culture of tourist destinations into a generative AI, and the generative AI can generate detailed information.

[0076] The Ministry of Tourism can estimate a user's emotions and adjust the navigation of tourist destinations based on those emotions. For example, the Ministry of Tourism can estimate a user's emotions using facial recognition technology. It can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in the user's facial expressions. The Ministry of Tourism can also estimate a user's emotions using voice analysis technology. It can record the user's voice and estimate their emotions using a voice analysis algorithm. For example, it can analyze the tone and speed of the user's voice and calculate an emotion score. Furthermore, the Ministry of Tourism adjusts the navigation of tourist destinations based on the estimated user emotions. For example, if the user is relaxed, it provides navigation at a relaxed pace. If the user is excited, it provides navigation at a fast pace. If the user is tired, it prioritizes the shortest route. This allows for more appropriate navigation by adjusting the navigation of tourist destinations based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the Ministry of Tourism may be performed using, for example, a generative AI, or not using a generative AI. For example, the Ministry of Tourism can input user sentiment data into a generative AI, which can then adjust the navigation of tourist destinations.

[0077] The Tourism Department can select the optimal 3D space display method based on the user's device information. For example, if the user is using a smartphone, the Tourism Department will provide a 3D space display method adapted to the screen size. If the user is using a tablet, the Tourism Department will provide a 3D space display method optimized for a larger screen. If the user is using a VR headset, the Tourism Department will provide a highly immersive 3D space display method. For example, the Tourism Department collects the user's device information and uses a generative AI to select the optimal 3D space display method. The Tourism Department inputs the user's device information as a prompt into the generative AI, and the generative AI selects the optimal 3D space display method. This allows the Tourism Department to provide the optimal 3D space display method based on the user's device information. Some or all of the above processing in the Tourism Department may be performed using a generative AI, or without using a generative AI. For example, the Tourism Department can input the user's device information into a generative AI, and the generative AI can select the optimal 3D space display method.

[0078] The Tourism Department can suggest relevant tourist destinations by referring to the user's past travel history. For example, the Tourism Department can suggest similar tourist destinations based on the tourist destinations the user has visited in the past. The Tourism Department can suggest similar activities based on the activities the user has participated in in the past. The Tourism Department can suggest highly-rated tourist destinations based on the user's ratings of tourist destinations they have visited in the past. For example, the Tourism Department can collect the user's past travel history data and suggest relevant tourist destinations using a generative AI. The Tourism Department inputs the user's past travel history data as a prompt into the generative AI, and the generative AI suggests relevant tourist destinations. This allows the Tourism Department to suggest relevant tourist destinations based on the user's past travel history. Some or all of the above processes in the Tourism Department may be performed using a generative AI, or not. For example, the Tourism Department can input the user's past travel history data into a generative AI, and the generative AI can suggest relevant tourist destinations.

[0079] The guide unit can estimate the user's emotions and adjust the tone and expression of the guide based on the estimated emotions. For example, the guide unit can estimate the user's emotions using facial recognition technology. The guide unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the guide unit calculates an emotion score based on changes in the user's facial expressions. The guide unit can also estimate the user's emotions using voice analysis technology. The guide unit records the user's voice and estimates the emotions using a voice analysis algorithm. For example, the guide unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the guide unit adjusts the tone and expression of the guide based on the estimated emotions of the user. For example, if the user is relaxed, the guide unit will provide guidance in a calm tone. If the user is excited, the guide unit will provide guidance in a bright tone. If the user is tired, the guide unit will provide guidance in a gentle tone. In this way, by adjusting the tone and expression of the guide based on the user's emotions, a more appropriate guide can be provided. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the guide unit may be performed using the generative AI, or not using the generative AI. For example, the guide unit can input user emotion data into the generative AI, which can then adjust the tone and expression of the guide.

[0080] The guide unit can analyze the user's question history and provide more appropriate answers. For example, the guide unit provides relevant information based on the user's past questions. The guide unit provides detailed information based on the user's past questions. The guide unit provides additional information based on the user's past questions. For example, the guide unit collects the user's question history data and uses a generative AI to provide more appropriate answers. The guide unit inputs the user's question history data as a prompt into the generative AI, and the generative AI generates an appropriate answer. This allows the guide unit to provide more appropriate answers based on the user's question history. Some or all of the above processing in the guide unit may be performed using a generative AI, or without using a generative AI. For example, the guide unit can input the user's question history data into a generative AI, and the generative AI can generate an appropriate answer.

[0081] The guide unit can collect user feedback on the information provided by the guide and reflect it in the next guide. For example, the guide unit can improve the next guide based on user evaluations of the guide's information. The guide unit can improve the next guide based on user comments on the guide's information. The guide unit can improve the next guide based on user responses to questionnaires about the guide's information. For example, the guide unit can collect user feedback data and generate the next guide using a generative AI. The guide unit can input user feedback data as prompts into the generative AI, which then generates the next guide. This allows the next guide to be improved based on user feedback. Some or all of the above processes in the guide unit may be performed using a generative AI, or not. For example, the guide unit can input user feedback data into a generative AI, which then generates the next guide.

[0082] The guide unit can estimate the user's emotions and adjust the timing of information delivery based on the estimated emotions. For example, the guide unit can estimate the user's emotions using facial recognition technology. The guide unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the guide unit calculates an emotion score based on changes in the user's facial expressions. The guide unit can also estimate the user's emotions using voice analysis technology. The guide unit records the user's voice and estimates the emotions using a voice analysis algorithm. For example, the guide unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the guide unit adjusts the timing of information delivery based on the estimated emotions. For example, if the user is relaxed, the guide unit provides information at a relaxed pace. If the user is excited, the guide unit provides information at a rapid pace. If the user is tired, the guide unit provides only the minimum necessary information. By adjusting the timing of information delivery based on the user's emotions, information can be delivered at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the guide unit may be performed using the generative AI, or not using the generative AI. For example, the guide unit can input user emotion data into the generative AI, which can then adjust the timing of the guide's information provision.

[0083] The guide unit can provide a guide in the most suitable language based on the user's language settings. For example, the guide unit can automatically set the guide language based on the language settings of the user's device. The guide unit provides a language switching function when the user uses multiple languages. When the user selects a specific language, the guide unit provides a guide in that language. For example, the guide unit collects the user's language setting data and uses a generative AI to provide a guide in the most suitable language. The guide unit inputs the user's language setting data as a prompt to the generative AI, and the generative AI provides a guide in the most suitable language. This allows the guide to be provided in the most suitable language based on the user's language settings. Some or all of the above processing in the guide unit may be performed using a generative AI, or not using a generative AI. For example, the guide unit can input the user's language setting data to a generative AI, and the generative AI can provide a guide in the most suitable language.

[0084] The guide unit can provide relevant additional information based on the user's interests. For example, if the user shows interest in a particular tourist destination, the guide unit will provide additional information related to that destination. If the user shows interest in a particular activity, the guide unit will provide additional information related to that activity. If the user shows interest in a particular culture, the guide unit will provide additional information related to that culture. For example, the guide unit can collect user interest data and provide relevant additional information using a generative AI. The guide unit inputs the user's interest data as a prompt into the generative AI, and the generative AI generates the relevant additional information. This allows the guide unit to provide relevant additional information based on the user's interests. Some or all of the above processing in the guide unit may be performed using a generative AI, or without using a generative AI. For example, the guide unit can input user interest data into a generative AI, and the generative AI can generate the relevant additional information.

[0085] Communication functions can estimate the user's emotions and adjust the communication method based on the estimated emotions. For example, a communication function might use facial recognition technology to estimate the user's emotions. This function might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the function might calculate an emotion score based on changes in the user's facial expressions. Alternatively, a communication function could use voice analysis technology to estimate the user's emotions. This function might record the user's voice and estimate their emotions using a voice analysis algorithm. For example, the function might analyze the tone and speed of the user's voice and calculate an emotion score. Furthermore, the communication function adjusts the communication method based on the estimated emotions. For example, if the user is relaxed, it might provide a casual communication method. If the user is excited, it might provide an active communication method. If the user is tired, it might provide a simple communication method. This allows the communication method to be adjusted based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the processing described above in the communication function may be performed using, for example, a generative AI, or not using a generative AI. For example, the communication function can input user emotion data into a generative AI, which can then adjust the method of communication.

[0086] The communication function can analyze the communication history between users and suggest the most suitable communication partner. For example, the communication function can suggest the most suitable communication partner based on who the user has frequently communicated with in the past. The communication function can suggest users with common interests based on the history of group chats the user has participated in in the past. The communication function can suggest compatible users by analyzing the content of past communications the user has had. For example, the communication function can collect the user's communication history data and suggest the most suitable communication partner using a generative AI. The communication function inputs the user's communication history data as a prompt into the generative AI, and the generative AI suggests the most suitable communication partner. This allows for the suggestion of the most suitable communication partner based on the communication history between users. Some or all of the above processing in the communication function may be performed using a generative AI, or not using a generative AI. For example, the communication function can input the user's communication history data into a generative AI, and the generative AI can suggest the most suitable communication partner.

[0087] Communication functions can estimate the user's emotions and adjust the frequency of communication based on those estimated emotions. For example, a communication function might use facial recognition technology to estimate the user's emotions. This could involve capturing the user's facial expressions with a camera and using an emotion estimation algorithm to estimate their emotions. For instance, the function might calculate an emotion score based on changes in the user's facial expressions. Alternatively, a communication function could use voice analysis technology to estimate the user's emotions. This could involve recording the user's voice and using a voice analysis algorithm to estimate their emotions. For example, the function might analyze the tone and speed of the user's voice to calculate an emotion score. Furthermore, the communication function adjusts the frequency of communication based on the estimated emotions. For example, if the user is relaxed, it might encourage communication at an appropriate frequency. If the user is excited, it might encourage communication more frequently. If the user is tired, it might reduce the frequency of communication. This allows for adjustment of communication frequency based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the processing described above in the communication function may be performed using, for example, a generative AI, or not using a generative AI. For example, the communication function may input user sentiment data into a generative AI, which can then adjust the frequency of communication.

[0088] The communication function can provide the optimal means of communication based on the user's device information. For example, if the user is using a smartphone, the communication function can provide chat or voice calls. If the user is using a tablet, the communication function can provide video calls or chat. If the user is using a desktop, the communication function can provide video calls or chat. For example, the communication function collects the user's device information and uses a generative AI to provide the optimal means of communication. The communication function inputs the user's device information as a prompt into the generative AI, and the generative AI provides the optimal means of communication. This allows the optimal means of communication to be provided based on the user's device information. Some or all of the above processing in the communication function may be performed using a generative AI, or it may be performed without using a generative AI. For example, the communication function can input the user's device information into a generative AI, and the generative AI can provide the optimal means of communication.

[0089] The digital item saving and exchange function can estimate the user's emotions and suggest digital items based on those estimated emotions. For example, the function can estimate user emotions using facial recognition technology. It captures the user's facial expressions with a camera and estimates emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in the user's facial expressions. The function can also estimate user emotions using voice analysis technology. It records the user's voice and estimates emotions using a voice analysis algorithm. For example, it analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the function suggests digital items based on the estimated user emotions. For example, if the user is relaxed, it suggests relaxing digital items. If the user is excited, it suggests items that increase excitement. If the user is tired, it suggests relaxing digital items. This allows for the suggestion of digital items based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the digital item storage and exchange function may be performed using a generative AI or not. For example, the digital item storage and exchange function can input user emotion data into a generative AI, which can then suggest digital items.

[0090] The digital item saving and exchange function can analyze a user's past digital item collection history and suggest the most suitable items. For example, the digital item saving and exchange function can suggest similar items based on digital items the user has collected in the past. The digital item saving and exchange function can suggest highly rated items based on digital items the user has given high ratings to in the past. The digital item saving and exchange function can suggest exchangeable items based on digital items the user has exchanged in the past. For example, the digital item saving and exchange function collects the user's past digital item collection history data and suggests the most suitable items using a generating AI. The digital item saving and exchange function inputs the user's past digital item collection history data as a prompt into the generating AI, and the generating AI suggests the most suitable items. This allows the system to suggest the most suitable items based on the user's past digital item collection history. Some or all of the above processes in the digital item saving and exchange function may be performed using a generating AI, or not using a generating AI. For example, the digital item saving and exchange function can input the user's past digital item collection history data into the generating AI, and the generating AI can suggest the most suitable items.

[0091] The digital item saving and exchange function can estimate the user's emotions and adjust the display method of digital items based on the estimated emotions. For example, the digital item saving and exchange function can estimate the user's emotions using facial recognition technology. It can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the digital item saving and exchange function calculates an emotion score based on changes in the user's facial expressions. Furthermore, the digital item saving and exchange function can also estimate the user's emotions using voice analysis technology. It can record the user's voice and estimate emotions using a voice analysis algorithm. For example, it can analyze the tone and speed of the user's voice and calculate an emotion score. In addition, the digital item saving and exchange function adjusts the display method of digital items based on the estimated emotions. For example, if the user is relaxed, it provides a display method with calm colors. If the user is excited, it provides a display method with vibrant colors. If the user is tired, it provides a display method with high visibility. This allows the display method of digital items to be adjusted 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 digital item saving and exchange function may be performed using a generative AI or not. For example, the digital item saving and exchange function can input user emotion data into a generative AI, which can then adjust the display method of the digital items.

[0092] The digital item saving and exchange function can suggest highly relevant digital items based on the user's geographical location information. For example, the digital item saving and exchange function can suggest digital items related to tourist destinations close to the user's current location. The digital item saving and exchange function can suggest digital items related to tourist destinations easily accessible from the user's current location. The digital item saving and exchange function can suggest the most relevant digital items considering the user's travel time from their current location. For example, the digital item saving and exchange function collects the user's geographical location information and suggests highly relevant digital items using a generating AI. The digital item saving and exchange function inputs the user's geographical location information as a prompt into the generating AI, and the generating AI suggests highly relevant digital items. This allows the system to suggest highly relevant digital items based on the user's geographical location information. Some or all of the above-described processes in the digital item saving and exchange function may be performed using a generating AI, or without using a generating AI. For example, the digital item saving and exchange function can input the user's geographical location information into a generating AI, and the generating AI can suggest highly relevant digital items.

[0093] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0094] The generation unit can monitor the user's health status and adjust the itinerary based on that status. For example, the generation unit can measure the user's heart rate and blood pressure to assess their health status. The generation unit can then suggest relaxing tourist destinations and activities based on the user's health status. For example, if the user's heart rate is high, the generation unit will prioritize suggesting relaxing tourist destinations. If the user's health status is good, the generation unit can suggest active activities. This allows for the provision of a more appropriate itinerary based on the user's health status.

[0095] The Tourism Department can provide virtual tours tailored to specific themes based on user interests. For example, it can offer virtual tours of historical sites or nature-based tours. Based on the user's chosen theme, the Tourism Department can suggest relevant tourist destinations and activities. For instance, if a user is interested in history, the Tourism Department can suggest a tour of historical sites. If a user wants to enjoy nature, the Tourism Department can suggest a nature-based tour. This allows the Tourism Department to provide virtual tours tailored to specific themes based on user interests.

[0096] The Ministry of Tourism can estimate users' emotions and adjust the acoustic environment of tourist destinations based on those estimated emotions. For example, the Ministry of Tourism can estimate users' emotions using facial recognition technology. The Ministry of Tourism can capture users' facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the Ministry of Tourism can calculate an emotion score based on changes in the user's facial expressions. The Ministry of Tourism can also estimate users' emotions using voice analysis technology. The Ministry of Tourism can record users' voices and estimate their emotions using a voice analysis algorithm. For example, the Ministry of Tourism can analyze the tone and speed of the user's voice and calculate an emotion score. Furthermore, the Ministry of Tourism can adjust the acoustic environment of tourist destinations based on the estimated user emotions. For example, if the user is relaxed, the Ministry of Tourism can provide calming music. If the user is excited, the Ministry of Tourism can provide lively music. If the user is tired, the Ministry of Tourism can provide soothing music. In this way, by adjusting the acoustic environment of tourist destinations based on users' emotions, a more appropriate acoustic environment can be provided.

[0097] The generation unit can analyze a user's past travel history and propose the optimal itinerary. For example, the generation unit can suggest similar tourist destinations based on the tourist destinations the user has visited in the past. The generation unit can suggest similar activities based on the activities the user has participated in in the past. The generation unit can suggest highly-rated tourist destinations based on the user's ratings of tourist destinations they have visited in the past. For example, the generation unit collects data on tourist destinations the user has visited in the past and generates the optimal itinerary using a generation AI. The generation unit inputs the user's past travel history as a prompt into the generation AI, and the generation AI generates the optimal itinerary. In this way, it can propose the optimal itinerary based on the user's past travel history.

[0098] The generation unit can collect user feedback in real time and instantly adjust the itinerary. For example, the generation unit can improve the itinerary based on user evaluations. The generation unit can improve the itinerary based on user comments. The generation unit can improve the itinerary based on user responses to questionnaires. For example, the generation unit collects user feedback data and instantly adjusts the itinerary using a generation AI. The generation unit inputs user feedback data as a prompt into the generation AI, which then instantly adjusts the itinerary. This allows for instant adjustment of the itinerary content based on user feedback.

[0099] The Ministry of Tourism can estimate a user's emotions and adjust how tourist attractions are displayed based on those emotions. For example, the Ministry of Tourism can estimate a user's emotions using facial recognition technology. The Ministry of Tourism can capture a user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. For example, the Ministry of Tourism can calculate an emotion score based on changes in the user's facial expression. The Ministry of Tourism can also estimate a user's emotions using voice analysis technology. The Ministry of Tourism can record a user's voice and estimate their emotions using a voice analysis algorithm. For example, the Ministry of Tourism can analyze the tone and speed of the user's voice and calculate an emotion score. Furthermore, the Ministry of Tourism can adjust how tourist attractions are displayed based on the estimated emotions of the user. For example, if the user is relaxed, the Ministry of Tourism can provide a display method with calm colors. If the user is excited, the Ministry of Tourism can provide a display method with bright colors. If the user is tired, the Ministry of Tourism can provide a display method with high visibility. In this way, by adjusting how tourist attractions are displayed based on the user's emotions, a more appropriate display can be provided.

[0100] The guide unit can estimate the user's emotions and adjust the tone and expression of the guide based on the estimated emotions. For example, the guide unit can estimate the user's emotions using facial recognition technology. The guide unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the guide unit calculates an emotion score based on changes in the user's facial expressions. The guide unit can also estimate the user's emotions using voice analysis technology. The guide unit records the user's voice and estimates the emotions using a voice analysis algorithm. For example, the guide unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the guide unit adjusts the tone and expression of the guide based on the estimated emotions of the user. For example, if the user is relaxed, the guide unit will provide guidance in a calm tone. If the user is excited, the guide unit will provide guidance in a bright tone. If the user is tired, the guide unit will provide guidance in a gentle tone. In this way, by adjusting the tone and expression of the guide based on the user's emotions, a more appropriate guide can be provided.

[0101] The guide unit can analyze the user's question history and provide more appropriate answers. For example, the guide unit provides relevant information based on the user's past questions. The guide unit provides detailed information based on the user's past questions. The guide unit provides additional information based on the user's past questions. For example, the guide unit collects the user's question history data and uses generative AI to provide more appropriate answers. The guide unit inputs the user's question history data as a prompt into the generative AI, which generates an appropriate answer. This allows the guide unit to provide more appropriate answers based on the user's question history.

[0102] The guide unit can estimate the user's emotions and adjust the timing of information delivery based on the estimated emotions. For example, the guide unit can estimate the user's emotions using facial recognition technology. The guide unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the guide unit calculates an emotion score based on changes in the user's facial expressions. The guide unit can also estimate the user's emotions using voice analysis technology. The guide unit records the user's voice and estimates the emotions using a voice analysis algorithm. For example, the guide unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the guide unit adjusts the timing of information delivery based on the estimated emotions. For example, if the user is relaxed, the guide unit provides information at a relaxed pace. If the user is excited, the guide unit provides information at a rapid pace. If the user is tired, the guide unit provides only the minimum necessary information. By adjusting the timing of information delivery based on the user's emotions, information can be delivered at a more appropriate time.

[0103] The guide unit can provide guides in the most suitable language based on the user's language settings. For example, the guide unit can automatically set the guide language based on the language settings of the user's device. The guide unit provides a language switching function if the user uses multiple languages. If the user selects a specific language, the guide unit will provide the guide in that language. For example, the guide unit can collect the user's language setting data and use a generative AI to provide the guide in the most suitable language. The guide unit inputs the user's language setting data as a prompt to the generative AI, and the generative AI provides the guide in the most suitable language. This allows the guide to be provided in the most suitable language based on the user's language settings.

[0104] The following briefly describes the processing flow for example form 2.

[0105] Step 1: The generation unit creates an itinerary based on the user's interests and preferences. The generation unit analyzes the information on the user's interests and preferences and generates the optimal itinerary. The generation unit can create an itinerary based on the user's interests and preferences using a generation AI. For example, the generation unit inputs information from the user as a prompt into the generation AI, and the generation AI generates the optimal itinerary. Step 2: The Tourism Department provides virtual tourism based on the itinerary created by the Generation Department. The Tourism Department uses a generation AI to generate high-quality 3D spaces in real time and provide them to users. The Tourism Department allows users to freely explore tourist destinations in the virtual space and experience local culture and activities. For example, the Tourism Department inputs a prompt to the generation AI, "Generate a 3D model of this tourist destination," and the generation AI generates a high-quality 3D space. Step 3: The guide unit answers user questions during the virtual tour provided by the tourism unit. The guide unit uses natural language processing to answer user questions and provides information about tourist destinations and culture. The guide unit can use generative AI to generate appropriate answers to user questions. For example, the guide unit inputs the user's question as a prompt into the generative AI, which then generates an appropriate answer.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] Each of the multiple elements described above, including the generation unit, tourism unit, and guide unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The tourism unit is implemented by, for example, the processor 46 of the smart device 14 or the processor 28 of the data processing unit 12. The guide unit is implemented by, for example, the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0110] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the generation unit, sightseeing unit, and guide unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The sightseeing unit is implemented, for example, by the processor 46 of the smart glasses 214 or the processor 28 of the data processing unit 12. The guide unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0126] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the generation unit, the sightseeing unit, and the guide unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The sightseeing unit is implemented, for example, by the processor 46 of the headset terminal 314 or the processor 28 of the data processing unit 12. The guide unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0142] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the generation unit, the sightseeing unit, and the guide unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The sightseeing unit is implemented by, for example, the processor 46 of the robot 414 or the processor 28 of the data processing unit 12. The guide unit is implemented by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] (Note 1) A generation unit that creates itineraries based on the user's interests and preferences, A tourism unit that provides virtual sightseeing based on the itinerary created by the generation unit, The system includes a guide unit that answers user questions during virtual sightseeing provided by the aforementioned tourism unit. A system characterized by the following features. (Note 2) The aforementioned tourism department, Provides features that allow users to communicate with other users. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned tourism department, This feature allows users to save digital items acquired during virtual travel as NFTs and exchange them with other users. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is The system estimates the user's emotions and adjusts the itinerary based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is We analyze the user's past travel history and suggest the optimal itinerary. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is We collect user feedback on the generated itineraries and incorporate it into future itinerary generation. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is It estimates the user's emotions and determines the priority of the itinerary based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is Based on the user's geographical location, we suggest highly relevant tourist destinations. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is Analyze users' social media activity and generate itineraries based on their interests. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned tourism department, The system estimates the user's emotions and adjusts how tourist destinations are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned tourism department, Analyze user behavior in real time while sightseeing and provide engaging content. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned tourism department, Provides detailed information about the historical background and culture of tourist destinations. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned tourism department, It estimates the user's emotions and adjusts the navigation of tourist destinations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned tourism department, Based on the user's device information, the optimal method for displaying the 3D space is selected. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned tourism department, The system suggests relevant tourist destinations based on the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned guide section is The system estimates the user's emotions and adjusts the tone and expression of the guide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned guide section is Analyze the user's question history to provide more appropriate answers. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned guide section is We collect user feedback on the information provided in the guide and incorporate it into future guides. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned guide section is The system estimates the user's emotions and adjusts the timing of guide information delivery based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned guide section is Based on the user's language settings, the guide will be provided in the most suitable language. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned guide section is Provide relevant additional information based on the user's interests. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned communication function is It estimates the user's emotions and adjusts the communication method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned communication function is We analyze the communication history between users and suggest the most suitable communication partner. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned communication function is It estimates the user's emotions and adjusts the frequency of communication based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned communication function is Based on the user's device information, we provide the optimal communication method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned digital item storage and exchange function is It estimates the user's emotions and suggests digital items based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned digital item storage and exchange function is We analyze the user's past digital item collection history and suggest the most suitable items. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned digital item storage and exchange function is It estimates the user's emotions and adjusts how digital items are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned digital item storage and exchange function is Based on the user's geographical location, we suggest highly relevant digital items. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0178] 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 generation unit that creates itineraries based on the user's interests and preferences, A tourism unit that provides virtual sightseeing based on the itinerary created by the generation unit, The system includes a guide unit that answers user questions during virtual sightseeing provided by the aforementioned tourism unit. A system characterized by the following features.

2. The aforementioned tourism department, Provides features that allow users to communicate with other users. The system according to feature 1.

3. The aforementioned tourism department, This feature allows users to save digital items acquired during virtual travel as NFTs and exchange them with other users. The system according to feature 1.

4. The generating unit is The system estimates the user's emotions and adjusts the itinerary based on those emotions. The system according to feature 1.

5. The generating unit is We analyze the user's past travel history and suggest the optimal itinerary. The system according to feature 1.

6. The generating unit is We collect user feedback on the generated itineraries and incorporate it into future itinerary generation. The system according to feature 1.

7. The generating unit is It estimates the user's emotions and determines the priority of the itinerary based on those estimated emotions. The system according to feature 1.

8. The generating unit is Based on the user's geographical location, we suggest highly relevant tourist destinations. The system according to feature 1.

Images