system

Abstract

The system according to this embodiment aims to allow users to experience the historical background of a specific place or time period. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a navigation unit. The reception unit receives input of the desired location and time period. The generation unit learns map information and historical background based on the information received by the reception unit and implements a metaverse space. The navigation unit guides the user through the metaverse space implemented by the generation unit via a VR device.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document

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 experience the historical background of a specific place or era.

[0005] The system according to the embodiment aims to enable the experience of the historical background of a specific place or era.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a navigation unit. The reception unit receives an input of a place and an era to go to. The generation unit learns map information and historical background based on the information received by the reception unit and implements a metaverse space. The navigation unit guides the metaverse space implemented by the generation unit through a VR device. [Effects of the Invention]

[0007] The system according to this embodiment can enable users to experience the historical background of a specific place or time period. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The metaverse navigation system according to an embodiment of the present invention is a system in which, upon receiving a request for a desired location and time period, a generative AI, having learned map information and historical background, implements a metaverse space and guides the user through a VR device. This metaverse navigation system can fulfill requests such as "I want to go to New York 100 years ago" or "I want to go to Paris 30 years from now." This system targets people interested in travel, history, VR, and the metaverse. First, the user inputs the desired location and time period. For example, they might input "I want to go to New York 100 years ago." This information is input to the generative AI. The generative AI learns data such as map information and historical background and implements a metaverse space based on the specified location and time period. Next, the generative AI provides the user with the metaverse space it has implemented through a VR device. The user puts on the VR device and can experience the specified location and time period in the virtual space. For example, the user can walk around the streets of New York 100 years ago and feel the atmosphere of that era. This system allows users to virtually travel to specific locations in the past and future, experiencing historical context and future predictions. This can deepen interest in travel and history, and provide new experiences utilizing VR technology. The metaverse navigation system generates a metaverse space based on the user's desired location and time period, and guides them through a VR device.

[0029] The metaverse guidance system according to this embodiment comprises a reception unit, a generation unit, and a navigation unit. The reception unit receives input of a desired location and time period. Desired locations include, but are not limited to, cities, tourist destinations, and historical buildings. Time periods include, but are not limited to, the year AD, BC, and specific eras. The generation unit learns map information and historical background based on the information received by the reception unit and implements a metaverse space. The generation unit uses, for example, machine learning and deep learning techniques to learn map information and historical background. The generation unit implements a metaverse space based on the specified location and time period. The navigation unit guides the user through the metaverse space implemented by the generation unit via a VR device. The navigation unit uses, for example, streaming and downloading methods to provide the generated metaverse space to the VR device. The navigation unit manages the user's interactions within the virtual space. The navigation unit manages interactions in which the system responds to user actions. As a result, the metaverse guidance system according to this embodiment can generate a metaverse space based on the user's desired destination and age group, and guide them through a VR device.

[0030] The reception desk receives input from users regarding the place and time period they wish to visit. The desired place includes, but is not limited to, cities, tourist attractions, and historical buildings. For example, users can specify a particular place and time period, such as modern-day New York, ancient Rome, or Edo-period Tokyo. The time period includes, but is not limited to, the AD, BC, or a specific era. Users can enter a specific date, such as Athens in 500 BC or 19th-century Paris. The reception desk accurately receives this input and sends it to the generation desk as information necessary for the next processing step. The reception desk also has the function of centrally managing the information entered by users and storing it in a database. This allows users to refer to their history of previously visited places and time periods, enabling quick responses if they wish to revisit the same place or time period. Furthermore, the reception desk also has the function of suggesting relevant information and recommended places based on the user's input. For example, if a user enters "Medieval Europe," it can suggest specific cities and tourist attractions, expanding the user's options. This allows the reception desk to respond flexibly to user needs, improving the overall usability of the system.

[0031] The generation unit learns map information and historical background based on the information received by the reception unit and implements the metaverse space. The generation unit uses technologies such as machine learning and deep learning to learn map information and historical background. Specifically, the generation unit collects a vast amount of data on a specified location and time period and constructs the metaverse space based on this. For example, it learns in detail the map information of a specified city, the layout of buildings, and the culture and lifestyle of the time, and generates a realistic virtual space. The generation unit analyzes this data and reproduces the metaverse space based on the location and time period specified by the user with high accuracy. The generation unit utilizes AI technology to generate the virtual space that the user experiences in real time and can also customize it according to the user's requests. For example, if the user wants to see a specific building or landscape in detail, it will focus on generating that part, enriching the user's experience. In addition, the generation unit improves the quality of the virtual space by utilizing not only past data but also current technology and knowledge. As a result, the generation unit can provide a high-quality metaverse space based on the location and time period specified by the user and meet the user's expectations. Furthermore, the generation unit can continuously update the generated metaverse space and incorporate the latest information and technologies, thereby always providing the optimal experience.

[0032] The navigation unit guides the user through the metaverse space implemented by the generation unit via a VR device. The navigation unit uses methods such as streaming and downloading to provide the generated metaverse space to the VR device. Specifically, the navigation unit streams the metaverse space to the user's VR device in real time, allowing the user to experience the virtual space smoothly. Furthermore, it can pre-download parts of the metaverse space as needed, providing a comfortable experience even in unstable communication environments. The navigation unit manages the user's interactions within the virtual space. For example, it controls the system to respond appropriately when the user moves within the virtual space or interacts with specific objects. The navigation unit updates information within the virtual space in real time in response to user actions, ensuring a natural experience. Additionally, the navigation unit can provide customized guidance based on the user's behavior history and preferences. For example, if the user shows interest in a particular place or object, it can display related information or suggest places to visit next. This allows the navigation unit to provide a personalized experience for the user, enriching their exploration within the metaverse space.

[0033] The generation unit includes a learning unit that learns map information and historical background. The learning unit uses technologies such as machine learning and deep learning to learn map information and historical background, for example. The learning unit learns from geographical data, history books, databases, expert knowledge, etc. As a result, the accuracy of the metaverse space is improved by the generation unit learning map information and historical background.

[0034] The navigation unit includes a provisioning unit that provides the generated metaverse space to the VR device. The provisioning unit uses methods such as streaming or downloading to provide the generated metaverse space to the VR device. For example, the provisioning unit streams the generated metaverse space in real time. Alternatively, the provisioning unit can pre-download the generated metaverse space and provide it when the user accesses it. In this way, the navigation unit provides the generated metaverse space to the VR device, allowing the user to experience the virtual space.

[0035] The navigation unit includes a management unit that manages the user's interactions within the virtual space. The management unit manages interactions such as the system responding to user actions. For example, when a user touches a specific object in the virtual space, the management unit displays information about that object. The management unit can also provide information about a specific location when the user moves to that location within the virtual space. In this way, the navigation unit improves the user experience by managing interactions within the virtual space.

[0036] The reception desk analyzes the user's past input history and suggests the optimal input method. For example, the reception desk automatically displays locations and time periods that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, the reception desk can predict and suggest locations and time periods that the user will use at specific times based on their past input history. In this way, the reception desk can suggest the most suitable input method for the user by analyzing past input history.

[0037] The input system presents suggestions based on the user's current interests when they enter a desired location and time period. For example, it might suggest relevant locations and time periods based on historical events the user has recently searched for. It can also suggest relevant time periods based on places the user has recently visited. Furthermore, it can suggest relevant locations and time periods based on books the user has recently read or movies they have recently watched. This improves input efficiency by presenting suggestions based on the user's interests.

[0038] The reception desk prioritizes and presents highly relevant suggestions based on the user's geographical location when they input their desired destination and time period. For example, the reception desk can suggest relevant historical events or future predictions based on the user's current location. It can also suggest relevant time periods based on places the user has visited in the past. Furthermore, the reception desk can suggest nearby historical sites or future predictions based on the user's current location. In this way, the reception desk can present highly relevant suggestions by considering the user's geographical location.

[0039] The reception desk analyzes the user's social media activity when they input their desired destination and age range, and then presents relevant suggestions. For example, the reception desk suggests relevant suggestions based on places and age ranges the user has shown interest in on social media. It can also suggest relevant suggestions based on places and age ranges visited by the user's friends. Furthermore, the reception desk can suggest relevant places and age ranges based on content the user has shared on social media. In this way, the reception desk can present relevant suggestions by analyzing the user's social media activity.

[0040] The generation unit improves accuracy when generating the metaverse space by referencing detailed data of a specified location and time period. For example, the generation unit can generate an accurate metaverse space by referencing historical maps or photographs of a specified location. It can also generate a realistic metaverse space by referencing the culture and architectural styles of a specified time period. Furthermore, the generation unit can recreate the environment by referencing climate data of a specified location and time period. This improves the accuracy of the metaverse space by allowing the generation unit to reference detailed data of the specified location and time period.

[0041] The generation unit customizes the metaverse space when generating it, taking into account the user's past experiences. For example, the generation unit customizes the metaverse space based on data of places the user has visited in the past. Furthermore, the generation unit can generate a metaverse space that incorporates elements likely to interest the user based on their past experiences. In addition, the generation unit can analyze the user's past experiences and propose an optimal metaverse space. In this way, the generation unit can provide a customized metaverse space by considering the user's past experiences.

[0042] The generation unit prioritizes the use of highly relevant data when generating the metaverse space, taking into account the user's geographical location. For example, the generation unit prioritizes the use of relevant historical data based on the user's current location. It can also prioritize the use of relevant data based on places the user has visited in the past. Furthermore, the generation unit can prioritize the use of nearby historical data based on the user's current location. In this way, the generation unit can use highly relevant data by taking the user's geographical location into consideration.

[0043] The generation unit analyzes the user's social media activity and uses relevant data when generating the metaverse space. For example, the generation unit uses relevant data based on the places and ages the user has shown interest in on social media. It can also use relevant data based on the places and ages the user's friends have visited. Furthermore, the generation unit can use relevant data based on the content the user has shared on social media. In this way, the generation unit can use relevant data by analyzing the user's social media activity.

[0044] The navigation unit selects the optimal guidance method by referring to the user's past experience history during navigation. For example, the navigation unit proposes the optimal guidance method based on the navigation methods the user has used in the past. Furthermore, the navigation unit can also propose a navigation method that avoids congestion based on the user's past experience history. In addition, the navigation unit can analyze the user's past experience history and propose the most efficient navigation method. Thus, the navigation unit can select the optimal guidance method by referring to the user's past experience history.

[0045] The navigation system customizes the guidance content based on the user's current interests and preferences during navigation. For example, it can provide relevant guidance based on historical events the user has recently shown interest in. It can also provide relevant guidance based on places the user has recently visited. Furthermore, it can provide relevant guidance based on books the user has recently read or movies the user has recently watched. In this way, the navigation system improves the user experience by customizing the guidance content based on the user's current interests and preferences.

[0046] The navigation unit selects the optimal guidance method during navigation, taking into account the user's geographical location. For example, the navigation unit provides the optimal guidance method based on the user's current location. It can also provide the optimal guidance method based on places the user has visited in the past. Furthermore, the navigation unit can provide nearby guidance based on the user's current location. In this way, the navigation unit can select the optimal guidance method by considering the user's geographical location.

[0047] The navigation unit analyzes the user's social media activity during navigation and provides relevant guidance. For example, the navigation unit provides relevant guidance based on the places and age groups the user has shown interest in on social media. It can also provide relevant guidance based on the places and age groups visited by the user's friends. Furthermore, the navigation unit can provide relevant guidance based on the content the user has shared on social media. In this way, the navigation unit can provide relevant guidance by analyzing the user's social media activity.

[0048] The learning unit optimizes the learning algorithm by referring to past learning data during the learning process. For example, the learning unit sets optimal learning parameters based on past learning data. It can also extract effective learning methods from past learning data and incorporate them into the algorithm. Furthermore, the learning unit can analyze past learning data to improve the accuracy of the learning algorithm. Thus, the learning unit improves the accuracy of the learning algorithm by referring to past learning data.

[0049] The learning unit weights the training data based on data from specified locations and time periods during training. For example, the learning unit weights the training data based on historical data of a specified location. It can also weight the training data based on the culture and architectural style of a specified time period. Furthermore, the learning unit can weight the training data based on climate data of a specified location and time period. As a result, the learning unit improves the accuracy of training by weighting the training data based on data from specified locations and time periods.

[0050] The service provider selects the optimal display method by referring to the user's past experience history when providing the service. For example, the service provider may suggest the optimal display method based on the display methods the user has used in the past. Furthermore, the service provider can also suggest a display method that avoids congestion based on the user's past experience history. In addition, the service provider can analyze the user's past experience history and suggest the most efficient display method. This allows the service provider to select the optimal display method by referring to the user's past experience history.

[0051] The service provider selects the optimal display method at the time of delivery, taking into account the user's geographical location information. For example, the service provider provides the optimal display method based on the user's current location. It can also provide the optimal display method based on places the user has visited in the past. Furthermore, the service provider can provide nearby content based on the user's current location information. This allows the service provider to select the optimal display method by considering the user's geographical location information.

[0052] The management department selects the optimal interaction method by referring to the user's past operation history during interactions within the virtual space. For example, the management department proposes the optimal interaction method based on the interaction methods the user has used in the past. Furthermore, the management department can also propose interaction methods that avoid congestion based on the user's past operation history. In addition, the management department can analyze the user's past operation history and propose the most efficient interaction method. This allows the management department to select the optimal interaction method by referring to the user's past operation history.

[0053] The management department selects the optimal interaction method during interactions within the virtual space, taking into account the user's device information. For example, if the user is using a smartphone, the management department provides an interaction method adapted to the screen size. Furthermore, if the user is using a tablet, the management department can provide an interaction method optimized for larger screens. Additionally, if the user is using a smartwatch, the management department can provide a concise and highly visible interaction method. This allows the management department to select the optimal interaction method by considering the user's device information.

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

[0055] The reception unit can translate user input in real time and support input in multiple languages. For example, if a user inputs "I want to go to New York 100 years ago" in Japanese, the reception unit translates this into English or other languages ​​and sends it to the generation unit. Furthermore, the reception unit can accurately understand input from users in different languages ​​and generate an appropriate metaverse space. In addition, even if a user uses voice input, the reception unit can use speech recognition technology to convert the input into text and translate it. This allows the reception unit to serve a wider range of users, overcoming language barriers.

[0056] The generation unit can monitor the user's health status and adjust the method of generating the metaverse space according to that status. For example, if the user is tired, the generation unit can provide a relaxing environment. If the user is stressed, the generation unit can also generate a metaverse space that incorporates elements to reduce stress. Furthermore, if the user is in good health, the generation unit can generate a metaverse space that provides a more active experience. In this way, the generation unit can provide an optimal metaverse space according to the user's health status.

[0057] The navigation unit can adjust its guidance method according to the user's speed of movement. For example, if the user is walking slowly, the navigation unit will provide detailed directions. If the user is in a hurry, the navigation unit can also provide concise directions that get straight to the point. Furthermore, if the user is using a bicycle or car, the navigation unit can provide guidance methods optimized for that mode of transportation. In this way, the navigation unit can provide the optimal guidance method according to the user's speed of movement.

[0058] The navigation unit can provide special guidance methods to accommodate users with visual or hearing impairments. For example, it can provide audio guidance to visually impaired users and visual guidance to hearing-impaired users. Furthermore, the navigation unit can customize the guidance methods according to the degree of the user's disability. In addition, if a user is using a specific assistive device, the navigation unit can provide guidance methods optimized for that device. This allows the navigation unit to accommodate users with disabilities.

[0059] The reception desk analyzes the user's past input history and suggests the optimal input method. For example, the reception desk automatically displays locations and time periods that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, the reception desk can predict and suggest locations and time periods that the user will use at specific times based on their past input history. In this way, the reception desk can suggest the most suitable input method for the user by analyzing past input history.

[0060] The input system presents suggestions based on the user's current interests when they enter a desired location and time period. For example, it might suggest relevant locations and time periods based on historical events the user has recently searched for. It can also suggest relevant time periods based on places the user has recently visited. Furthermore, it can suggest relevant locations and time periods based on books the user has recently read or movies they have recently watched. This improves input efficiency by presenting suggestions based on the user's interests.

[0061] The reception desk prioritizes and presents highly relevant suggestions based on the user's geographical location when they input their desired destination and time period. For example, the reception desk can suggest relevant historical events or future predictions based on the user's current location. It can also suggest relevant time periods based on places the user has visited in the past. Furthermore, the reception desk can suggest nearby historical sites or future predictions based on the user's current location. In this way, the reception desk can present highly relevant suggestions by considering the user's geographical location.

[0062] The reception desk analyzes the user's social media activity when they input their desired destination and age range, and then presents relevant suggestions. For example, the reception desk suggests relevant suggestions based on places and age ranges the user has shown interest in on social media. It can also suggest relevant suggestions based on places and age ranges visited by the user's friends. Furthermore, the reception desk can suggest relevant places and age ranges based on content the user has shared on social media. In this way, the reception desk can present relevant suggestions by analyzing the user's social media activity.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk accepts input of the desired destination and time period. Destinations include cities, tourist attractions, historical buildings, etc., while time periods include the year AD, BC, or specific historical periods. Step 2: The generation unit learns map information and historical background based on the information received by the reception unit and implements the metaverse space. The generation unit uses technologies such as machine learning and deep learning to implement the metaverse space based on the specified location and time period. Step 3: The navigation unit guides the user through the metaverse space implemented by the generation unit via the VR device. The navigation unit uses methods such as streaming or downloading to provide the generated metaverse space to the VR device. It also manages the user's interactions within the virtual space and manages the system's responses to user actions.

[0065] (Example of form 2) The metaverse navigation system according to an embodiment of the present invention is a system in which, upon receiving a request for a desired location and time period, a generative AI, having learned map information and historical background, implements a metaverse space and guides the user through a VR device. This metaverse navigation system can fulfill requests such as "I want to go to New York 100 years ago" or "I want to go to Paris 30 years from now." This system targets people interested in travel, history, VR, and the metaverse. First, the user inputs the desired location and time period. For example, they might input "I want to go to New York 100 years ago." This information is input to the generative AI. The generative AI learns data such as map information and historical background and implements a metaverse space based on the specified location and time period. Next, the generative AI provides the user with the metaverse space it has implemented through a VR device. The user puts on the VR device and can experience the specified location and time period in the virtual space. For example, the user can walk around the streets of New York 100 years ago and feel the atmosphere of that era. This system allows users to virtually travel to specific locations in the past and future, experiencing historical context and future predictions. This can deepen interest in travel and history, and provide new experiences utilizing VR technology. The metaverse navigation system generates a metaverse space based on the user's desired location and time period, and guides them through a VR device.

[0066] The metaverse guidance system according to this embodiment comprises a reception unit, a generation unit, and a navigation unit. The reception unit receives input of a desired location and time period. Desired locations include, but are not limited to, cities, tourist destinations, and historical buildings. Time periods include, but are not limited to, the year AD, BC, and specific eras. The generation unit learns map information and historical background based on the information received by the reception unit and implements a metaverse space. The generation unit uses, for example, machine learning and deep learning techniques to learn map information and historical background. The generation unit implements a metaverse space based on the specified location and time period. The navigation unit guides the user through the metaverse space implemented by the generation unit via a VR device. The navigation unit uses, for example, streaming and downloading methods to provide the generated metaverse space to the VR device. The navigation unit manages the user's interactions within the virtual space. The navigation unit manages interactions in which the system responds to user actions. As a result, the metaverse guidance system according to this embodiment can generate a metaverse space based on the user's desired destination and age group, and guide them through a VR device.

[0067] The reception desk receives input from users regarding the place and time period they wish to visit. The desired place includes, but is not limited to, cities, tourist attractions, and historical buildings. For example, users can specify a particular place and time period, such as modern-day New York, ancient Rome, or Edo-period Tokyo. The time period includes, but is not limited to, the AD, BC, or a specific era. Users can enter a specific date, such as Athens in 500 BC or 19th-century Paris. The reception desk accurately receives this input and sends it to the generation desk as information necessary for the next processing step. The reception desk also has the function of centrally managing the information entered by users and storing it in a database. This allows users to refer to their history of previously visited places and time periods, enabling quick responses if they wish to revisit the same place or time period. Furthermore, the reception desk also has the function of suggesting relevant information and recommended places based on the user's input. For example, if a user enters "Medieval Europe," it can suggest specific cities and tourist attractions, expanding the user's options. This allows the reception desk to respond flexibly to user needs, improving the overall usability of the system.

[0068] The generation unit learns map information and historical background based on the information received by the reception unit and implements the metaverse space. The generation unit uses technologies such as machine learning and deep learning to learn map information and historical background. Specifically, the generation unit collects a vast amount of data on a specified location and time period and constructs the metaverse space based on this. For example, it learns in detail the map information of a specified city, the layout of buildings, and the culture and lifestyle of the time, and generates a realistic virtual space. The generation unit analyzes this data and reproduces the metaverse space based on the location and time period specified by the user with high accuracy. The generation unit utilizes AI technology to generate the virtual space that the user experiences in real time and can also customize it according to the user's requests. For example, if the user wants to see a specific building or landscape in detail, it will focus on generating that part, enriching the user's experience. In addition, the generation unit improves the quality of the virtual space by utilizing not only past data but also current technology and knowledge. As a result, the generation unit can provide a high-quality metaverse space based on the location and time period specified by the user and meet the user's expectations. Furthermore, the generation unit can continuously update the generated metaverse space and incorporate the latest information and technologies, thereby always providing the optimal experience.

[0069] The navigation unit guides the user through the metaverse space implemented by the generation unit via a VR device. The navigation unit uses methods such as streaming and downloading to provide the generated metaverse space to the VR device. Specifically, the navigation unit streams the metaverse space to the user's VR device in real time, allowing the user to experience the virtual space smoothly. Furthermore, it can pre-download parts of the metaverse space as needed, providing a comfortable experience even in unstable communication environments. The navigation unit manages the user's interactions within the virtual space. For example, it controls the system to respond appropriately when the user moves within the virtual space or interacts with specific objects. The navigation unit updates information within the virtual space in real time in response to user actions, ensuring a natural experience. Additionally, the navigation unit can provide customized guidance based on the user's behavior history and preferences. For example, if the user shows interest in a particular place or object, it can display related information or suggest places to visit next. This allows the navigation unit to provide a personalized experience for the user, enriching their exploration within the metaverse space.

[0070] The generation unit includes a learning unit that learns map information and historical background. The learning unit uses technologies such as machine learning and deep learning to learn map information and historical background, for example. The learning unit learns from geographical data, history books, databases, expert knowledge, etc. As a result, the accuracy of the metaverse space is improved by the generation unit learning map information and historical background.

[0071] The navigation unit includes a provisioning unit that provides the generated metaverse space to the VR device. The provisioning unit uses methods such as streaming or downloading to provide the generated metaverse space to the VR device. For example, the provisioning unit streams the generated metaverse space in real time. Alternatively, the provisioning unit can pre-download the generated metaverse space and provide it when the user accesses it. In this way, the navigation unit provides the generated metaverse space to the VR device, allowing the user to experience the virtual space.

[0072] The navigation unit includes a management unit that manages the user's interactions within the virtual space. The management unit manages interactions such as the system responding to user actions. For example, when a user touches a specific object in the virtual space, the management unit displays information about that object. The management unit can also provide information about a specific location when the user moves to that location within the virtual space. In this way, the navigation unit improves the user experience by managing interactions within the virtual space.

[0073] The reception desk estimates the user's emotions and adjusts the input method for desired destinations and age groups based on the estimated emotions. For example, if the user is excited, the reception desk provides a visually appealing interface to make the input process enjoyable. If the user is tired, the reception desk can also provide a simple and intuitive interface to facilitate the input process. Furthermore, if the user is feeling anxious, the reception desk can provide a guided input process to provide reassurance. In this way, the reception desk improves user convenience by adjusting the input method according to 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) or multimodal generation AI.

[0074] The reception desk analyzes the user's past input history and suggests the optimal input method. For example, the reception desk automatically displays locations and time periods that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, the reception desk can predict and suggest locations and time periods that the user will use at specific times based on their past input history. In this way, the reception desk can suggest the most suitable input method for the user by analyzing past input history.

[0075] The input system presents suggestions based on the user's current interests when they enter a desired location and time period. For example, it might suggest relevant locations and time periods based on historical events the user has recently searched for. It can also suggest relevant time periods based on places the user has recently visited. Furthermore, it can suggest relevant locations and time periods based on books the user has recently read or movies they have recently watched. This improves input efficiency by presenting suggestions based on the user's interests.

[0076] The reception desk estimates the user's emotions and prioritizes input based on those emotions. For example, if the user is in a hurry, the reception desk will prioritize displaying the most important input items. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is stressed, the reception desk can minimize the input steps and provide a simple interface. This improves input efficiency by prioritizing input according to 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) or multimodal generation AI.

[0077] The reception desk prioritizes and presents highly relevant suggestions based on the user's geographical location when they input their desired destination and time period. For example, the reception desk can suggest relevant historical events or future predictions based on the user's current location. It can also suggest relevant time periods based on places the user has visited in the past. Furthermore, the reception desk can suggest nearby historical sites or future predictions based on the user's current location. In this way, the reception desk can present highly relevant suggestions by considering the user's geographical location.

[0078] The reception desk analyzes the user's social media activity when they input their desired destination and age range, and then presents relevant suggestions. For example, the reception desk suggests relevant suggestions based on places and age ranges the user has shown interest in on social media. It can also suggest relevant suggestions based on places and age ranges visited by the user's friends. Furthermore, the reception desk can suggest relevant places and age ranges based on content the user has shared on social media. In this way, the reception desk can present relevant suggestions by analyzing the user's social media activity.

[0079] The generation unit estimates the user's emotions and adjusts the way the metaverse space is generated based on the estimated emotions. For example, if the user is relaxed, the generation unit generates a metaverse space that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also generate a metaverse space that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a metaverse space with visually stimulating effects. In this way, the generation unit improves the user experience by adjusting the way the metaverse space is generated according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The generation unit improves accuracy when generating the metaverse space by referencing detailed data of a specified location and time period. For example, the generation unit can generate an accurate metaverse space by referencing historical maps or photographs of a specified location. It can also generate a realistic metaverse space by referencing the culture and architectural styles of a specified time period. Furthermore, the generation unit can recreate the environment by referencing climate data of a specified location and time period. This improves the accuracy of the metaverse space by allowing the generation unit to reference detailed data of the specified location and time period.

[0081] The generation unit customizes the metaverse space when generating it, taking into account the user's past experiences. For example, the generation unit customizes the metaverse space based on data of places the user has visited in the past. Furthermore, the generation unit can generate a metaverse space that incorporates elements likely to interest the user based on their past experiences. In addition, the generation unit can analyze the user's past experiences and propose an optimal metaverse space. In this way, the generation unit can provide a customized metaverse space by considering the user's past experiences.

[0082] The generator estimates the user's emotions and determines the priority of the metaverse space to generate based on the estimated emotions. For example, if the user is in a hurry, the generator will prioritize generating the most important elements. If the user is relaxed, the generator can also generate a metaverse space that includes detailed elements. Furthermore, if the user is excited, the generator can prioritize generating visually stimulating elements. In this way, the generator improves the user experience by prioritizing the metaverse space according to 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) or multimodal generation AI.

[0083] The generation unit prioritizes the use of highly relevant data when generating the metaverse space, taking into account the user's geographical location. For example, the generation unit prioritizes the use of relevant historical data based on the user's current location. It can also prioritize the use of relevant data based on places the user has visited in the past. Furthermore, the generation unit can prioritize the use of nearby historical data based on the user's current location. In this way, the generation unit can use highly relevant data by taking the user's geographical location into consideration.

[0084] The generation unit analyzes the user's social media activity and uses relevant data when generating the metaverse space. For example, the generation unit uses relevant data based on the places and ages the user has shown interest in on social media. It can also use relevant data based on the places and ages the user's friends have visited. Furthermore, the generation unit can use relevant data based on the content the user has shared on social media. In this way, the generation unit can use relevant data by analyzing the user's social media activity.

[0085] The navigation unit estimates the user's emotions and adjusts the navigation method based on the estimated emotions. For example, if the user is tense, the navigation unit provides a simple and highly visible navigation method. If the user is relaxed, the navigation unit can also provide a navigation method that includes detailed information. Furthermore, if the user is in a hurry, the navigation unit can provide a concise navigation method. In this way, the navigation unit improves the user experience by adjusting the navigation method according to 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.

[0086] The navigation unit selects the optimal guidance method by referring to the user's past experience history during navigation. For example, the navigation unit proposes the optimal guidance method based on the navigation methods the user has used in the past. Furthermore, the navigation unit can also propose a navigation method that avoids congestion based on the user's past experience history. In addition, the navigation unit can analyze the user's past experience history and propose the most efficient navigation method. Thus, the navigation unit can select the optimal guidance method by referring to the user's past experience history.

[0087] The navigation system customizes the guidance content based on the user's current interests and preferences during navigation. For example, it can provide relevant guidance based on historical events the user has recently shown interest in. It can also provide relevant guidance based on places the user has recently visited. Furthermore, it can provide relevant guidance based on books the user has recently read or movies the user has recently watched. In this way, the navigation system improves the user experience by customizing the guidance content based on the user's current interests and preferences.

[0088] The navigation unit estimates the user's emotions and determines navigation priorities based on the estimated emotions. For example, if the user is in a hurry, the navigation unit will prioritize displaying the most important information. It can also provide detailed information if the user is relaxed. Furthermore, if the user is excited, the navigation unit can prioritize displaying visually stimulating information. This improves the user experience by prioritizing navigation according to 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) or multimodal generation AI.

[0089] The navigation unit selects the optimal guidance method during navigation, taking into account the user's geographical location. For example, the navigation unit provides the optimal guidance method based on the user's current location. It can also provide the optimal guidance method based on places the user has visited in the past. Furthermore, the navigation unit can provide nearby guidance based on the user's current location. In this way, the navigation unit can select the optimal guidance method by considering the user's geographical location.

[0090] The navigation unit analyzes the user's social media activity during navigation and provides relevant guidance. For example, the navigation unit provides relevant guidance based on the places and age groups the user has shown interest in on social media. It can also provide relevant guidance based on the places and age groups visited by the user's friends. Furthermore, the navigation unit can provide relevant guidance based on the content the user has shared on social media. In this way, the navigation unit can provide relevant guidance by analyzing the user's social media activity.

[0091] The learning unit estimates the user's emotions and selects training data based on the estimated emotions. For example, if the user is relaxed, the learning unit will select data that includes detailed historical background. If the user is in a hurry, the learning unit can also select concise data that gets straight to the point. Furthermore, if the user is excited, the learning unit can select visually stimulating data. In this way, the learning unit improves the accuracy of learning by selecting training data according to 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) or multimodal generation AI.

[0092] The learning unit optimizes the learning algorithm by referring to past learning data during the learning process. For example, the learning unit sets optimal learning parameters based on past learning data. It can also extract effective learning methods from past learning data and incorporate them into the algorithm. Furthermore, the learning unit can analyze past learning data to improve the accuracy of the learning algorithm. Thus, the learning unit improves the accuracy of the learning algorithm by referring to past learning data.

[0093] The learning unit estimates the user's emotions and adjusts the learning frequency based on the estimated emotions. For example, if the user is relaxed, the learning unit increases the learning frequency to provide detailed data. If the user is in a hurry, the learning unit can also decrease the learning frequency to provide concise data. Furthermore, if the user is excited, the learning unit can adjust the learning frequency to provide visually stimulating data. In this way, the learning unit improves learning efficiency by adjusting the learning frequency according to 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) or multimodal generation AI.

[0094] The learning unit weights the training data based on data from specified locations and time periods during training. For example, the learning unit weights the training data based on historical data of a specified location. It can also weight the training data based on the culture and architectural style of a specified time period. Furthermore, the learning unit can weight the training data based on climate data of a specified location and time period. As a result, the learning unit improves the accuracy of training by weighting the training data based on data from specified locations and time periods.

[0095] The service provider estimates the user's emotions and adjusts the display method of the metaverse space based on the estimated emotions. For example, if the user is relaxed, the service provider provides a display method that includes detailed information. If the user is in a hurry, the service provider can also provide a display method that gets straight to the point. Furthermore, if the user is excited, the service provider can provide a display method that is visually stimulating. In this way, the service provider improves the user experience by adjusting the display method of the metaverse space according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, 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.

[0096] The service provider selects the optimal display method by referring to the user's past experience history when providing the service. For example, the service provider may suggest the optimal display method based on the display methods the user has used in the past. Furthermore, the service provider can also suggest a display method that avoids congestion based on the user's past experience history. In addition, the service provider can analyze the user's past experience history and suggest the most efficient display method. This allows the service provider to select the optimal display method by referring to the user's past experience history.

[0097] The service provider estimates the user's emotions and determines the priority of the metaverse space to be provided based on the estimated emotions. For example, if the user is in a hurry, the service provider will prioritize displaying the most important elements. If the user is relaxed, the service provider may also provide a metaverse space with more detailed elements. Furthermore, if the user is excited, the service provider may prioritize displaying visually stimulating elements. In this way, the service provider improves the user experience by prioritizing the metaverse space according to 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) or multimodal generation AI.

[0098] The service provider selects the optimal display method at the time of delivery, taking into account the user's geographical location information. For example, the service provider provides the optimal display method based on the user's current location. It can also provide the optimal display method based on places the user has visited in the past. Furthermore, the service provider can provide nearby content based on the user's current location information. This allows the service provider to select the optimal display method by considering the user's geographical location information.

[0099] The management unit estimates the user's emotions and adjusts the interaction within the virtual space based on the estimated emotions. For example, if the user is relaxed, the management unit provides detailed interactions. If the user is in a hurry, the management unit can provide concise interactions. Furthermore, if the user is excited, the management unit can provide visually stimulating interactions. In this way, the management unit improves the user experience by adjusting the interaction within the virtual space according to 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) or multimodal generation AI.

[0100] The management department selects the optimal interaction method by referring to the user's past operation history during interactions within the virtual space. For example, the management department proposes the optimal interaction method based on the interaction methods the user has used in the past. Furthermore, the management department can also propose interaction methods that avoid congestion based on the user's past operation history. In addition, the management department can analyze the user's past operation history and propose the most efficient interaction method. This allows the management department to select the optimal interaction method by referring to the user's past operation history.

[0101] The management system estimates the user's emotions and prioritizes interactions based on those emotions. For example, if the user is in a hurry, the management system will prioritize the most important interactions. If the user is relaxed, the management system may also provide more detailed interactions. Furthermore, if the user is excited, the management system may prioritize visually stimulating interactions. This improves the user experience by prioritizing interactions according to 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) or multimodal generation AI.

[0102] The management department selects the optimal interaction method during interactions within the virtual space, taking into account the user's device information. For example, if the user is using a smartphone, the management department provides an interaction method adapted to the screen size. Furthermore, if the user is using a tablet, the management department can provide an interaction method optimized for larger screens. Additionally, if the user is using a smartwatch, the management department can provide a concise and highly visible interaction method. This allows the management department to select the optimal interaction method by considering the user's device information.

[0103] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0104] The reception unit can translate user input in real time and support input in multiple languages. For example, if a user inputs "I want to go to New York 100 years ago" in Japanese, the reception unit translates this into English or other languages ​​and sends it to the generation unit. Furthermore, the reception unit can accurately understand input from users in different languages ​​and generate an appropriate metaverse space. In addition, even if a user uses voice input, the reception unit can use speech recognition technology to convert the input into text and translate it. This allows the reception unit to serve a wider range of users, overcoming language barriers.

[0105] The generation unit can monitor the user's health status and adjust the method of generating the metaverse space according to that status. For example, if the user is tired, the generation unit can provide a relaxing environment. If the user is stressed, the generation unit can also generate a metaverse space that incorporates elements to reduce stress. Furthermore, if the user is in good health, the generation unit can generate a metaverse space that provides a more active experience. In this way, the generation unit can provide an optimal metaverse space according to the user's health status.

[0106] The navigation unit can adjust its guidance method according to the user's speed of movement. For example, if the user is walking slowly, the navigation unit will provide detailed directions. If the user is in a hurry, the navigation unit can also provide concise directions that get straight to the point. Furthermore, if the user is using a bicycle or car, the navigation unit can provide guidance methods optimized for that mode of transportation. In this way, the navigation unit can provide the optimal guidance method according to the user's speed of movement.

[0107] The navigation unit can provide special guidance methods to accommodate users with visual or hearing impairments. For example, it can provide audio guidance to visually impaired users and visual guidance to hearing-impaired users. Furthermore, the navigation unit can customize the guidance methods according to the degree of the user's disability. In addition, if a user is using a specific assistive device, the navigation unit can provide guidance methods optimized for that device. This allows the navigation unit to accommodate users with disabilities.

[0108] The reception desk estimates the user's emotions and adjusts the input method for desired destinations and age groups based on those estimated emotions. For example, if the user is excited, the reception desk provides a visually appealing interface to make the input process enjoyable. If the user is tired, the reception desk can also provide a simple and intuitive interface to facilitate the input process. Furthermore, if the user is feeling anxious, the reception desk can provide a guided input process to reassure them. In this way, the reception desk improves user convenience by adjusting the input method according to the user's emotions.

[0109] The reception desk analyzes the user's past input history and suggests the optimal input method. For example, the reception desk automatically displays locations and time periods that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, the reception desk can predict and suggest locations and time periods that the user will use at specific times based on their past input history. In this way, the reception desk can suggest the most suitable input method for the user by analyzing past input history.

[0110] The input system presents suggestions based on the user's current interests when they enter a desired location and time period. For example, it might suggest relevant locations and time periods based on historical events the user has recently searched for. It can also suggest relevant time periods based on places the user has recently visited. Furthermore, it can suggest relevant locations and time periods based on books the user has recently read or movies they have recently watched. This improves input efficiency by presenting suggestions based on the user's interests.

[0111] The reception desk estimates the user's emotions and prioritizes input based on those emotions. For example, if the user is in a hurry, the reception desk will prioritize displaying the most important input fields. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is stressed, the reception desk can minimize the input steps and provide a simple interface. In this way, the reception desk improves input efficiency by prioritizing input according to the user's emotions.

[0112] The reception desk prioritizes and presents highly relevant suggestions based on the user's geographical location when they input their desired destination and time period. For example, the reception desk can suggest relevant historical events or future predictions based on the user's current location. It can also suggest relevant time periods based on places the user has visited in the past. Furthermore, the reception desk can suggest nearby historical sites or future predictions based on the user's current location. In this way, the reception desk can present highly relevant suggestions by considering the user's geographical location.

[0113] The reception desk analyzes the user's social media activity when they input their desired destination and age range, and then presents relevant suggestions. For example, the reception desk suggests relevant suggestions based on places and age ranges the user has shown interest in on social media. It can also suggest relevant suggestions based on places and age ranges visited by the user's friends. Furthermore, the reception desk can suggest relevant places and age ranges based on content the user has shared on social media. In this way, the reception desk can present relevant suggestions by analyzing the user's social media activity.

[0114] The following briefly describes the processing flow for example form 2.

[0115] Step 1: The reception desk accepts input of the desired destination and time period. Destinations include cities, tourist attractions, historical buildings, etc., while time periods include the year AD, BC, or specific historical periods. Step 2: The generation unit learns map information and historical background based on the information received by the reception unit and implements the metaverse space. The generation unit uses technologies such as machine learning and deep learning to implement the metaverse space based on the specified location and time period. Step 3: The navigation unit guides the user through the metaverse space implemented by the generation unit via the VR device. The navigation unit uses methods such as streaming or downloading to provide the generated metaverse space to the VR device. It also manages the user's interactions within the virtual space and manages the system's responses to user actions.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the reception unit, generation unit, and navigation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives input of the user's desired location and time period. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns map information and historical background to implement the metaverse space. The navigation unit is implemented by the control unit 46A of the smart device 14 and provides the generated metaverse space to the VR device. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0120] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0121] 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.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The 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.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 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.

[0128] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the 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.

[0131] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0133] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] The data processing system 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.

[0135] Each of the multiple elements described above, including the reception unit, generation unit, and navigation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives input of the user's desired location and time period. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and learns map information and historical background to implement the metaverse space. The navigation unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated metaverse space to the VR device. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0136] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0137] 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.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The 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.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the reception unit, generation unit, and navigation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives input of the user's desired location and time period. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns map information and historical background to implement the metaverse space. The navigation unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated metaverse space to the VR device. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the reception unit, generation unit, and navigation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives input of the user's desired location and time period. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns map information and historical background to implement the metaverse space. The navigation unit is implemented by the control unit 46A of the robot 414 and provides the generated metaverse space to the VR device. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] (Note 1) A reception desk where you can input the place you want to go and your age group, Based on the information received by the aforementioned reception unit, a generation unit learns map information and historical background, and implements a metaverse space. The system includes a navigation unit that guides users through the metaverse space implemented by the generation unit via a VR device. A system characterized by the following features. (Note 2) The generating unit is It includes a learning section for studying map information and historical background. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned navigation unit is It includes a provisioning unit that provides the generated metaverse space to VR devices. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned navigation unit is It includes a management unit that manages the user's interactions within the virtual space. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates the user's emotions and adjusts the input method for desired destinations and age groups based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When users enter their desired destination and timeframe, the system will suggest input options based on their current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users input their desired destination and age range, the system prioritizes presenting highly relevant suggestions by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users input their desired destination and age range, the system analyzes their social media activity and suggests relevant options. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is It estimates the user's emotions and adjusts the method of generating the metaverse space based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating the metaverse space, accuracy is improved by referencing detailed data on specified locations and time periods. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating the metaverse space, it is customized by taking into account the user's past experience history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and determines the priority of the metaverse space to be generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating the metaverse space, the system prioritizes the use of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating the metaverse space, we analyze users' social media activity and use relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned navigation unit is It estimates the user's emotions and adjusts the navigation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned navigation unit is During navigation, the system selects the optimal guidance method by referring to the user's past experience history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned navigation unit is During navigation, the guidance content is customized based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned navigation unit is It estimates the user's emotions and determines navigation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned navigation unit is During navigation, the system selects the optimal guidance method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned navigation unit is During navigation, the system analyzes the user's social media activity and provides relevant guidance. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 24) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned learning unit, During training, the training data is weighted based on data from specified locations and time periods. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the metaverse space is displayed based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past experience history. The system described in Appendix 3, characterized by the features described herein. (Note 29) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the metaverse space to provide based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, the optimal display method will be selected considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned management department, It estimates the user's emotions and adjusts interactions within the virtual space based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 32) The aforementioned management department, During interactions within a virtual space, the system selects the optimal interaction method by referring to the user's past operation history. The system described in Appendix 4, characterized by the features described herein. (Note 33) The aforementioned management department, It estimates the user's emotions and prioritizes interactions based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned management department, When interacting within a virtual space, the system selects the optimal interaction method by considering the user's device information. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where you can input the place you want to go and your age group, Based on the information received by the aforementioned reception unit, a generation unit learns map information and historical background, and implements a metaverse space. The system includes a navigation unit that guides users through the metaverse space implemented by the generation unit via a VR device. A system characterized by the following features.

2. The generating unit is It includes a learning section for studying map information and historical background. The system according to feature 1.

3. The aforementioned navigation unit is It includes a provisioning unit that provides the generated metaverse space to VR devices. The system according to feature 1.

4. The aforementioned navigation unit is It includes a management unit that manages the user's interactions within the virtual space. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the user's emotions and adjusts the input method for desired destinations and age groups based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

7. The aforementioned reception unit is When users enter their desired destination and timeframe, the system will suggest input options based on their current interests and preferences. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is When users input their desired destination and age range, the system prioritizes presenting highly relevant suggestions by considering the user's geographical location. The system according to feature 1.

10. The aforementioned reception unit is When users input their desired destination and age range, the system analyzes their social media activity and suggests relevant options. The system according to feature 1.

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