system

Abstract

An object of a system according to an embodiment is to enable a user to experience a specific place or a specific age desired by the user in a virtual space.SOLUTION: A system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives an input of a desired place and an age group from the user. The generation unit generates a Metaverse space based on the information received by the reception unit. The providing unit provides the metaverse space generated by the generating unit through the VR device.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of making it difficult for users to experience a specific location or era in a virtual space.

[0005] The system according to the embodiment aims to enable a user to experience a specific place or era of their choice in a virtual space. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives input from a user of a desired location and age. The generation unit generates a metaverse space based on the information received by the reception unit. The provision unit provides the metaverse space generated by the generation unit through a VR device. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user to experience a specific place or era of their choice in a virtual space. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) In an embodiment of the present invention, a metaverse experience system allows a user to input a desired location and time period. The AI ​​then learns map information and historical background, generates a metaverse space, and allows the user to experience it through a VR device. The metaverse experience system allows users to input a desired location and time period. The AI ​​analyzes the information and generates a metaverse space based on the map information and historical background. The generated metaverse space is then provided to the user through a VR device. For example, if a user inputs "I want to go to New York 100 years ago," the AI ​​recreates the streets and buildings of New York 100 years ago, allowing the user to experience it through a VR device. Similarly, if a user inputs "I want to go to Paris 30 years from now," the AI ​​predicts Paris in the future and generates a metaverse space. This allows users to experience specific locations from the past or future, learning about historical background and future predictions. For example, experiencing New York 100 years ago in a history class can help students learn about the lifestyle and culture of that time. Experiencing Paris of the future can also help students predict future urban planning and technological advances. This service, which allows users to experience a metaverse space through a VR device by simply providing the desired location and time period, holds great potential in the fields of education and entertainment. This allows the metaverse experience system to experience specific locations in the past or future based on the user's preferences. For example, experiencing New York 100 years ago in a history class can help students learn about the lifestyle and culture of that time. Experiencing Paris in the future can also help students predict future urban planning and technological advances. This service, which allows users to experience a metaverse space through a VR device by simply providing the desired location and time period, holds great potential in the fields of education and entertainment.

[0029] A metaverse experience system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives input from a user of a desired location and era. For example, a user can input "New York 100 years ago." The generation unit uses a generation AI to generate a metaverse space based on the information received by the reception unit. The generation unit, for example, learns from past maps and historical data and recreates a space that meets the user's wishes. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to learn map information and historical background and generate the metaverse space. For example, the generation AI recreates the streets and buildings of New York 100 years ago based on past maps and historical data. The provision unit provides the metaverse space generated by the generation unit to a user through a VR device. For example, the provision unit allows the user to experience the metaverse space using a VR device such as VR goggles or a head-mounted display. As a result, the metaverse experience system according to an embodiment generates a metaverse space based on the user's wishes and allows the user to experience it through a VR device. For example, users can walk through the streets of New York 100 years ago, or experience Paris of the future, helping to predict future urban planning and technological advances.

[0030] The generation unit includes a learning unit that uses the generation AI to learn map information or historical background. The learning unit learns the map information and historical background using the generation AI. For example, the learning unit collects past maps and historical data and uses them to learn data for generating a metaverse space. The generation AI learns the map information and historical background using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI recreates the streets and buildings of New York 100 years ago based on past maps and historical data. The generation AI can also recreate a future Paris based on future predicted data. This allows the generation AI to learn the map information and historical background, thereby generating a more accurate metaverse space. For example, the generation AI recreates a space that meets the user's wishes based on past maps and historical data. The generation AI can also generate a metaverse space that reflects future urban planning and technological advances based on future predicted data.

[0031] The providing unit can provide the generated metaverse space to a user. The providing unit provides the metaverse space generated by the generating unit to the user. For example, the providing unit allows the user to experience the metaverse space using a VR device such as VR goggles or a head-mounted display. The providing unit has an interface for providing the generated metaverse space to the user. For example, the providing unit allows the user to experience the generated metaverse space by wearing a VR device. The providing unit also has software for providing the generated metaverse space to the user. For example, the providing unit transmits the generated metaverse space to the user's VR device so that the user can experience it. In this way, the generated metaverse space can be provided to the user, allowing the user to experience it. For example, the user can walk around the streets of New York 100 years ago. Furthermore, by experiencing Paris of the future, future urban planning and technological advances can be predicted.

[0032] The generation unit can generate a metaverse space based on past maps or historical data. The generation unit generates a metaverse space based on past maps or historical data. For example, the generation unit collects past maps and historical data and generates a metaverse space based on that data. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to learn from past maps and historical data and generate a metaverse space. For example, the generation AI recreates the streets and buildings of New York 100 years ago based on past maps and historical data. The generation AI can also recreate future Paris based on future predicted data. This enables a more realistic experience by generating a metaverse space based on past maps and historical data. For example, the generation AI recreates a space that meets the user's wishes based on past maps and historical data. The generation AI can also generate a metaverse space that reflects future urban planning and technological advances based on future predicted data.

[0033] The providing unit allows a user to experience the metaverse space through a VR device. The providing unit allows a user to experience the metaverse space through a VR device. For example, the providing unit allows a user to experience the metaverse space using a VR device such as VR goggles or a head-mounted display. The providing unit has an interface for providing the generated metaverse space to a user. For example, the providing unit allows a user to experience the generated metaverse space by wearing a VR device. The providing unit also has software for providing the generated metaverse space to a user. For example, the providing unit transmits the generated metaverse space to the user's VR device so that the user can experience it. This allows the user to experience the metaverse space through a VR device, thereby providing an immersive experience to the user. For example, a user can walk around the streets of New York 100 years ago. Furthermore, by experiencing Paris of the future, future urban planning and technological advances can be predicted.

[0034] The reception unit can analyze the user's past input history and suggest an appropriate input method. The reception unit analyzes the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays locations and years that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest locations and years that will be used during a specific time period from the user's past input history. In this way, the reception unit can suggest the optimal input method for the user by analyzing the past input history. For example, the reception unit suggests the optimal input method based on data that the user has input in the past. The reception unit can also suggest a more appropriate input method by analyzing the user's past input history and understanding the user's preferences and tendencies.

[0035] The reception unit can present input candidates based on the user's current interests or concerns when the user inputs a desired destination and a desired era. The reception unit presents input candidates based on the user's current interests or concerns when the user inputs a desired destination and a desired era. For example, the reception unit suggests related places and eras based on historical events recently searched for by the user. The reception unit can also display places and eras in which the user has expressed interest on social media as candidates. The reception unit can also present new candidates related to places and eras that the user has visited in the past. This makes it possible to provide more appropriate candidates by presenting input candidates based on the user's interests or concerns. For example, the reception unit suggests related places and eras based on historical events recently searched for by the user. The reception unit can also display places and eras in which the user has expressed interest on social media as candidates. The reception unit can also present new candidates related to places and eras that the user has visited in the past.

[0036] The reception unit can select an appropriate input means according to the user's input method when inputting a desired destination and a desired era. The reception unit selects the optimal input means according to the user's input method (voice, text, gesture, etc.). For example, when the user uses voice input, the reception unit utilizes voice recognition technology to input the desired destination and the desired era. Furthermore, when the user uses text input, the reception unit can provide an auto-complete function to assist input. Furthermore, when the user uses gesture input, the reception unit can enable the user to set the desired destination and the desired era with an intuitive gesture. This improves input convenience by selecting the optimal input means according to the user's input method. For example, when the user uses voice input, the reception unit utilizes voice recognition technology to input the desired destination and the desired era. Furthermore, when the user uses text input, the reception unit can provide an auto-complete function to assist input. Furthermore, when the user uses gesture input, the reception unit can enable the user to set the desired destination and the desired era with an intuitive gesture.

[0037] The reception unit can prioritize presenting highly relevant candidates based on the user's geographical location information when the user inputs a desired destination and a desired era. The reception unit prioritizes presenting highly relevant candidates by taking the user's geographical location information into consideration when the user inputs a desired destination and a desired era. For example, the reception unit displays past historical events close to the user's current location as candidates. The reception unit can also display future events that are easily accessible from the user's current location as candidates. The reception unit can also present candidates based on historical background related to the user's current location. In this way, highly relevant candidates can be prioritized by taking the user's geographical location information into consideration. For example, the reception unit displays past historical events close to the user's current location as candidates. The reception unit can also display future events that are easily accessible from the user's current location as candidates. The reception unit can also present candidates based on historical background related to the user's current location.

[0038] The reception unit can analyze the user's social media usage status when the user inputs a desired destination and a desired age group, and present related candidates. The reception unit can analyze the user's social media activity when the user inputs a desired destination and a desired age group, and present related candidates. For example, the reception unit can display age groups related to places the user has checked in to on social media as candidates. The reception unit can also analyze the content of the user's social media posts and present related places and age groups as candidates. The reception unit can also present related places and age groups with reference to the activity of the user's friends on social media. In this way, related candidates can be presented by analyzing the user's social media activity. For example, the reception unit can display age groups related to places the user has checked in to on social media as candidates. The reception unit can also analyze the content of the user's social media posts and present related places and age groups as candidates. The reception unit can also present related places and age groups with reference to the activity of the user's friends on social media.

[0039] The reception unit can customize the input method by reflecting the user's past opinions when inputting a desired destination and a desired era. The reception unit customizes the input method by reflecting the user's past feedback when inputting a desired destination and a desired era. For example, the reception unit preferentially provides an input method that the user has previously preferred. The reception unit can also adjust the input interface based on the user's past feedback. The reception unit can also avoid input methods that the user has previously been dissatisfied with. In this way, the input method can be customized by reflecting the user's past feedback. For example, the reception unit preferentially provides an input method that the user has previously preferred. The reception unit can also adjust the input interface based on the user's past feedback. The reception unit can also avoid input methods that the user has previously been dissatisfied with.

[0040] The generation unit may adjust the level of detail of the map information or historical background when generating the metaverse space. The generation unit adjusts the level of detail of the map information or historical background when generating the metaverse space. For example, if a user desires detailed information, the generation unit may generate a metaverse space that is reproduced in detail. Alternatively, if a user desires concise information, the generation unit may generate a metaverse space that reproduces only major landmarks. Alternatively, if a user is interested in a particular historical event, the generation unit may generate a metaverse space that includes detailed information related to the event. In this way, by adjusting the level of detail of the map information or historical background, a metaverse space that meets the user's needs can be generated. For example, if a user desires detailed information, the generation unit may generate a metaverse space that is reproduced in detail. Alternatively, if a user desires concise information, the generation unit may generate a metaverse space that includes detailed information related to the event.

[0041] When generating a metaverse space, the generation unit can improve the accuracy of the generation based on the user's past experience history. When generating a metaverse space, the generation unit improves the accuracy of the generation by referring to the user's past experience history. For example, the generation unit generates a more accurate metaverse space based on places and eras visited by the user in the past. The generation unit can also generate a metaverse space that reflects the user's preferred style and perspective based on the user's past experience history. The generation unit can also improve the accuracy of the generation based on feedback provided by the user in the past. In this way, the accuracy of the generation is improved by referring to the past experience history. For example, the generation unit generates a more accurate metaverse space based on places and eras visited by the user in the past. The generation unit can also generate a metaverse space that reflects the user's preferred style and perspective based on the user's past experience history. The generation unit can also improve the accuracy of the generation based on feedback provided by the user in the past.

[0042] The generation unit can apply different generation algorithms depending on the user's interests when generating the metaverse space. The generation unit can apply different generation algorithms depending on the user's interests when generating the metaverse space. For example, if the user is interested in historical buildings, the generation unit can apply an algorithm that recreates the buildings in detail. Furthermore, if the user is interested in future technology, the generation unit can apply an algorithm that generates a metaverse space that emphasizes future technology. Furthermore, if the user is interested in a particular culture, the generation unit can apply an algorithm that generates a metaverse space that reflects that culture. In this way, by applying a generation algorithm depending on the user's interests, a more appropriate metaverse space can be generated. For example, if the user is interested in historical buildings, the generation unit can apply an algorithm that recreates the buildings in detail. Furthermore, if the user is interested in future technology, the generation unit can apply an algorithm that generates a metaverse space that emphasizes future technology. Furthermore, if the user is interested in a particular culture, the generation unit can apply an algorithm that generates a metaverse space that reflects that culture.

[0043] When generating a metaverse space, the generation unit can prioritize generating a highly relevant space based on the user's geographical location information. When generating a metaverse space, the generation unit prioritizes generating a highly relevant space by taking into account the user's geographical location information. For example, the generation unit prioritizes generating a past metaverse space related to the user's current location. The generation unit can also prioritize generating a future metaverse space that is easily accessible from the user's current location. The generation unit can also prioritize generating a metaverse space based on historical background related to the user's current location. In this way, by taking into account the user's geographical location information, highly relevant spaces can be prioritized. For example, the generation unit prioritizes generating a past metaverse space related to the user's current location. The generation unit can also prioritize generating a future metaverse space that is easily accessible from the user's current location. The generation unit can also prioritize generating a metaverse space based on historical background related to the user's current location.

[0044] The generation unit may analyze the user's social media usage status and generate a related space when generating the metaverse space. The generation unit may analyze the user's social media activity and generate a related space when generating the metaverse space. For example, the generation unit may generate a metaverse space related to a place where the user has checked in on social media. The generation unit may also analyze the content of the user's social media posts and generate a related metaverse space. The generation unit may also generate a related metaverse space by referring to the activities of the user's friends on social media. In this way, a related space can be generated by analyzing social media activity. For example, the generation unit may generate a metaverse space related to a place where the user has checked in on social media. The generation unit may also analyze the content of the user's social media posts and generate a related metaverse space. The generation unit may also generate a related metaverse space by referring to the activities of the user's friends on social media.

[0045] The generation unit can customize the generation method by reflecting the user's past opinions when generating a metaverse space. The generation unit customizes the generation method by reflecting the user's past feedback when generating a metaverse space. For example, the generation unit preferentially applies a generation method that the user has previously preferred. The generation unit can also adjust the generation algorithm based on the user's past feedback. The generation unit can also avoid generation methods that the user has previously been dissatisfied with. In this way, the generation method can be customized by reflecting past feedback. For example, the generation unit preferentially applies a generation method that the user has previously preferred. The generation unit can also adjust the generation algorithm based on the user's past feedback. The generation unit can also avoid generation methods that the user has previously been dissatisfied with.

[0046] The providing unit can select the optimal delivery method based on the user's past experience history when providing the metaverse space. The providing unit selects the optimal delivery method by referring to the user's past experience history when providing the metaverse space. For example, the providing unit preferentially applies a delivery method that the user has previously preferred. The providing unit can also adjust the delivery method based on the user's past experience history. The providing unit can also avoid delivery methods that the user has previously been dissatisfied with. In this way, the optimal delivery method can be selected by referring to the past experience history. For example, the providing unit preferentially applies a delivery method that the user has previously preferred. The providing unit can also adjust the delivery method based on the user's past experience history. The providing unit can also avoid delivery methods that the user has previously been dissatisfied with.

[0047] The providing unit can customize the content to be provided based on the user's current interests or concerns when providing the metaverse space. The providing unit customizes the content to be provided based on the user's current interests or concerns when providing the metaverse space. For example, the providing unit provides a metaverse space related to a historical event in which the user is currently interested. The providing unit can also provide a metaverse space related to a future technology in which the user is currently interested. The providing unit can also provide a metaverse space related to a culture or region in which the user is currently interested. In this way, by customizing the content to be provided based on the current interests or concerns, a more appropriate metaverse space can be provided. For example, the providing unit provides a metaverse space related to a historical event in which the user is currently interested. The providing unit can also provide a metaverse space related to a future technology in which the user is currently interested. The providing unit can also provide a metaverse space related to a culture or region in which the user is currently interested.

[0048] The providing unit can improve the provision method by reflecting the user's opinions when providing the metaverse space. The providing unit improves the provision method by reflecting the user's feedback when providing the metaverse space. For example, the providing unit adjusts the provision method based on feedback previously provided by the user. The providing unit can also reflect the user's feedback in real time and improve the provision method. The providing unit can also avoid provision methods that the user has been dissatisfied with in the past. In this way, the provision method can be improved by reflecting the feedback. For example, the providing unit adjusts the provision method based on feedback previously provided by the user. The providing unit can also reflect the user's feedback in real time and improve the provision method. The providing unit can also avoid provision methods that the user has been dissatisfied with in the past.

[0049] The providing unit can select an optimal providing method based on the user's geographical location information when providing the metaverse space. The providing unit selects the optimal providing method by taking the user's geographical location information into consideration when providing the metaverse space. For example, the providing unit can prioritize providing a past metaverse space related to the user's current location. The providing unit can also prioritize providing a future metaverse space that is easily accessible from the user's current location. The providing unit can also provide the metaverse space based on historical background related to the user's current location. In this way, the optimal providing method can be selected by taking the geographical location information into consideration. For example, the providing unit can prioritize providing a past metaverse space related to the user's current location. The providing unit can also prioritize providing a future metaverse space that is easily accessible from the user's current location. The providing unit can also provide the metaverse space based on historical background related to the user's current location.

[0050] When providing a metaverse space, the providing unit can analyze the user's social media usage status and suggest content to be provided. When providing a metaverse space, the providing unit analyzes the user's social media activity and suggest content to be provided. For example, the providing unit provides a metaverse space related to a place where the user has checked in on social media. The providing unit can also analyze the content posted by the user on social media and provide a related metaverse space. The providing unit can also provide a related metaverse space by referring to the activity of the user's friends on social media. In this way, content to be provided can be suggested by analyzing social media activity. For example, the providing unit provides a metaverse space related to a place where the user has checked in on social media. The providing unit can also analyze the content posted by the user on social media and provide a related metaverse space. The providing unit can also provide a related metaverse space by referring to the activity of the user's friends on social media.

[0051] The providing unit can customize the delivery method by reflecting the user's past opinions when providing the metaverse space. The providing unit customizes the delivery method by reflecting the user's past feedback when providing the metaverse space. For example, the providing unit preferentially applies a delivery method that the user has previously preferred. The providing unit can also adjust the delivery method based on the user's past feedback. The providing unit can also avoid delivery methods that the user has previously been dissatisfied with. In this way, the delivery method can be customized by reflecting past feedback. For example, the providing unit preferentially applies a delivery method that the user has previously preferred. The providing unit can also adjust the delivery method based on the user's past feedback. The providing unit can also avoid delivery methods that the user has previously been dissatisfied with.

[0052] The learning unit can optimize the learning algorithm based on past learning data during learning. The learning unit optimizes the learning algorithm by referring to past learning data during learning. For example, the learning unit selects an optimal learning algorithm based on past learning data. The learning unit can also apply an algorithm that improves learning accuracy from the past learning data. The learning unit can also analyze past learning data and adjust the learning algorithm. In this way, the learning algorithm can be optimized by referring to past learning data. For example, the learning unit selects an optimal learning algorithm based on past learning data. The learning unit can also apply an algorithm that improves learning accuracy from the past learning data. The learning unit can also analyze past learning data and adjust the learning algorithm.

[0053] The learning unit can update the learning data by reflecting user opinions during learning. The learning unit can update the learning data by reflecting user feedback during learning. For example, the learning unit adds learning data based on user feedback. The learning unit can also update the learning data by reflecting user feedback in real time. The learning unit can also analyze user feedback to improve the quality of the learning data. In this way, the learning data can be updated by reflecting feedback. For example, the learning unit adds learning data based on user feedback. The learning unit can also update the learning data by reflecting user feedback in real time. The learning unit can also improve the quality of the learning data by analyzing user feedback.

[0054] The learning unit can analyze changes in map information or historical background during learning and adjust the update frequency of the learning data. The learning unit can analyze changes in map information or historical background during learning and adjust the update frequency of the learning data. For example, the learning unit adjusts the update frequency of the learning data based on changes in map information. The learning unit can also adjust the update frequency of the learning data based on changes in the historical background. The learning unit can also comprehensively analyze changes in map information and historical background and optimize the update frequency of the learning data. In this way, the update frequency of the learning data can be optimized by analyzing changes in map information and historical background. For example, the learning unit adjusts the update frequency of the learning data based on changes in map information. The learning unit can also adjust the update frequency of the learning data based on changes in the historical background. The learning unit can also comprehensively analyze changes in map information and historical background and optimize the update frequency of the learning data.

[0055] The learning unit can, during learning, integrate information from multiple data sources to enrich the learning data. The learning unit, during learning, integrates information from different data sources to enrich the learning data. For example, the learning unit can integrate map data and historical data to enrich the learning data. The learning unit can also integrate social media data to enrich the learning data. The learning unit can also integrate user feedback data to enrich the learning data. In this way, the learning data can be enriched by integrating information from different data sources. For example, the learning unit can integrate map data and historical data to enrich the learning data. The learning unit can also integrate social media data to enrich the learning data. The learning unit can also integrate user feedback data to enrich the learning data.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The generation unit can also customize the generation of the metaverse space based on the user's past travel history. For example, a more detailed metaverse space can be generated based on data on places the user has visited in the past. It can also generate a metaverse space that reflects the tourist spots and activities the user has previously liked. Furthermore, the accuracy of the generation can be improved based on feedback provided by the user in the past. In this way, by referring to the past travel history, the accuracy of the generation can be improved, and a more attractive metaverse space can be provided to the user.

[0058] The providing unit can also provide a metaverse space taking into account the user's current weather information. For example, if the weather where the user is currently located is bad, a metaverse space with sunny weather can be provided to allow the user to have a comfortable experience. Also, if the user is in a cold location, a metaverse space with a warm climate can be provided. Furthermore, if the user is in a hot location, a metaverse space with a cool climate can be provided. In this way, by taking into account the current weather information, a more comfortable metaverse experience can be provided for the user.

[0059] The generator can also customize the theme of the metaverse space based on the user's interests. For example, if the user is interested in history, the generator can generate a metaverse space that focuses on historical events or people. If the user is interested in nature, the generator can generate a metaverse space that recreates beautiful landscapes or natural phenomena. Furthermore, if the user is interested in science and technology, the generator can generate a metaverse space themed around future technology and inventions. This allows the user to customize the theme according to their interests and concerns, providing a more engaging metaverse experience.

[0060] The reception unit can also analyze the user's past input history and suggest an appropriate input method. For example, it can automatically display locations and years that the user has frequently input in the past as candidates. It can also prioritize suggestions for input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest locations and years that will be used during a specific time period from the user's past input history. In this way, it is possible to suggest the optimal input method for the user by analyzing the past input history.

[0061] The providing unit can also provide a highly relevant metaverse space based on the user's geographical location information. For example, the providing unit can provide a metaverse space that recreates past historical events related to the user's current location. The providing unit can also provide a metaverse space that recreates future events that are easily accessible from the user's current location. Furthermore, the providing unit can provide a metaverse space that reflects the culture and scenery related to the user's current location. In this way, by taking geographical location information into consideration, a more relevant metaverse experience can be provided to the user.

[0062] The generation unit can also analyze the user's social media usage and generate a related metaverse space. For example, a metaverse space related to a place where the user checked in on social media can be generated. The generation unit can also analyze the content of the user's social media posts and generate a related metaverse space. Furthermore, a related metaverse space can be generated by referring to the activities of the user's friends on social media. In this way, a related metaverse space can be generated by analyzing social media activity.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The reception unit accepts input from the user of the place and era they want to go to. For example, the user can input "New York 100 years ago." Step 2: The generation unit uses a generation AI to generate a metaverse space based on the information received by the reception unit. The generation unit, for example, learns from past maps and historical data and recreates a space that meets the user's wishes. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to learn map information and historical background and generate a metaverse space. For example, the generation AI recreates the streets and buildings of New York 100 years ago based on past maps and historical data. Step 3: The providing unit provides the metaverse space generated by the generating unit to the user through a VR device. For example, the providing unit allows the user to experience the metaverse space using a VR device such as VR goggles or a head-mounted display.

[0065] (Example 2) In an embodiment of the present invention, a metaverse experience system allows a user to input a desired location and time period. The AI ​​then learns map information and historical background, generates a metaverse space, and allows the user to experience it through a VR device. The metaverse experience system allows users to input a desired location and time period. The AI ​​analyzes the information and generates a metaverse space based on the map information and historical background. The generated metaverse space is then provided to the user through a VR device. For example, if a user inputs "I want to go to New York 100 years ago," the AI ​​recreates the streets and buildings of New York 100 years ago, allowing the user to experience it through a VR device. Similarly, if a user inputs "I want to go to Paris 30 years from now," the AI ​​predicts Paris in the future and generates a metaverse space. This allows users to experience specific locations from the past or future, learning about historical background and future predictions. For example, experiencing New York 100 years ago in a history class can help students learn about the lifestyle and culture of that time. Experiencing Paris of the future can also help students predict future urban planning and technological advances. This service, which allows users to experience a metaverse space through a VR device by simply providing the desired location and time period, holds great potential in the fields of education and entertainment. This allows the metaverse experience system to experience specific locations in the past or future based on the user's preferences. For example, experiencing New York 100 years ago in a history class can help students learn about the lifestyle and culture of that time. Experiencing Paris in the future can also help students predict future urban planning and technological advances. This service, which allows users to experience a metaverse space through a VR device by simply providing the desired location and time period, holds great potential in the fields of education and entertainment.

[0066] A metaverse experience system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives input from a user of a desired location and era. For example, a user can input "New York 100 years ago." The generation unit uses a generation AI to generate a metaverse space based on the information received by the reception unit. The generation unit, for example, learns from past maps and historical data and recreates a space that meets the user's wishes. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to learn map information and historical background and generate the metaverse space. For example, the generation AI recreates the streets and buildings of New York 100 years ago based on past maps and historical data. The provision unit provides the metaverse space generated by the generation unit to a user through a VR device. For example, the provision unit allows the user to experience the metaverse space using a VR device such as VR goggles or a head-mounted display. As a result, the metaverse experience system according to an embodiment generates a metaverse space based on the user's wishes and allows the user to experience it through a VR device. For example, users can walk through the streets of New York 100 years ago, or experience Paris of the future, helping to predict future urban planning and technological advances.

[0067] The generation unit includes a learning unit that uses the generation AI to learn map information or historical background. The learning unit learns the map information and historical background using the generation AI. For example, the learning unit collects past maps and historical data and uses them to learn data for generating a metaverse space. The generation AI learns the map information and historical background using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI recreates the streets and buildings of New York 100 years ago based on past maps and historical data. The generation AI can also recreate a future Paris based on future predicted data. This allows the generation AI to learn the map information and historical background, thereby generating a more accurate metaverse space. For example, the generation AI recreates a space that meets the user's wishes based on past maps and historical data. The generation AI can also generate a metaverse space that reflects future urban planning and technological advances based on future predicted data.

[0068] The providing unit can provide the generated metaverse space to a user. The providing unit provides the metaverse space generated by the generating unit to the user. For example, the providing unit allows the user to experience the metaverse space using a VR device such as VR goggles or a head-mounted display. The providing unit has an interface for providing the generated metaverse space to the user. For example, the providing unit allows the user to experience the generated metaverse space by wearing a VR device. The providing unit also has software for providing the generated metaverse space to the user. For example, the providing unit transmits the generated metaverse space to the user's VR device so that the user can experience it. In this way, the generated metaverse space can be provided to the user, allowing the user to experience it. For example, the user can walk around the streets of New York 100 years ago. Furthermore, by experiencing Paris of the future, future urban planning and technological advances can be predicted.

[0069] The generation unit can generate a metaverse space based on past maps or historical data. The generation unit generates a metaverse space based on past maps or historical data. For example, the generation unit collects past maps and historical data and generates a metaverse space based on that data. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to learn from past maps and historical data and generate a metaverse space. For example, the generation AI recreates the streets and buildings of New York 100 years ago based on past maps and historical data. The generation AI can also recreate future Paris based on future predicted data. This enables a more realistic experience by generating a metaverse space based on past maps and historical data. For example, the generation AI recreates a space that meets the user's wishes based on past maps and historical data. The generation AI can also generate a metaverse space that reflects future urban planning and technological advances based on future predicted data.

[0070] The providing unit allows a user to experience the metaverse space through a VR device. The providing unit allows a user to experience the metaverse space through a VR device. For example, the providing unit allows a user to experience the metaverse space using a VR device such as VR goggles or a head-mounted display. The providing unit has an interface for providing the generated metaverse space to a user. For example, the providing unit allows a user to experience the generated metaverse space by wearing a VR device. The providing unit also has software for providing the generated metaverse space to a user. For example, the providing unit transmits the generated metaverse space to the user's VR device so that the user can experience it. This allows the user to experience the metaverse space through a VR device, thereby providing an immersive experience to the user. For example, a user can walk around the streets of New York 100 years ago. Furthermore, by experiencing Paris of the future, future urban planning and technological advances can be predicted.

[0071] The reception unit can estimate the user's emotions and adjust the input method for the desired destination and the desired era based on the estimated user emotions. The reception unit can estimate the user's emotions and adjust the input method for the desired destination and the desired era based on the estimated user emotions. For example, if the user is excited, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is relaxed, the reception unit can provide a simple interface and minimize the input steps. Furthermore, if the user is stressed, the reception unit can prioritize voice input to enable the user to quickly input the desired destination and the desired era. This allows for more appropriate input by adjusting the input method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, the reception unit can input the user's facial expressions and voice data into the generation AI and have the generation AI perform emotion estimation.

[0072] The reception unit can analyze the user's past input history and suggest an appropriate input method. The reception unit analyzes the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays locations and years that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest locations and years that will be used during a specific time period from the user's past input history. In this way, the reception unit can suggest the optimal input method for the user by analyzing the past input history. For example, the reception unit suggests the optimal input method based on data that the user has input in the past. The reception unit can also suggest a more appropriate input method by analyzing the user's past input history and understanding the user's preferences and tendencies.

[0073] The reception unit can present input candidates based on the user's current interests or concerns when the user inputs a desired destination and a desired era. The reception unit presents input candidates based on the user's current interests or concerns when the user inputs a desired destination and a desired era. For example, the reception unit suggests related places and eras based on historical events recently searched for by the user. The reception unit can also display places and eras in which the user has expressed interest on social media as candidates. The reception unit can also present new candidates related to places and eras that the user has visited in the past. This makes it possible to provide more appropriate candidates by presenting input candidates based on the user's interests or concerns. For example, the reception unit suggests related places and eras based on historical events recently searched for by the user. The reception unit can also display places and eras in which the user has expressed interest on social media as candidates. The reception unit can also present new candidates related to places and eras that the user has visited in the past.

[0074] The reception unit can select an appropriate input means according to the user's input method when inputting a desired destination and a desired era. The reception unit selects the optimal input means according to the user's input method (voice, text, gesture, etc.). For example, when the user uses voice input, the reception unit utilizes voice recognition technology to input the desired destination and the desired era. Furthermore, when the user uses text input, the reception unit can provide an auto-complete function to assist input. Furthermore, when the user uses gesture input, the reception unit can enable the user to set the desired destination and the desired era with an intuitive gesture. This improves input convenience by selecting the optimal input means according to the user's input method. For example, when the user uses voice input, the reception unit utilizes voice recognition technology to input the desired destination and the desired era. Furthermore, when the user uses text input, the reception unit can provide an auto-complete function to assist input. Furthermore, when the user uses gesture input, the reception unit can enable the user to set the desired destination and the desired era with an intuitive gesture.

[0075] The reception unit can estimate the user's emotions and prioritize the input content based on the estimated user emotions. The reception unit can estimate the user's emotions and prioritize the input content based on the estimated user emotions. For example, the reception unit can prioritize the input of the most important information when the user is in a hurry. The reception unit can also prioritize the input of detailed information when the user is relaxed. The reception unit can also prioritize the input of simple information when the user is stressed. This allows the input of important information to be prioritized by prioritizing the input content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the reception unit can input the user's facial expressions and voice data into the generation AI and cause the generation AI to estimate emotions.

[0076] The reception unit can prioritize presenting highly relevant candidates based on the user's geographical location information when the user inputs a desired destination and a desired era. The reception unit prioritizes presenting highly relevant candidates by taking the user's geographical location information into consideration when the user inputs a desired destination and a desired era. For example, the reception unit displays past historical events close to the user's current location as candidates. The reception unit can also display future events that are easily accessible from the user's current location as candidates. The reception unit can also present candidates based on historical background related to the user's current location. In this way, highly relevant candidates can be prioritized by taking the user's geographical location information into consideration. For example, the reception unit displays past historical events close to the user's current location as candidates. The reception unit can also display future events that are easily accessible from the user's current location as candidates. The reception unit can also present candidates based on historical background related to the user's current location.

[0077] The reception unit can analyze the user's social media usage status when the user inputs a desired destination and a desired age group, and present related candidates. The reception unit can analyze the user's social media activity when the user inputs a desired destination and a desired age group, and present related candidates. For example, the reception unit can display age groups related to places the user has checked in to on social media as candidates. The reception unit can also analyze the content of the user's social media posts and present related places and age groups as candidates. The reception unit can also present related places and age groups with reference to the activity of the user's friends on social media. In this way, related candidates can be presented by analyzing the user's social media activity. For example, the reception unit can display age groups related to places the user has checked in to on social media as candidates. The reception unit can also analyze the content of the user's social media posts and present related places and age groups as candidates. The reception unit can also present related places and age groups with reference to the activity of the user's friends on social media.

[0078] The reception unit can customize the input method by reflecting the user's past opinions when inputting a desired destination and a desired era. The reception unit customizes the input method by reflecting the user's past feedback when inputting a desired destination and a desired era. For example, the reception unit preferentially provides an input method that the user has previously preferred. The reception unit can also adjust the input interface based on the user's past feedback. The reception unit can also avoid input methods that the user has previously been dissatisfied with. In this way, the input method can be customized by reflecting the user's past feedback. For example, the reception unit preferentially provides an input method that the user has previously preferred. The reception unit can also adjust the input interface based on the user's past feedback. The reception unit can also avoid input methods that the user has previously been dissatisfied with.

[0079] The generation unit can estimate the user's emotions and adjust the generation method of the metaverse space based on the estimated user emotions. The generation unit can estimate the user's emotions and adjust the generation method of the metaverse space based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a metaverse space that progresses at a leisurely pace. If the user is in a hurry, the generation unit can generate a metaverse space that emphasizes the shortest route. If the user is excited, the generation unit can generate a metaverse space that adds visually stimulating effects. This allows for the generation method to be adjusted based on the user's emotions, thereby generating a more appropriate metaverse space. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, the generation unit can input the user's facial expressions and voice data into the generation AI and cause the generation AI to estimate emotions.

[0080] The generation unit may adjust the level of detail of the map information or historical background when generating the metaverse space. The generation unit adjusts the level of detail of the map information or historical background when generating the metaverse space. For example, if a user desires detailed information, the generation unit may generate a metaverse space that is reproduced in detail. Alternatively, if a user desires concise information, the generation unit may generate a metaverse space that reproduces only major landmarks. Alternatively, if a user is interested in a particular historical event, the generation unit may generate a metaverse space that includes detailed information related to the event. In this way, by adjusting the level of detail of the map information or historical background, a metaverse space that meets the user's needs can be generated. For example, if a user desires detailed information, the generation unit may generate a metaverse space that is reproduced in detail. Alternatively, if a user desires concise information, the generation unit may generate a metaverse space that includes detailed information related to the event.

[0081] When generating a metaverse space, the generation unit can improve the accuracy of the generation based on the user's past experience history. When generating a metaverse space, the generation unit improves the accuracy of the generation by referring to the user's past experience history. For example, the generation unit generates a more accurate metaverse space based on places and eras visited by the user in the past. The generation unit can also generate a metaverse space that reflects the user's preferred style and perspective based on the user's past experience history. The generation unit can also improve the accuracy of the generation based on feedback provided by the user in the past. In this way, the accuracy of the generation is improved by referring to the past experience history. For example, the generation unit generates a more accurate metaverse space based on places and eras visited by the user in the past. The generation unit can also generate a metaverse space that reflects the user's preferred style and perspective based on the user's past experience history. The generation unit can also improve the accuracy of the generation based on feedback provided by the user in the past.

[0082] The generation unit can apply different generation algorithms depending on the user's interests when generating the metaverse space. The generation unit can apply different generation algorithms depending on the user's interests when generating the metaverse space. For example, if the user is interested in historical buildings, the generation unit can apply an algorithm that recreates the buildings in detail. Furthermore, if the user is interested in future technology, the generation unit can apply an algorithm that generates a metaverse space that emphasizes future technology. Furthermore, if the user is interested in a particular culture, the generation unit can apply an algorithm that generates a metaverse space that reflects that culture. In this way, by applying a generation algorithm depending on the user's interests, a more appropriate metaverse space can be generated. For example, if the user is interested in historical buildings, the generation unit can apply an algorithm that recreates the buildings in detail. Furthermore, if the user is interested in future technology, the generation unit can apply an algorithm that generates a metaverse space that emphasizes future technology. Furthermore, if the user is interested in a particular culture, the generation unit can apply an algorithm that generates a metaverse space that reflects that culture.

[0083] The generation unit can estimate the user's emotions and determine the priority of the metaverse spaces to be generated based on the estimated user emotions. The generation unit can estimate the user's emotions and determine the priority of the metaverse spaces to be generated based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can prioritize generating the most important metaverse space. Also, if the user is relaxed, the generation unit can prioritize generating a detailed metaverse space. Also, if the user is excited, the generation unit can prioritize generating a visually stimulating metaverse space. In this way, by determining the priority based on the user's emotions, important metaverse spaces can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, the generation unit can input the user's facial expressions and voice data into the generation AI and cause the generation AI to estimate emotions.

[0084] When generating a metaverse space, the generation unit can prioritize generating a highly relevant space based on the user's geographical location information. When generating a metaverse space, the generation unit prioritizes generating a highly relevant space by taking into account the user's geographical location information. For example, the generation unit prioritizes generating a past metaverse space related to the user's current location. The generation unit can also prioritize generating a future metaverse space that is easily accessible from the user's current location. The generation unit can also prioritize generating a metaverse space based on historical background related to the user's current location. In this way, by taking into account the user's geographical location information, highly relevant spaces can be prioritized. For example, the generation unit prioritizes generating a past metaverse space related to the user's current location. The generation unit can also prioritize generating a future metaverse space that is easily accessible from the user's current location. The generation unit can also prioritize generating a metaverse space based on historical background related to the user's current location.

[0085] The generation unit may analyze the user's social media usage status and generate a related space when generating the metaverse space. The generation unit may analyze the user's social media activity and generate a related space when generating the metaverse space. For example, the generation unit may generate a metaverse space related to a place where the user has checked in on social media. The generation unit may also analyze the content of the user's social media posts and generate a related metaverse space. The generation unit may also generate a related metaverse space by referring to the activities of the user's friends on social media. In this way, a related space can be generated by analyzing social media activity. For example, the generation unit may generate a metaverse space related to a place where the user has checked in on social media. The generation unit may also analyze the content of the user's social media posts and generate a related metaverse space. The generation unit may also generate a related metaverse space by referring to the activities of the user's friends on social media.

[0086] The generation unit can customize the generation method by reflecting the user's past opinions when generating a metaverse space. The generation unit customizes the generation method by reflecting the user's past feedback when generating a metaverse space. For example, the generation unit preferentially applies a generation method that the user has previously preferred. The generation unit can also adjust the generation algorithm based on the user's past feedback. The generation unit can also avoid generation methods that the user has previously been dissatisfied with. In this way, the generation method can be customized by reflecting past feedback. For example, the generation unit preferentially applies a generation method that the user has previously preferred. The generation unit can also adjust the generation algorithm based on the user's past feedback. The generation unit can also avoid generation methods that the user has previously been dissatisfied with.

[0087] The providing unit can estimate the user's emotions and adjust the method of providing the metaverse space based on the estimated user emotions. The providing unit can estimate the user's emotions and adjust the method of providing the metaverse space based on the estimated user emotions. For example, if the user is relaxed, the providing unit can provide the metaverse space at a leisurely pace. If the user is in a hurry, the providing unit can also provide the metaverse space quickly. If the user is excited, the providing unit can also provide the metaverse space with visually stimulating effects. This allows for adjusting the method of providing the metaverse space based on the user's emotions to provide a more appropriate metaverse space. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the providing unit can input the user's facial expressions and voice data into the generation AI and cause the generation AI to estimate emotions.

[0088] The providing unit can select the optimal delivery method based on the user's past experience history when providing the metaverse space. The providing unit selects the optimal delivery method by referring to the user's past experience history when providing the metaverse space. For example, the providing unit preferentially applies a delivery method that the user has previously preferred. The providing unit can also adjust the delivery method based on the user's past experience history. The providing unit can also avoid delivery methods that the user has previously been dissatisfied with. In this way, the optimal delivery method can be selected by referring to the past experience history. For example, the providing unit preferentially applies a delivery method that the user has previously preferred. The providing unit can also adjust the delivery method based on the user's past experience history. The providing unit can also avoid delivery methods that the user has previously been dissatisfied with.

[0089] The providing unit can customize the content to be provided based on the user's current interests or concerns when providing the metaverse space. The providing unit customizes the content to be provided based on the user's current interests or concerns when providing the metaverse space. For example, the providing unit provides a metaverse space related to a historical event in which the user is currently interested. The providing unit can also provide a metaverse space related to a future technology in which the user is currently interested. The providing unit can also provide a metaverse space related to a culture or region in which the user is currently interested. In this way, by customizing the content to be provided based on the current interests or concerns, a more appropriate metaverse space can be provided. For example, the providing unit provides a metaverse space related to a historical event in which the user is currently interested. The providing unit can also provide a metaverse space related to a future technology in which the user is currently interested. The providing unit can also provide a metaverse space related to a culture or region in which the user is currently interested.

[0090] The providing unit can improve the provision method by reflecting the user's opinions when providing the metaverse space. The providing unit improves the provision method by reflecting the user's feedback when providing the metaverse space. For example, the providing unit adjusts the provision method based on feedback previously provided by the user. The providing unit can also reflect the user's feedback in real time and improve the provision method. The providing unit can also avoid provision methods that the user has been dissatisfied with in the past. In this way, the provision method can be improved by reflecting the feedback. For example, the providing unit adjusts the provision method based on feedback previously provided by the user. The providing unit can also reflect the user's feedback in real time and improve the provision method. The providing unit can also avoid provision methods that the user has been dissatisfied with in the past.

[0091] The providing unit can estimate the user's emotions and determine the priority of metaverse spaces to be provided based on the estimated user emotions. The providing unit can estimate the user's emotions and determine the priority of metaverse spaces to be provided based on the estimated user emotions. For example, if the user is in a hurry, the providing unit can prioritize providing the most important metaverse space. Also, if the user is relaxed, the providing unit can prioritize providing a detailed metaverse space. Also, if the user is excited, the providing unit can prioritize providing a visually stimulating metaverse space. In this way, by determining the priority based on the user's emotions, important metaverse spaces can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, the providing unit can input the user's facial expression and voice data into the generation AI and cause the generation AI to estimate emotions.

[0092] The providing unit can select an optimal providing method based on the user's geographical location information when providing the metaverse space. The providing unit selects the optimal providing method by taking the user's geographical location information into consideration when providing the metaverse space. For example, the providing unit can prioritize providing a past metaverse space related to the user's current location. The providing unit can also prioritize providing a future metaverse space that is easily accessible from the user's current location. The providing unit can also provide the metaverse space based on historical background related to the user's current location. In this way, the optimal providing method can be selected by taking the geographical location information into consideration. For example, the providing unit can prioritize providing a past metaverse space related to the user's current location. The providing unit can also prioritize providing a future metaverse space that is easily accessible from the user's current location. The providing unit can also provide the metaverse space based on historical background related to the user's current location.

[0093] When providing a metaverse space, the providing unit can analyze the user's social media usage status and suggest content to be provided. When providing a metaverse space, the providing unit analyzes the user's social media activity and suggest content to be provided. For example, the providing unit provides a metaverse space related to a place where the user has checked in on social media. The providing unit can also analyze the content posted by the user on social media and provide a related metaverse space. The providing unit can also provide a related metaverse space by referring to the activity of the user's friends on social media. In this way, content to be provided can be suggested by analyzing social media activity. For example, the providing unit provides a metaverse space related to a place where the user has checked in on social media. The providing unit can also analyze the content posted by the user on social media and provide a related metaverse space. The providing unit can also provide a related metaverse space by referring to the activity of the user's friends on social media.

[0094] The providing unit can customize the delivery method by reflecting the user's past opinions when providing the metaverse space. The providing unit customizes the delivery method by reflecting the user's past feedback when providing the metaverse space. For example, the providing unit preferentially applies a delivery method that the user has previously preferred. The providing unit can also adjust the delivery method based on the user's past feedback. The providing unit can also avoid delivery methods that the user has previously been dissatisfied with. In this way, the delivery method can be customized by reflecting past feedback. For example, the providing unit preferentially applies a delivery method that the user has previously preferred. The providing unit can also adjust the delivery method based on the user's past feedback. The providing unit can also avoid delivery methods that the user has previously been dissatisfied with.

[0095] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is relaxed, the learning unit can select detailed training data. If the user is in a hurry, the learning unit can also select concise training data. If the user is excited, the learning unit can also select visually stimulating training data. This allows for more appropriate data to be learned by selecting training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the learning unit can input the user's facial expressions and voice data into the generation AI and have the generation AI perform emotion estimation.

[0096] The learning unit can optimize the learning algorithm based on past learning data during learning. The learning unit optimizes the learning algorithm by referring to past learning data during learning. For example, the learning unit selects an optimal learning algorithm based on past learning data. The learning unit can also apply an algorithm that improves learning accuracy from the past learning data. The learning unit can also analyze past learning data and adjust the learning algorithm. In this way, the learning algorithm can be optimized by referring to past learning data. For example, the learning unit selects an optimal learning algorithm based on past learning data. The learning unit can also apply an algorithm that improves learning accuracy from the past learning data. The learning unit can also analyze past learning data and adjust the learning algorithm.

[0097] The learning unit can update the learning data by reflecting user opinions during learning. The learning unit can update the learning data by reflecting user feedback during learning. For example, the learning unit adds learning data based on user feedback. The learning unit can also update the learning data by reflecting user feedback in real time. The learning unit can also analyze user feedback to improve the quality of the learning data. In this way, the learning data can be updated by reflecting feedback. For example, the learning unit adds learning data based on user feedback. The learning unit can also update the learning data by reflecting user feedback in real time. The learning unit can also improve the quality of the learning data by analyzing user feedback.

[0098] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. For example, the learning unit can perform learning more frequently when the user is relaxed. The learning unit can also reduce the frequency of learning when the user is in a hurry. The learning unit can also adjust the frequency of learning when the user is excited. This enables more effective learning by adjusting the frequency of learning based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the learning unit can input the user's facial expressions and voice data into the generation AI and cause the generation AI to estimate emotions.

[0099] The learning unit can analyze changes in map information or historical background during learning and adjust the update frequency of the learning data. The learning unit can analyze changes in map information or historical background during learning and adjust the update frequency of the learning data. For example, the learning unit adjusts the update frequency of the learning data based on changes in map information. The learning unit can also adjust the update frequency of the learning data based on changes in the historical background. The learning unit can also comprehensively analyze changes in map information and historical background and optimize the update frequency of the learning data. In this way, the update frequency of the learning data can be optimized by analyzing changes in map information and historical background. For example, the learning unit adjusts the update frequency of the learning data based on changes in map information. The learning unit can also adjust the update frequency of the learning data based on changes in the historical background. The learning unit can also comprehensively analyze changes in map information and historical background and optimize the update frequency of the learning data.

[0100] The learning unit can, during learning, integrate information from multiple data sources to enrich the learning data. The learning unit, during learning, integrates information from different data sources to enrich the learning data. For example, the learning unit can integrate map data and historical data to enrich the learning data. The learning unit can also integrate social media data to enrich the learning data. The learning unit can also integrate user feedback data to enrich the learning data. In this way, the learning data can be enriched by integrating information from different data sources. For example, the learning unit can integrate map data and historical data to enrich the learning data. The learning unit can also integrate social media data to enrich the learning data. The learning unit can also integrate user feedback data to enrich the learning data. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and learning unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives input from the user of the desired location and era. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates the metaverse space using a generation AI. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns map information and historical background. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the generated metaverse space to the user through a VR device. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, provision unit, and learning unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives input from the user of the desired location and era. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates the metaverse space using a generation AI. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns map information and historical background. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated metaverse space to the user through a VR device. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and learning unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives input from the user of the desired location and era. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates the metaverse space using a generation AI. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns map information and historical background. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the generated metaverse space to the user through a VR device. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and learning unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives input from the user of the place and era they want to go to. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates the metaverse space using a generation AI. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns map information and historical background. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated metaverse space to the user through a VR device.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The reception unit can also monitor the user's current health condition and adjust the input method. For example, if the user is tired, it can prioritize voice input and allow the user to enter the desired destination and age with simple operations. If the user is in good health, it can provide detailed input options and suggest a customizable input method. Furthermore, if the user is feeling stressed, it can provide a relaxing interface and minimize input steps. This allows the input method to be adjusted according to the user's health condition, enabling more appropriate input.

[0103] The generation unit can also customize the generation of the metaverse space based on the user's past travel history. For example, a more detailed metaverse space can be generated based on data on places the user has visited in the past. It can also generate a metaverse space that reflects the tourist spots and activities the user has previously liked. Furthermore, the accuracy of the generation can be improved based on feedback provided by the user in the past. In this way, by referring to the past travel history, the accuracy of the generation can be improved, and a more attractive metaverse space can be provided to the user.

[0104] The providing unit can also provide a metaverse space taking into account the user's current weather information. For example, if the weather where the user is currently located is bad, a metaverse space with sunny weather can be provided to allow the user to have a comfortable experience. Also, if the user is in a cold location, a metaverse space with a warm climate can be provided. Furthermore, if the user is in a hot location, a metaverse space with a cool climate can be provided. In this way, by taking into account the current weather information, a more comfortable metaverse experience can be provided for the user.

[0105] The generator can also customize the theme of the metaverse space based on the user's interests. For example, if the user is interested in history, the generator can generate a metaverse space that focuses on historical events or people. If the user is interested in nature, the generator can generate a metaverse space that recreates beautiful landscapes or natural phenomena. Furthermore, if the user is interested in science and technology, the generator can generate a metaverse space themed around future technology and inventions. This allows the user to customize the theme according to their interests and concerns, providing a more engaging metaverse experience.

[0106] The providing unit can also estimate the user's emotions and adjust the method of providing the metaverse space based on the estimated user's emotions. For example, if the user is relaxed, the metaverse space can be provided at a leisurely pace. If the user is in a hurry, the metaverse space can be provided quickly. Furthermore, if the user is excited, the metaverse space can be provided with visually stimulating effects. In this way, by adjusting the method of provision based on the user's emotions, a more appropriate metaverse space can be provided.

[0107] The reception unit can also analyze the user's past input history and suggest an appropriate input method. For example, it can automatically display locations and years that the user has frequently input in the past as candidates. It can also prioritize suggestions for input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest locations and years that will be used during a specific time period from the user's past input history. In this way, it is possible to suggest the optimal input method for the user by analyzing the past input history.

[0108] The generation unit can also estimate the user's emotions and adjust the generation method of the metaverse space based on the estimated user's emotions. For example, if the user is relaxed, a metaverse space that progresses at a leisurely pace can be generated. If the user is in a hurry, a metaverse space that emphasizes the shortest route can be generated. Furthermore, if the user is excited, a metaverse space that adds visually stimulating effects can be generated. In this way, by adjusting the generation method based on the user's emotions, a more appropriate metaverse space can be generated.

[0109] The providing unit can also provide a highly relevant metaverse space based on the user's geographical location information. For example, the providing unit can provide a metaverse space that recreates past historical events related to the user's current location. The providing unit can also provide a metaverse space that recreates future events that are easily accessible from the user's current location. Furthermore, the providing unit can provide a metaverse space that reflects the culture and scenery related to the user's current location. In this way, by taking geographical location information into consideration, a more relevant metaverse experience can be provided to the user.

[0110] The generation unit can also analyze the user's social media usage and generate a related metaverse space. For example, a metaverse space related to a place where the user checked in on social media can be generated. The generation unit can also analyze the content of the user's social media posts and generate a related metaverse space. Furthermore, a related metaverse space can be generated by referring to the activities of the user's friends on social media. In this way, a related metaverse space can be generated by analyzing social media activity.

[0111] The providing unit can also estimate the user's emotions and determine the priority of metaverse spaces to be provided based on the estimated user's emotions. For example, if the user is in a hurry, the most important metaverse space can be provided preferentially. Also, if the user is relaxed, a detailed metaverse space can be provided preferentially. Furthermore, if the user is excited, a visually stimulating metaverse space can be provided preferentially. In this way, by determining the priority based on the user's emotions, important metaverse spaces can be provided preferentially.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The reception unit accepts input from the user of the place and era they want to go to. For example, the user can input "New York 100 years ago." Step 2: The generation unit uses a generation AI to generate a metaverse space based on the information received by the reception unit. The generation unit, for example, learns from past maps and historical data and recreates a space that meets the user's wishes. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to learn map information and historical background and generate a metaverse space. For example, the generation AI recreates the streets and buildings of New York 100 years ago based on past maps and historical data. Step 3: The providing unit provides the metaverse space generated by the generating unit to the user through a VR device. For example, the providing unit allows the user to experience the metaverse space using a VR device such as VR goggles or a head-mounted display.

[0114] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 7, a 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.

[0152] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0154] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0156] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0157] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0158] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0163] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0164] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0169] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0170] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0171] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0175] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0176] 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.

[0177] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0178] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0179] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0180] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0182] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0183] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0184] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of a desired place and age from a user; a generation unit that generates a metaverse space based on the information received by the reception unit; a providing unit that provides the metaverse space generated by the generating unit through a VR device; Equipped with A system characterized by:

2. Equipped with a learning unit that learns map information or historical background using generation AI 2. The system of claim 1.

3. Providing the generated metaverse space to users The system of claim 1 .

4. The generation unit Generate a metaverse space based on past maps or historical data 2. The system of claim 1.

5. The providing unit Allowing users to experience the Metaverse space through a VR device The system of claim 1 .

6. The reception unit To estimate the user's emotions and adjust the input method of the desired destination and age based on the estimated user emotions. The system of claim 1 .

7. The reception unit Analyzes the user's past input history and suggests appropriate input methods 2. The system of claim 1.

8. The reception unit When entering a desired destination and age, suggestions are provided based on the user's current interests.

2. The system of claim 1.

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