System

Abstract

To efficiently generate a metaverse space on the basis of an image.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The receiver receives an image. The analysis unit analyzes the image input by the reception unit. The generation unit generates a Metaverse space based on the image analyzed by the analysis unit. The providing unit provides the Metaverse space generated by the generating unit.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 technology has had the problem that the process of generating a metaverse space based on images is inefficient.

[0005] The system according to the embodiment aims to efficiently generate a metaverse space based on an image. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs an image. The analysis unit analyzes the image input by the reception unit. The generation unit generates a metaverse space based on the image analyzed by the analysis unit. The provision unit provides the metaverse space generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate a metaverse space based on an image. [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) A system according to an embodiment of the present invention uses a generation AI to generate a metaverse space from an image. In this system, a user inputs an image, and the generation AI analyzes the image, generates the structure of the metaverse space, and provides it as a virtual space in which the user can freely move. For example, when a user inputs an arbitrary image, such as a landscape photo or a building photo, the generation AI analyzes the image and generates the structure of the metaverse space. The generated metaverse space is provided as a virtual space in which the user can freely move. This allows the user to experience a virtual space based on a real-world image. For example, the system can generate a virtual space based on a landscape photo of a travel destination, allowing users to walk around in the space and experience the sensation of actually visiting the place. Furthermore, the system can generate a virtual space based on a building's blueprint, allowing users to explore the space and check the building's design. This technology can be used in a variety of fields, including tourism, architecture, and education. For example, in the tourism industry, virtual tours of travel destinations can be provided. In the architecture industry, building designs can be checked in virtual spaces. In the education field, historical buildings and locations can be recreated in virtual spaces and experienced by students.

[0029] A metaverse generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives an image input from a user. For example, any image, such as a landscape photo or a building photo, can be input. The reception unit inputs the image to a generation AI. The analysis unit analyzes the input image using the generation AI. For example, the generation AI extracts image features and generates a metaverse space structure based on the extracted features. The generation AI can extract features using techniques such as image edge detection, color histograms, and shape analysis. The generation unit generates a metaverse space structure based on the analyzed image using the generation AI. For example, the generation AI reproduces the topography, buildings, and object placement based on the extracted features. The provision unit provides the generated metaverse space to a user. For example, the user can move freely within the virtual space. The provision unit can provide the virtual space via a web browser, a VR device, a mobile app, or the like. This allows the metaverse generation system according to an embodiment to generate a metaverse space from an image and provide it to a user.

[0030] The analysis unit can extract image features. For example, the analysis unit can extract the contours of the image using edge detection technology. The analysis unit can also analyze the color distribution of the image using a color histogram. The analysis unit can also identify the shape of an object in the image using shape analysis technology. By extracting image features, the accuracy of generating the metaverse space is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input an image into a generation AI, which then extracts the image features.

[0031] The generation unit can generate the structure of the metaverse space based on the extracted features. The generation unit can, for example, generate a terrain based on the extracted features. The generation unit can also reproduce the structure of a building. The generation unit can also arrange objects. In this way, by generating the structure of the metaverse space based on the extracted features, a virtual space that is close to reality can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the extracted features into a generation AI, which then generates the structure of the metaverse space.

[0032] The providing unit can provide a virtual space in which the user can move freely. For example, the providing unit can allow the user to walk within the virtual space. The providing unit can also allow the user to fly within the virtual space. The providing unit can also allow the user to teleport within the virtual space. This improves the user experience by providing a virtual space in which the user can move freely. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's movement data into AI, which can then calculate the movement route.

[0033] The provision unit can provide usage scenarios in the tourism, construction, and education industries. For example, in the tourism industry, the provision unit can provide a virtual tour of a travel destination. In the construction industry, the provision unit can check building designs in a virtual space. In the education field, the provision unit can recreate historical buildings and places in a virtual space and allow students to experience them. This allows the provision of usage scenarios in the tourism, construction, and education fields, enabling use in a wide range of fields. Some or all of the above-mentioned processing in the provision unit may be performed using AI, or may be performed without using AI. For example, the provision unit can input virtual tour data from the tourism industry into AI, which can calculate the optimal tour route.

[0034] The reception unit can analyze the user's past image input history and select the optimal input method. For example, the reception unit preferentially suggests an image input method (e.g., drag and drop) that the user has frequently used in the past. The reception unit can also analyze the types of images (landscapes, buildings, etc.) that the user has used in the past and suggest the optimal input method. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into AI, which can select the optimal input method.

[0035] When inputting images, the reception unit can filter them based on the user's current project or area of ​​interest. For example, if the user is working on a construction project, the reception unit can prioritize inputting images of buildings. Furthermore, if the user is interested in tourism, the reception unit can prioritize inputting images of landscapes and tourist spots. Furthermore, if the user is interested in education, the reception unit can prioritize inputting images of historical buildings and places. This enables efficient image input by filtering images based on the user's area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's project data into AI, which then filters out the most suitable images.

[0036] When inputting an image, the reception unit can select the optimal input means according to the user's input method. For example, when the user uses voice input, the reception unit inputs a description of the image using voice recognition technology. Furthermore, when the user uses text input, the reception unit can search for and input related images from the text. Furthermore, when the user directly uploads images, the reception unit can provide a drag-and-drop function to simplify input. This improves input convenience by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's input method data into AI, which can select the optimal input means.

[0037] When inputting images, the reception unit can prioritize inputting highly relevant images taking into account the user's geographical location information. For example, if the user is in a specific city, the reception unit can prioritize inputting images of the city's landmarks and tourist attractions. Furthermore, if the user is in a specific country, the reception unit can prioritize inputting images related to the country's culture and scenery. Furthermore, if the user is in a specific region, the reception unit can prioritize inputting images of the region's historical buildings and natural landscapes. In this way, highly relevant images are prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to AI, which can select highly relevant images.

[0038] When inputting an image, the reception unit can analyze the user's social media activity and input related images. For example, the reception unit can automatically input images shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and suggest related images. The reception unit can also input related images by referring to the activity of the user's friends on social media. In this way, related images are efficiently input by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into AI, which can select related images.

[0039] The reception unit can customize the input method by reflecting the user's past feedback when inputting an image. For example, the reception unit preferentially suggests image input methods that the user has previously preferred. The reception unit can also customize the input interface based on the user's past feedback. The reception unit can also make adjustments to avoid input methods that the user has previously dissatisfied with. In this way, the input method is customized by reflecting the user's past feedback, improving convenience. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into AI, which can select the optimal input method.

[0040] During image analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the image. For example, the analysis unit performs a detailed analysis for an image of an important building or landmark. The analysis unit can also perform a simplified analysis for a general landscape image. The analysis unit can also adjust the level of detail of the analysis based on the importance specified by the user. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the image. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input image importance data to the generation AI, which can then adjust the level of detail of the analysis.

[0041] When analyzing an image, the analysis unit can apply different analysis algorithms depending on the category of the image. For example, in the case of an image of a building, the analysis unit can apply an architectural analysis algorithm. In addition, in the case of an image of a natural landscape, the analysis unit can also apply a natural landscape analysis algorithm. In addition, in the case of an image of a person, the analysis unit can also apply a facial recognition algorithm. In this way, by applying different analysis algorithms depending on the image category, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using or without using the generation AI. For example, the analysis unit can input image category data into the generation AI, and the generation AI can select the optimal analysis algorithm.

[0042] When analyzing an image, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the results of images previously analyzed by the user. The analysis unit can also learn specific patterns from the user's past analysis results and improve the analysis accuracy. The analysis unit can also improve the analysis algorithm based on the user's past feedback. In this way, the analysis accuracy is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI, which can then adjust the analysis algorithm.

[0043] When analyzing images, the analysis unit can determine the priority of analysis based on the time the image was taken. For example, the analysis unit can prioritize analyzing the most recent image and provide the most recent information. In addition, the analysis unit can also analyze older images by comparing them with past data. The analysis unit can also adjust the priority of analysis based on the time the image was taken specified by the user. In this way, by determining the priority of analysis based on the time the image was taken, the most recent information is analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input image taking time data into the generation AI, and the generation AI can determine the priority of analysis.

[0044] During image analysis, the analysis unit can adjust the order of analysis based on the relevance of the images. For example, the analysis unit prioritizes analysis of highly relevant images specified by the user. The analysis unit can also perform analysis in order of relevance based on the content of the images. The analysis unit can also prioritize analysis of highly relevant images by referring to the user's past analysis results. This enables efficient analysis by adjusting the order of analysis based on the relevance of the images. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input image relevance data into the generation AI, which can then adjust the order of analysis.

[0045] During image analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user is an expert, the analysis unit can provide analysis results using detailed technical terminology. Furthermore, if the user is a general user, the analysis unit can also provide analysis results using concise and easy-to-understand terminology. The analysis unit can also adjust the use of technical terminology based on the user's past feedback. This allows for a deeper understanding of the analysis results by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI, which can then adjust the use of technical terminology.

[0046] When generating a metaverse space, the generation unit can adjust the level of detail of the generation based on the importance of the image. For example, in the case of an image of an important building or landmark, the generation unit generates a detailed metaverse space. In addition, in the case of a general landscape image, the generation unit can also generate a simplified metaverse space. In addition, the generation unit can adjust the level of detail of the generation based on the importance specified by the user. This enables efficient generation of a metaverse space by adjusting the level of detail of the generation based on the importance of the image. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input image importance data to the generation AI, which can then adjust the level of detail of the generation.

[0047] When generating the metaverse space, the generation unit can apply different generation algorithms depending on the image category. For example, in the case of an image of a building, the generation unit can apply an architectural generation algorithm. In addition, in the case of an image of a natural landscape, the generation unit can also apply a natural landscape generation algorithm. In addition, in the case of an image of a person, the generation unit can also apply a character generation algorithm. In this way, by applying different generation algorithms depending on the image category, the generation accuracy is improved. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input image category data into the generation AI, which can select the optimal generation algorithm.

[0048] When generating a metaverse space, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, adjusts the generation algorithm based on the results of metaverse spaces generated by the user in the past. The generation unit can also learn specific patterns from the user's past generation results and improve the generation accuracy. The generation unit can also improve the generation algorithm based on the user's past feedback. In this way, the generation accuracy is improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past generation result data into the generation AI, which can then adjust the generation algorithm.

[0049] When generating a metaverse space, the generation unit can determine a generation priority based on the location where the image was taken. For example, the generation unit can prioritize generating images taken by the user in a specific city. The generation unit can also prioritize generating images taken by the user in a specific country. The generation unit can also prioritize generating images taken by the user in a specific region. In this way, by determining the generation priority based on the location where the image was taken, a highly relevant virtual space is preferentially generated. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input image shooting location data into the generation AI, and the generation AI can determine the generation priority.

[0050] When generating the metaverse space, the generation unit can improve the accuracy of generation by referring to literature related to the image. For example, the generation unit can adjust the generation algorithm by referring to academic papers related to the image. The generation unit can also improve the generation algorithm by referring to books related to the image. The generation unit can also optimize the generation algorithm by referring to websites related to the image. This improves the generation accuracy by referring to literature related to the image. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input literature data related to the image into the generation AI, which can then adjust the generation algorithm.

[0051] The generation unit can generate the metaverse space taking into consideration the market value of the image. For example, the generation unit can prioritize generating images of expensive real estate. The generation unit can also prioritize generating images of popular tourist destinations. The generation unit can also prioritize generating images of products with high market value. In this way, a high-value virtual space is generated by taking the market value of the image into consideration. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input market value data of the image into the generation AI, and the generation AI can generate the image based on the market value.

[0052] When providing the metaverse space, the providing unit can select the optimal providing method by referring to the user's past usage history. For example, the providing unit can preferentially suggest a metaverse space providing method that the user has previously preferred. The providing unit can also suggest the optimal providing method for a specific time period based on the user's past usage history. The providing unit can also customize the providing method based on the user's past feedback. In this way, the optimal providing method is selected by referring to the user's past usage history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's past usage history data into AI, which can select the optimal providing method.

[0053] When providing a metaverse space, the providing unit can customize the content provided according to the user's current task. For example, if the user wants to relax, the providing unit can provide a calm metaverse space. If the user is searching for information, the providing unit can also provide a metaverse space containing relevant information. If the user is seeking entertainment, the providing unit can also provide a visually stimulating metaverse space. In this way, by customizing the content provided according to the user's current task, a virtual space that meets the user's needs is provided. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's task data into AI, which can then customize the optimal content to be provided.

[0054] The providing unit can improve the provision method by reflecting user feedback when providing the metaverse space. For example, the providing unit adjusts the provision method based on feedback provided by the user regarding the provision method. The providing unit can also analyze the user's past feedback and optimize the provision method. The providing unit can also reflect user feedback in real time and improve the provision method. In this way, by reflecting user feedback, the provision method is improved and the user experience is enhanced. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input user feedback data into AI, which can improve the provision method.

[0055] When providing a metaverse space, the providing unit can select the optimal providing method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a metaverse space that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a metaverse space optimized for a large screen. Furthermore, if the user is using a VR device, the providing unit can also provide a metaverse space that is highly immersive. In this way, the optimal providing method is selected by taking into account the user's device information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's device information into AI, which can then select the optimal providing method.

[0056] When providing the metaverse space, the providing unit can make the provided content multilingual according to the user's language setting. The providing unit, for example, automatically sets the language of the metaverse space based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the metaverse space in a specific language when the user selects that language. This improves user convenience by making the provided content multilingual according to the user's language setting. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data into AI, which can select the optimal language setting.

[0057] When providing a metaverse space, the providing unit can customize it based on the user's occupation and lifestyle. For example, if the user is an architect, the providing unit can provide an architecture-related metaverse space. Furthermore, if the user is a student, the providing unit can provide an education-related metaverse space. Furthermore, if the user likes to travel, the providing unit can provide a tourism-related metaverse space. In this way, by customizing based on the user's occupation and lifestyle, a virtual space that meets the user's needs is provided. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's occupation and lifestyle data into AI, which can then customize the content to be provided optimally.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The reception unit can suggest the optimal input method based on the user's past input history. For example, it can preferentially suggest input methods (drag and drop, voice input, etc.) that the user has frequently used in the past. The reception unit can also analyze the types of images (landscapes, buildings, etc.) that the user has input in the past and suggest the optimal input method. Furthermore, the reception unit can predict and suggest the input method that will be used in a specific time period based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history.

[0060] The generation unit can adjust the generation algorithm based on the user's past generation results. For example, the generation algorithm can be adjusted based on the results of metaverse spaces generated by the user in the past. It can also learn specific patterns from the user's past generation results to improve generation accuracy. Furthermore, it can improve the generation algorithm based on the user's past feedback. This improves generation accuracy by referring to the user's past generation results.

[0061] The reception unit can prioritize input of highly relevant images taking into account the user's geographical location information. For example, if the user is in a specific city, images of the city's landmarks and tourist attractions can be prioritized. Also, if the user is in a specific country, images related to the culture and scenery of that country can be prioritized. Furthermore, if the user is in a specific region, images of historical buildings and natural scenery of that region can be prioritized. In this way, highly relevant images are prioritized by taking into account the user's geographical location information.

[0062] When analyzing an image, the analysis unit can apply different analysis algorithms depending on the image category. For example, for an image of a building, an architectural analysis algorithm can be applied. For an image of a natural landscape, a natural landscape analysis algorithm can be applied. Furthermore, for an image of a person, a face recognition algorithm can be applied. In this way, by applying different analysis algorithms depending on the image category, the analysis accuracy can be improved.

[0063] The provision unit can select the optimal provision method by referring to the user's past usage history. For example, it can preferentially suggest a metaverse space provision method that the user has previously preferred. It can also suggest the optimal provision method for a specific time period based on the user's past usage history. Furthermore, it can customize the provision method based on the user's past feedback. In this way, the optimal provision method is selected by referring to the user's past usage history.

[0064] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user is an expert, the analysis results can be provided using detailed technical terms. On the other hand, if the user is a general user, the analysis results can be provided using concise and easy-to-understand terms. Furthermore, the use of technical terms can be adjusted based on the user's past feedback. In this way, adjusting the use of technical terms in the analysis according to the user's level of expertise deepens the understanding of the analysis results.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The user inputs an image into the reception unit. For example, any image can be input, such as a landscape photo or a photo of a building. The reception unit inputs the image into the generation AI. Step 2: The analysis unit uses the generation AI to analyze the input image. For example, the generation AI extracts image features and generates a structure for the Metaverse space based on them. The generation AI can extract features using techniques such as edge detection, color histograms, and shape analysis of the image. Step 3: The generator uses AI to generate the structure of the metaverse space based on the analyzed image. For example, the AI ​​may recreate the terrain, buildings, and object placement based on the extracted features. Step 4: The provider provides the generated metaverse space to the user. For example, the user can move freely within the virtual space. The provider can provide the virtual space through a web browser, a VR device, a mobile app, etc.

[0067] (Example 2) A system according to an embodiment of the present invention uses a generation AI to generate a metaverse space from an image. In this system, a user inputs an image, and the generation AI analyzes the image, generates the structure of the metaverse space, and provides it as a virtual space in which the user can freely move. For example, when a user inputs an arbitrary image, such as a landscape photo or a building photo, the generation AI analyzes the image and generates the structure of the metaverse space. The generated metaverse space is provided as a virtual space in which the user can freely move. This allows the user to experience a virtual space based on a real-world image. For example, the system can generate a virtual space based on a landscape photo of a travel destination, allowing users to walk around in the space and experience the sensation of actually visiting the place. Furthermore, the system can generate a virtual space based on a building's blueprint, allowing users to explore the space and check the building's design. This technology can be used in a variety of fields, including tourism, architecture, and education. For example, in the tourism industry, virtual tours of travel destinations can be provided. In the architecture industry, building designs can be checked in virtual spaces. In the education field, historical buildings and locations can be recreated in virtual spaces and experienced by students.

[0068] A metaverse generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives an image input from a user. For example, any image, such as a landscape photo or a building photo, can be input. The reception unit inputs the image to a generation AI. The analysis unit analyzes the input image using the generation AI. For example, the generation AI extracts image features and generates a metaverse space structure based on the extracted features. The generation AI can extract features using techniques such as image edge detection, color histograms, and shape analysis. The generation unit generates a metaverse space structure based on the analyzed image using the generation AI. For example, the generation AI reproduces the topography, buildings, and object placement based on the extracted features. The provision unit provides the generated metaverse space to a user. For example, the user can move freely within the virtual space. The provision unit can provide the virtual space via a web browser, a VR device, a mobile app, or the like. This allows the metaverse generation system according to an embodiment to generate a metaverse space from an image and provide it to a user.

[0069] The analysis unit can extract image features. For example, the analysis unit can extract the contours of the image using edge detection technology. The analysis unit can also analyze the color distribution of the image using a color histogram. The analysis unit can also identify the shape of an object in the image using shape analysis technology. By extracting image features, the accuracy of generating the metaverse space is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input an image into a generation AI, which then extracts the image features.

[0070] The generation unit can generate the structure of the metaverse space based on the extracted features. The generation unit can, for example, generate a terrain based on the extracted features. The generation unit can also reproduce the structure of a building. The generation unit can also arrange objects. In this way, by generating the structure of the metaverse space based on the extracted features, a virtual space that is close to reality can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the extracted features into a generation AI, which then generates the structure of the metaverse space.

[0071] The providing unit can provide a virtual space in which the user can move freely. For example, the providing unit can allow the user to walk within the virtual space. The providing unit can also allow the user to fly within the virtual space. The providing unit can also allow the user to teleport within the virtual space. This improves the user experience by providing a virtual space in which the user can move freely. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's movement data into AI, which can then calculate the movement route.

[0072] The provision unit can provide usage scenarios in the tourism, construction, and education industries. For example, in the tourism industry, the provision unit can provide a virtual tour of a travel destination. In the construction industry, the provision unit can check building designs in a virtual space. In the education field, the provision unit can recreate historical buildings and places in a virtual space and allow students to experience them. This allows the provision of usage scenarios in the tourism, construction, and education fields, enabling use in a wide range of fields. Some or all of the above-mentioned processing in the provision unit may be performed using AI, or may be performed without using AI. For example, the provision unit can input virtual tour data from the tourism industry into AI, which can calculate the optimal tour route.

[0073] The reception unit can estimate the user's emotions and adjust the timing of image input based on the estimated user emotions. For example, if the user is relaxed, the reception unit can adjust the timing of image input slowly to allow the user to input calmly. Furthermore, if the user is in a hurry, the reception unit can also speed up the timing of image input to allow the user to input quickly. Furthermore, if the user is excited, the reception unit can also moderately adjust the timing of image input to allow the user to input with concentration. This improves user convenience by adjusting the timing of image input 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's facial expression data into a generation AI, which can then estimate the emotion.

[0074] The reception unit can analyze the user's past image input history and select the optimal input method. For example, the reception unit preferentially suggests an image input method (e.g., drag and drop) that the user has frequently used in the past. The reception unit can also analyze the types of images (landscapes, buildings, etc.) that the user has used in the past and suggest the optimal input method. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into AI, which can select the optimal input method.

[0075] When inputting images, the reception unit can filter them based on the user's current project or area of ​​interest. For example, if the user is working on a construction project, the reception unit can prioritize inputting images of buildings. Furthermore, if the user is interested in tourism, the reception unit can prioritize inputting images of landscapes and tourist spots. Furthermore, if the user is interested in education, the reception unit can prioritize inputting images of historical buildings and places. This enables efficient image input by filtering images based on the user's area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's project data into AI, which then filters out the most suitable images.

[0076] When inputting an image, the reception unit can select the optimal input means according to the user's input method. For example, when the user uses voice input, the reception unit inputs a description of the image using voice recognition technology. Furthermore, when the user uses text input, the reception unit can search for and input related images from the text. Furthermore, when the user directly uploads images, the reception unit can provide a drag-and-drop function to simplify input. This improves input convenience by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's input method data into AI, which can select the optimal input means.

[0077] The reception unit can estimate the user's emotions and determine the priority of images to be input based on the estimated user emotions. For example, if the user is relaxed, the reception unit can prioritize inputting images of landscapes and nature. Furthermore, if the user is in a hurry, the reception unit can prioritize inputting images of important buildings and landmarks. Furthermore, if the user is excited, the reception unit can prioritize inputting visually stimulating images. By determining the priority of images based on the user's emotions, more appropriate images can be input. The emotion estimation is realized using an emotion estimation function, such as 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. Some or all of the above-described processing in the reception unit can be performed using AI, or can be performed without AI. For example, the reception unit can input the user's facial expression data to a generation AI, which can estimate the user's emotions and prioritize the images based on the emotions.

[0078] When inputting images, the reception unit can prioritize inputting highly relevant images taking into account the user's geographical location information. For example, if the user is in a specific city, the reception unit can prioritize inputting images of the city's landmarks and tourist attractions. Furthermore, if the user is in a specific country, the reception unit can prioritize inputting images related to the country's culture and scenery. Furthermore, if the user is in a specific region, the reception unit can prioritize inputting images of the region's historical buildings and natural landscapes. In this way, highly relevant images are prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to AI, which can select highly relevant images.

[0079] When inputting an image, the reception unit can analyze the user's social media activity and input related images. For example, the reception unit can automatically input images shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and suggest related images. The reception unit can also input related images by referring to the activity of the user's friends on social media. In this way, related images are efficiently input by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into AI, which can select related images.

[0080] The reception unit can customize the input method by reflecting the user's past feedback when inputting an image. For example, the reception unit preferentially suggests image input methods that the user has previously preferred. The reception unit can also customize the input interface based on the user's past feedback. The reception unit can also make adjustments to avoid input methods that the user has previously dissatisfied with. In this way, the input method is customized by reflecting the user's past feedback, improving convenience. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into AI, which can select the optimal input method.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation method of the image analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can display the analysis results in visually calming colors. If the user is in a hurry, the analysis unit can also display the analysis results concisely and highlight important information. If the user is excited, the analysis unit can also display the analysis results with visually stimulating effects. This allows the analysis results to be adjusted based on the user's emotions, thereby deepening understanding of the analysis results. 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 such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using AI or without AI. For example, the analysis unit can input the user's facial expression data into a generation AI, which can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated emotions.

[0082] During image analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the image. For example, the analysis unit performs a detailed analysis for an image of an important building or landmark. The analysis unit can also perform a simplified analysis for a general landscape image. The analysis unit can also adjust the level of detail of the analysis based on the importance specified by the user. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the image. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input image importance data to the generation AI, which can then adjust the level of detail of the analysis.

[0083] When analyzing an image, the analysis unit can apply different analysis algorithms depending on the category of the image. For example, in the case of an image of a building, the analysis unit can apply an architectural analysis algorithm. In addition, in the case of an image of a natural landscape, the analysis unit can also apply a natural landscape analysis algorithm. In addition, in the case of an image of a person, the analysis unit can also apply a facial recognition algorithm. In this way, by applying different analysis algorithms depending on the image category, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using or without using the generation AI. For example, the analysis unit can input image category data into the generation AI, and the generation AI can select the optimal analysis algorithm.

[0084] When analyzing an image, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the results of images previously analyzed by the user. The analysis unit can also learn specific patterns from the user's past analysis results and improve the analysis accuracy. The analysis unit can also improve the analysis algorithm based on the user's past feedback. In this way, the analysis accuracy is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI, which can then adjust the analysis algorithm.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide a longer result. If the user is in a hurry, the analysis unit can also perform a concise analysis and provide a shorter result. If the user is excited, the analysis unit can also provide a visually stimulating analysis result. This allows analysis tailored to the user's needs by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using the generation AI, or without the generation AI. For example, the analysis unit can input the user's facial expression data into the generation AI, which can then estimate the user's emotions and adjust the length of the analysis based on the estimated emotions.

[0086] When analyzing images, the analysis unit can determine the priority of analysis based on the time the image was taken. For example, the analysis unit can prioritize analyzing the most recent image and provide the most recent information. In addition, the analysis unit can also analyze older images by comparing them with past data. The analysis unit can also adjust the priority of analysis based on the time the image was taken specified by the user. In this way, by determining the priority of analysis based on the time the image was taken, the most recent information is analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input image taking time data into the generation AI, and the generation AI can determine the priority of analysis.

[0087] During image analysis, the analysis unit can adjust the order of analysis based on the relevance of the images. For example, the analysis unit prioritizes analysis of highly relevant images specified by the user. The analysis unit can also perform analysis in order of relevance based on the content of the images. The analysis unit can also prioritize analysis of highly relevant images by referring to the user's past analysis results. This enables efficient analysis by adjusting the order of analysis based on the relevance of the images. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input image relevance data into the generation AI, which can then adjust the order of analysis.

[0088] During image analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user is an expert, the analysis unit can provide analysis results using detailed technical terminology. Furthermore, if the user is a general user, the analysis unit can also provide analysis results using concise and easy-to-understand terminology. The analysis unit can also adjust the use of technical terminology based on the user's past feedback. This allows for a deeper understanding of the analysis results by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI, which can then adjust the use of technical terminology.

[0089] 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 with a calm atmosphere. If the user is in a hurry, the generation unit can also generate a simple and efficient metaverse space. If the user is excited, the generation unit can also generate a visually stimulating metaverse space. This allows a virtual space tailored to the user's needs to be provided by adjusting the generation method of the metaverse space based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as 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. Some or all of the above-described processing in the generation unit can be performed using the generation AI, or can be performed without the generation AI. For example, the generation unit can input the user's emotion data into the generation AI, which can then adjust the generation method of the metaverse space based on the emotion.

[0090] When generating a metaverse space, the generation unit can adjust the level of detail of the generation based on the importance of the image. For example, in the case of an image of an important building or landmark, the generation unit generates a detailed metaverse space. In addition, in the case of a general landscape image, the generation unit can also generate a simplified metaverse space. In addition, the generation unit can adjust the level of detail of the generation based on the importance specified by the user. This enables efficient generation of a metaverse space by adjusting the level of detail of the generation based on the importance of the image. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input image importance data to the generation AI, which can then adjust the level of detail of the generation.

[0091] When generating the metaverse space, the generation unit can apply different generation algorithms depending on the image category. For example, in the case of an image of a building, the generation unit can apply an architectural generation algorithm. In addition, in the case of an image of a natural landscape, the generation unit can also apply a natural landscape generation algorithm. In addition, in the case of an image of a person, the generation unit can also apply a character generation algorithm. In this way, by applying different generation algorithms depending on the image category, the generation accuracy is improved. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input image category data into the generation AI, which can select the optimal generation algorithm.

[0092] When generating a metaverse space, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, adjusts the generation algorithm based on the results of metaverse spaces generated by the user in the past. The generation unit can also learn specific patterns from the user's past generation results and improve the generation accuracy. The generation unit can also improve the generation algorithm based on the user's past feedback. In this way, the generation accuracy is improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past generation result data into the generation AI, which can then adjust the generation algorithm.

[0093] 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 relaxed, the generation unit can prioritize generating metaverse spaces of natural scenery. Furthermore, if the user is in a hurry, the generation unit can prioritize generating metaverse spaces of important buildings and landmarks. Furthermore, if the user is excited, the generation unit can prioritize generating visually stimulating metaverse spaces. Thus, by prioritizing metaverse spaces based on the user's emotions, a more appropriate virtual space is generated. The emotion estimation is realized using an emotion estimation function, such as 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. Some or all of the above-described processing in the generation unit can be performed using the generation AI, or can be performed without the generation AI. For example, the generation unit can input the user's emotion data into the generation AI, which can then prioritize the metaverse spaces based on the emotion.

[0094] When generating a metaverse space, the generation unit can determine a generation priority based on the location where the image was taken. For example, the generation unit can prioritize generating images taken by the user in a specific city. The generation unit can also prioritize generating images taken by the user in a specific country. The generation unit can also prioritize generating images taken by the user in a specific region. In this way, by determining the generation priority based on the location where the image was taken, a highly relevant virtual space is preferentially generated. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input image shooting location data into the generation AI, and the generation AI can determine the generation priority.

[0095] When generating the metaverse space, the generation unit can improve the accuracy of generation by referring to literature related to the image. For example, the generation unit can adjust the generation algorithm by referring to academic papers related to the image. The generation unit can also improve the generation algorithm by referring to books related to the image. The generation unit can also optimize the generation algorithm by referring to websites related to the image. This improves the generation accuracy by referring to literature related to the image. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input literature data related to the image into the generation AI, which can then adjust the generation algorithm.

[0096] The generation unit can generate the metaverse space taking into consideration the market value of the image. For example, the generation unit can prioritize generating images of expensive real estate. The generation unit can also prioritize generating images of popular tourist destinations. The generation unit can also prioritize generating images of products with high market value. In this way, a high-value virtual space is generated by taking the market value of the image into consideration. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input market value data of the image into the generation AI, and the generation AI can generate the image based on the market value.

[0097] The providing unit can estimate the user's emotions and adjust the way the metaverse space is provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide the metaverse space with calming music. Furthermore, if the user is in a hurry, the providing unit can provide the metaverse space for quick access. Furthermore, if the user is excited, the providing unit can provide the metaverse space with visually stimulating effects. Thus, by adjusting the way the metaverse space is provided based on the user's emotions, a virtual space that meets the user's needs is provided. The emotion estimation is realized 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. Some or all of the above-described processing in the providing unit can be performed using AI, or can be performed without AI. For example, the providing unit can input the user's emotion data into the generation AI, which can then adjust the way the metaverse space is provided based on the emotion.

[0098] When providing the metaverse space, the providing unit can select the optimal providing method by referring to the user's past usage history. For example, the providing unit can preferentially suggest a metaverse space providing method that the user has previously preferred. The providing unit can also suggest the optimal providing method for a specific time period based on the user's past usage history. The providing unit can also customize the providing method based on the user's past feedback. In this way, the optimal providing method is selected by referring to the user's past usage history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's past usage history data into AI, which can select the optimal providing method.

[0099] When providing a metaverse space, the providing unit can customize the content provided according to the user's current task. For example, if the user wants to relax, the providing unit can provide a calm metaverse space. If the user is searching for information, the providing unit can also provide a metaverse space containing relevant information. If the user is seeking entertainment, the providing unit can also provide a visually stimulating metaverse space. In this way, by customizing the content provided according to the user's current task, a virtual space that meets the user's needs is provided. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's task data into AI, which can then customize the optimal content to be provided.

[0100] The providing unit can improve the provision method by reflecting user feedback when providing the metaverse space. For example, the providing unit adjusts the provision method based on feedback provided by the user regarding the provision method. The providing unit can also analyze the user's past feedback and optimize the provision method. The providing unit can also reflect user feedback in real time and improve the provision method. In this way, by reflecting user feedback, the provision method is improved and the user experience is enhanced. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input user feedback data into AI, which can improve the provision method.

[0101] The providing unit can estimate the user's emotions and adjust the order in which the metaverse spaces are provided based on the estimated user emotions. For example, if the user is relaxed, the providing unit can prioritize providing a calm metaverse space. Furthermore, if the user is in a hurry, the providing unit can prioritize providing a metaverse space containing important information. Furthermore, if the user is excited, the providing unit can prioritize providing a visually stimulating metaverse space. By adjusting the order in which the metaverse spaces are provided based on the user's emotions, a virtual space that meets the user's needs is provided. The emotion estimation is realized 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. Some or all of the above-described processing in the providing unit can be performed using AI, or can be performed without AI. For example, the providing unit can input the user's emotion data into the generation AI, which can then adjust the order in which the metaverse spaces are provided based on the emotion.

[0102] When providing a metaverse space, the providing unit can select the optimal providing method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a metaverse space that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a metaverse space optimized for a large screen. Furthermore, if the user is using a VR device, the providing unit can also provide a metaverse space that is highly immersive. In this way, the optimal providing method is selected by taking into account the user's device information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's device information into AI, which can then select the optimal providing method.

[0103] When providing the metaverse space, the providing unit can make the provided content multilingual according to the user's language setting. The providing unit, for example, automatically sets the language of the metaverse space based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the metaverse space in a specific language when the user selects that language. This improves user convenience by making the provided content multilingual according to the user's language setting. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data into AI, which can select the optimal language setting.

[0104] When providing a metaverse space, the providing unit can customize it based on the user's occupation and lifestyle. For example, if the user is an architect, the providing unit can provide an architecture-related metaverse space. Furthermore, if the user is a student, the providing unit can provide an education-related metaverse space. Furthermore, if the user likes to travel, the providing unit can provide a tourism-related metaverse space. In this way, by customizing based on the user's occupation and lifestyle, a virtual space that meets the user's needs is provided. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's occupation and lifestyle data into AI, which can then customize the content to be provided optimally. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 reception device 38 of the smart device 14, and a user inputs an image. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input image using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a structure of the metaverse space based on the analyzed image. The provision unit is realized, for example, by the output device 40 of the smart device 14, and provides the generated metaverse space to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 microphone 238 of the smart glasses 214, and a user inputs an image. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input image using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a structure of the metaverse space based on the analyzed image. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214, and provides the generated metaverse space to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 microphone 238 of the headset-type terminal 314, and a user inputs an image. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input image using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a structure of the metaverse space based on the analyzed image. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314, and provides the generated metaverse space to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 microphone 238 of the robot 414, and a user inputs an image. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input image using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a structure of the metaverse space based on the analyzed image. The provision unit is realized, for example, by the speaker 240 of the robot 414, and provides the generated metaverse space to the user.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The reception unit can suggest the optimal input method based on the user's past input history. For example, it can preferentially suggest input methods (drag and drop, voice input, etc.) that the user has frequently used in the past. The reception unit can also analyze the types of images (landscapes, buildings, etc.) that the user has input in the past and suggest the optimal input method. Furthermore, the reception unit can predict and suggest the input method that will be used in a specific time period based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history.

[0107] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated user's emotions. For example, if the user is relaxed, a detailed analysis can be performed and longer results can be provided. If the user is in a hurry, a concise analysis can be performed and shorter results can be provided. Furthermore, if the user is excited, visually stimulating analysis results can be provided. In this way, by adjusting the level of detail of the analysis based on the user's emotions, analysis can be performed according to the user's needs.

[0108] The generation unit can adjust the generation algorithm based on the user's past generation results. For example, the generation algorithm can be adjusted based on the results of metaverse spaces generated by the user in the past. It can also learn specific patterns from the user's past generation results to improve generation accuracy. Furthermore, it can improve the generation algorithm based on the user's past feedback. This improves generation accuracy by referring to the user's past generation results.

[0109] The providing unit can 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 with calming music. If the user is in a hurry, the metaverse space can be provided for quick access. Furthermore, if the user is excited, the metaverse space can be provided with visually stimulating effects. In this way, by adjusting the method of providing the metaverse space based on the user's emotions, a virtual space that meets the user's needs can be provided.

[0110] The reception unit can prioritize input of highly relevant images taking into account the user's geographical location information. For example, if the user is in a specific city, images of the city's landmarks and tourist attractions can be prioritized. Also, if the user is in a specific country, images related to the culture and scenery of that country can be prioritized. Furthermore, if the user is in a specific region, images of historical buildings and natural scenery of that region can be prioritized. In this way, highly relevant images are prioritized by taking into account the user's geographical location information.

[0111] When analyzing an image, the analysis unit can apply different analysis algorithms depending on the image category. For example, for an image of a building, an architectural analysis algorithm can be applied. For an image of a natural landscape, a natural landscape analysis algorithm can be applied. Furthermore, for an image of a person, a face recognition algorithm can be applied. In this way, by applying different analysis algorithms depending on the image category, the analysis accuracy can be improved.

[0112] The generation unit can estimate the user's emotions and adjust the method for generating the metaverse space based on the estimated user's emotions. For example, if the user is relaxed, a metaverse space with a calm atmosphere can be generated. If the user is in a hurry, a simple and efficient metaverse space can be generated. Furthermore, if the user is excited, a visually stimulating metaverse space can be generated. In this way, by adjusting the method for generating the metaverse space based on the user's emotions, a virtual space that meets the user's needs can be provided.

[0113] The provision unit can select the optimal provision method by referring to the user's past usage history. For example, it can preferentially suggest a metaverse space provision method that the user has previously preferred. It can also suggest the optimal provision method for a specific time period based on the user's past usage history. Furthermore, it can customize the provision method based on the user's past feedback. In this way, the optimal provision method is selected by referring to the user's past usage history.

[0114] The reception unit can estimate the user's emotions and determine the priority of images to be input based on the estimated user's emotions. For example, if the user is relaxed, images of landscapes and nature can be input preferentially. If the user is in a hurry, images of important buildings and landmarks can be input preferentially. Furthermore, if the user is excited, visually stimulating images can be input preferentially. In this way, by determining the priority of images based on the user's emotions, more appropriate images can be input.

[0115] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user is an expert, the analysis results can be provided using detailed technical terms. On the other hand, if the user is a general user, the analysis results can be provided using concise and easy-to-understand terms. Furthermore, the use of technical terms can be adjusted based on the user's past feedback. In this way, adjusting the use of technical terms in the analysis according to the user's level of expertise deepens the understanding of the analysis results.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The user inputs an image into the reception unit. For example, any image can be input, such as a landscape photo or a photo of a building. The reception unit inputs the image into the generation AI. Step 2: The analysis unit uses the generation AI to analyze the input image. For example, the generation AI extracts image features and generates a structure for the Metaverse space based on them. The generation AI can extract features using techniques such as edge detection, color histograms, and shape analysis of the image. Step 3: The generator uses AI to generate the structure of the metaverse space based on the analyzed image. For example, the AI ​​may recreate the terrain, buildings, and object placement based on the extracted features. Step 4: The provider provides the generated metaverse space to the user. For example, the user can move freely within the virtual space. The provider can provide the virtual space through a web browser, a VR device, a mobile app, etc.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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 for inputting an image; an analysis unit that analyzes the image input by the reception unit; A generation unit that generates a metaverse space based on the image analyzed by the analysis unit; a providing unit that provides the metaverse space generated by the generating unit; A system characterized by:

2. The analysis unit Extracting image features 2. The system of claim 1.

3. The generation unit Generate a structure of the metaverse space based on extracted features 2. The system of claim 1.

4. The providing unit Providing a virtual space where users can move freely 2. The system of claim 1.

5. The providing unit Providing usage scenarios for the tourism, construction, and education industries 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the timing of image input based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past image input history and select the optimal input method 2. The system of claim 1.

8. The reception unit Filter images as they are input based on the user's current project or area of ​​interest 2. The system of claim 1.

Images