system

Abstract

To provide a system that realistically reproduces past streetscapes and imaginary cities.SOLUTION: A system according to the embodiment comprises a reception unit, a generation unit and a provision unit. The reception unit receives input of past images and geographic information from a user. The generation unit analyzes the information received by the reception unit, and generates a VR space that reproduces at least one of past streetscapes or cities, or imaginary cities. The provision unit provides the VR space generated by the generation 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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it is difficult to realistically reproduce streetscapes of the past or imaginary cities, and there is room for improvement.

[0005] The system according to the embodiment aims to realistically reproduce streetscapes of the past and imaginary cities. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives input of past images and geographic information from a user. The generation unit analyzes the information received by the reception unit and generates a VR space that reproduces at least one of past streets and cities, or a fantasy city. The provision unit provides the VR space generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can realistically reproduce streetscapes of the past and imaginary cities. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The VR space generation system according to the embodiment of the present invention is a system that generates a VR space that realistically reproduces past streets and cities, or imaginary cities, by inputting past images and geographic information. This system uses a generation AI to analyze the input information and automatically generate a VR space. The generated VR space can be used in various VR business situations, such as incorporation into game platforms, simulated travel, and educational VR. The VR space generation system assumes that everyone can use and experience automatic generation of VR space using search and this generation AI. For example, a user inputs past images and geographic information into the system. For example, past photos and map data can be uploaded. This information is input into the generation AI. Next, the generation AI analyzes the input information and generates a VR space for realistically reproducing past streets and cities, or imaginary cities. The generation AI uses image analysis technology and geographic information analysis technology to analyze the input information in detail and construct a VR space. For example, the shape and arrangement of buildings are identified from past photos, and a VR space is generated based on that. The generated VR space can be used for various purposes, such as incorporating it into a game platform, virtual travel, and educational VR. For example, by incorporating it into a game platform, it is possible to provide a game in which the player explores past cityscapes. It is also possible to provide the experience of users virtually visiting past cities as a virtual travel. Furthermore, as educational VR, students can learn more deeply by using a VR space that recreates a past city in a history class. The VR space generation system assumes that everyone can use and experience the automatic generation of VR space using search and book generation AI. For example, if a user searches for "New York in the 1920s," a VR space that recreates New York of that era is automatically generated, and the user can explore that space. This allows past cityscapes and cities, or imaginary cities, to be realistically reproduced, enabling various business developments. This allows the VR space generation system to generate and provide VR space based on user input information.

[0029] The VR space generation system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives input of past images and geographic information from a user. The input information from the user includes, but is not limited to, historical photos, old maps, past satellite images, GPS data, topographical data, and map information. The reception unit receives, for example, past photos and map data uploaded by the user. The reception unit can also receive geographical information input by the user. The generation unit uses a generation AI to analyze the information received by the reception unit and generate a VR space that reproduces past streets and cities, or imaginary cities. The generation unit uses, for example, image analysis technology and geographical information analysis technology to analyze the input information in detail and generate a VR space. For example, the generation AI identifies the shape and arrangement of buildings from past photos and generates a VR space based on the identified shapes and arrangements. The generation unit can also analyze geographical information and reproduce the arrangement of terrain and features. For example, the generation AI reproduces terrain based on topographical data, and reproduces the arrangement of roads and buildings based on map information. The providing unit provides the VR space generated by the generating unit. For example, the providing unit incorporates the generated VR space into a game platform. The providing unit can also provide the generated VR space as a simulated journey. Furthermore, the providing unit can also provide the generated VR space as an educational VR. For example, the providing unit incorporates the generated VR space into a game platform and provides a game in which a player explores past cityscapes. The providing unit also provides the generated VR space as a simulated journey, providing an experience in which a user virtually visits a past city. Furthermore, the providing unit provides the generated VR space as an educational VR, and students can learn more deeply by using the VR space that reproduces a past city in a history class. Thereby, the VR space generating system according to the embodiment can generate and provide a VR space based on the user's input information.

[0030] The reception unit receives input of past images and geographic information from the user. The input information from the user includes, but is not limited to, historical photos, old maps, past satellite images, GPS data, topographical data, map information, and the like. The reception unit receives, for example, past photos and map data uploaded by the user. The reception unit can also receive geographic information input by the user. Specifically, the reception unit provides a function for the user to upload image files and geographic information files through a dedicated interface. This includes a drag-and-drop function and a file selection dialog. The reception unit also has a function for automatically classifying information input by the user and converting it into an appropriate format. For example, image files of different resolutions and formats can be converted into a unified format so that the analysis unit can efficiently process the information. Furthermore, the reception unit extracts metadata of the information input by the user and stores it in a database. This allows the information required for subsequent processing to be quickly referenced. For example, information such as the date and location of the image and the year the map was created is automatically extracted and provided to the analysis unit. The reception unit also has a function of evaluating the quality of the information entered by the user, and if the information contains missing or inappropriate information, notifying the user and encouraging correction. This allows the reception unit to efficiently and accurately receive input information from the user and provide data necessary for subsequent processing.

[0031] The generation unit uses the generation AI to analyze the information received by the reception unit, and generates a VR space that reproduces past streets and cities, or imaginary cities. The generation unit uses, for example, image analysis technology and geographic information analysis technology to analyze the input information in detail and generate the VR space. Specifically, the generation AI identifies the shape and layout of buildings from past photos, and generates the VR space based on that. The generation unit can also analyze geographic information and reproduce the layout of terrain and features. For example, the generation AI reproduces the terrain based on terrain data, and reproduces the layout of roads and buildings based on map information. The generation AI uses deep learning technology to extract the features of buildings and terrain from past photos and map data, and generate a 3D model. For this, an image recognition algorithm and a geographic information system (GIS) are used in combination. For example, the image recognition algorithm identifies the appearance and shape of a building and reproduces it as a 3D model. The GIS also analyzes the terrain data and map information to accurately reproduce the undulations of the terrain and the layout of roads. Furthermore, the generator integrates the generated 3D model into the VR space to provide an environment that the user can explore interactively. This is achieved by using real-time rendering and physical simulation technologies to realize a realistic VR space. For example, the real-time rendering technology calculates the reflection of light and the generation of shadows in real time to provide a realistic image. In addition, the physical simulation technology makes the objects in the VR space behave naturally according to the user's movements and operations. This allows the generator to generate a VR space in which the user can realistically experience past streets and cities.

[0032] The providing unit provides the VR space generated by the generating unit. For example, the providing unit incorporates the generated VR space into a game platform. The providing unit can also provide the generated VR space as a simulated journey. Furthermore, the providing unit can also provide the generated VR space as an educational VR. Specifically, the providing unit incorporates the generated VR space into a game platform and provides a game in which a player explores past cityscapes. The providing unit also provides the generated VR space as a simulated journey, providing the user with an experience of virtually visiting a past city. Furthermore, the providing unit provides the generated VR space as an educational VR, and students can learn more deeply by using a VR space that reproduces a past city in history classes. The providing unit utilizes cloud servers and streaming technology to provide these VR spaces. For example, the generated VR space is stored in a cloud server, and users can access it through the Internet. This allows users to enjoy a realistic VR experience without the need for high-performance hardware. The providing unit also has a function of automatically adjusting the optimal display settings according to the user's device. For example, the provider can set optimal resolutions and frame rates for different devices, such as VR headsets, smartphones, and tablets, to provide a comfortable experience. Furthermore, the provider can collect user feedback and continuously improve the quality and functionality of the VR space. This allows the provider to provide users with high-quality VR spaces and realize flexible services tailored to a variety of uses.

[0033] The generation unit can use image analysis technology or geographic information analysis technology to analyze the input information in detail and generate a VR space. For example, the generation unit uses image analysis technology to analyze the input past photos and identify the shape and arrangement of buildings. For example, the generation unit uses object recognition technology to identify buildings and features in the photos and generate a VR space based on the identified buildings and features. The generation unit can also use image classification technology to extract important elements in the photos and generate a VR space based on the extracted important elements. Furthermore, the generation unit can use segmentation technology to divide different areas in the photos and generate a VR space based on each area. For example, the generation unit divides the areas of buildings, roads, sky, etc. and generates a VR space based on each area. The generation unit also uses geographic information analysis technology to analyze the input geographic information and reproduce the arrangement of the terrain and features. For example, the generation unit uses GIS (geographic information system) analysis technology to analyze terrain data and reproduce the terrain. The generation unit can also reproduce the undulations and characteristics of the terrain using terrain analysis technology. Furthermore, the generation unit can also reproduce the layout of features using spatial data analysis technology. For example, the generation unit reproduces the layout of roads and buildings based on map information. This allows the generation unit to generate a detailed VR space by using image analysis technology and geographic information analysis technology.

[0034] The providing unit can incorporate the generated VR space into a game platform. The providing unit, for example, incorporates the generated VR space into a game platform such as a PC game, a console game, or a mobile game. For example, the providing unit incorporates the generated VR space into a PC game to provide a game in which a player explores a cityscape of the past. The providing unit can also incorporate the generated VR space into a console game to provide a game in which a player adventures in a city of the past. Furthermore, the providing unit can incorporate the generated VR space into a mobile game to provide a game in which a user explores a city of the past on a smartphone or tablet. For example, the providing unit can incorporate the generated VR space into a mobile game, and a user can explore a cityscape of the past by operating the screen with a finger. As a result, the providing unit can use the generated VR space as a game by incorporating it into a game platform.

[0035] The providing unit can provide the generated VR space as a simulated trip. For example, the providing unit provides the generated VR space as a virtual tour of a tourist spot. For example, the providing unit provides the generated VR space as a virtual tour of a tourist spot, providing the user with an experience of virtually visiting a tourist spot. The providing unit can also provide the generated VR space as a reproduction of a historical place. For example, the providing unit provides the generated VR space as a reproduction of a historical place, providing the user with an experience of virtually visiting a city of the past. Furthermore, the providing unit can also provide the generated VR space as a virtual trip. For example, the providing unit provides the generated VR space as a virtual trip, providing the user with an experience of virtually traveling to a city of the past. In this way, the providing unit can provide the generated VR space as a simulated trip, allowing the user to experience a virtual trip.

[0036] The providing unit can provide the generated VR space as educational VR. For example, the providing unit provides the generated VR space as VR for history education. For example, the providing unit provides the generated VR space as VR for history education, so that students can learn history through the experience of virtually visiting cities of the past. The providing unit can also provide the generated VR space as VR for geography education. For example, the providing unit provides the generated VR space as VR for geography education, so that students can virtually learn about the arrangement of topography and features. Furthermore, the providing unit can also provide the generated VR space as VR for science education. For example, the providing unit provides the generated VR space as VR for science education, so that students can virtually learn about scientific phenomena and structures. As a result, the providing unit can use the generated VR space in educational settings by providing it as educational VR.

[0037] The providing unit can automatically generate and provide a VR space based on information searched by the user. The providing unit automatically generates and provides a VR space based on, for example, a keyword searched by the user. For example, when a user searches for "New York in the 1920s," the providing unit automatically generates a VR space that reproduces New York at that time and provides it to the user. In addition, when a user performs an image search, the providing unit can automatically generate and provide a VR space based on the image. For example, when a user searches for a past photo, the providing unit automatically generates a VR space based on the photo and provides it to the user. Furthermore, when a user performs a geographic information search, the providing unit can automatically generate and provide a VR space based on the geographic information. For example, when a user searches for specific geographic information, the providing unit automatically generates a VR space based on the geographic information and provides it to the user. As a result, the providing unit can automatically generate and provide a VR space based on the information searched by the user, thereby providing a VR space according to the user's needs.

[0038] The reception unit can analyze the user's past input history and select the optimal input method. For example, the reception unit automatically displays past images and geographic information frequently input by the user in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) used by the user in the past. The reception unit can also predict and suggest past images and geographic information to be used in a specific time period from the user's past input history. For example, the reception unit automatically displays past images and geographic information frequently input by the user in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) used by the user in the past. The reception unit can also predict and suggest past images and geographic information to be used in a specific time period from the user's past input history. In this way, the optimal input method can be provided by analyzing the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0039] The reception unit can perform filtering based on the user's current project and field of interest when inputting past images and geographic information. For example, when the user is working on a historical project, the reception unit preferentially displays related past images and geographic information. In addition, when the user is interested in sightseeing, the reception unit can also preferentially display past images and geographic information of tourist spots. Furthermore, when the user is using the device for educational purposes, the reception unit can also preferentially display past images and geographic information related to education. For example, when the user is working on a historical project, the reception unit preferentially displays related past images and geographic information. In addition, when the user is interested in sightseeing, the reception unit can also preferentially display past images and geographic information of tourist spots. Furthermore, when the user is using the device for educational purposes, the reception unit can also preferentially display past images and geographic information related to education. As a result, highly relevant information can be provided by filtering based on the user's current project and field of interest. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0040] When inputting past images and geographic information, the reception unit can preferentially input highly relevant information in consideration of the geographical location information of the user. For example, the reception unit preferentially displays past images and geographical information related to the user's current location. In addition, when the user is interested in a specific area, the reception unit can also preferentially display information related to the area. Furthermore, when the user is traveling, the reception unit can also preferentially display past images and geographical information related to the travel destination. For example, the reception unit preferentially displays past images and geographical information related to the user's current location. In addition, when the user is interested in a specific area, the reception unit can also preferentially display information related to the area. Furthermore, when the user is traveling, the reception unit can also preferentially display past images and geographical information related to the travel destination. In this way, by considering the geographical location information of the user, highly relevant information can be provided. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0041] The reception unit can analyze the user's social media activity and input related information when inputting past images and geographic information. The reception unit, for example, automatically inputs past photos shared by the user on social media. The reception unit can also preferentially display information related to places the user follows on social media. The reception unit can also preferentially display information related to places the user shows interest in on social media. For example, the reception unit automatically inputs past photos shared by the user on social media. The reception unit can also preferentially display information related to places the user follows on social media. The reception unit can also preferentially display information related to places the user shows interest in on social media. This makes it possible to provide related information by analyzing the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0042] The generation unit can adjust the level of detail of the generation based on the importance of the streetscape or city in the past. For example, in the case of a historically important city, the generation unit performs detailed reproduction. In addition, in the case of a place famous as a tourist destination, the generation unit can also emphasize tourist spots. Furthermore, in the case of being used for educational purposes, the generation unit can also reproduce places with high educational value in detail. For example, in the case of a historically important city, the generation unit performs detailed reproduction. In addition, in the case of a place famous as a tourist destination, the generation unit can also emphasize tourist spots. Furthermore, in the case of being used for educational purposes, the generation unit can also reproduce places with high educational value in detail. Thereby, by adjusting the level of detail of the generation based on the importance of the streetscape or city in the past, a VR space with an appropriate level of detail can be generated. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0043] The generating unit can apply different generating algorithms depending on the category of the past image or geographic information. For example, in the case of a historical photograph, the generating unit uses an algorithm specialized for historical reproduction. In addition, in the case of a photograph of a tourist spot, the generating unit can also use an algorithm that emphasizes tourist spots. In addition, in the case of educational geographic information, the generating unit can also use an algorithm that enhances educational value. For example, in the case of a historical photograph, the generating unit uses an algorithm specialized for historical reproduction. In addition, in the case of a photograph of a tourist spot, the generating unit can also use an algorithm that emphasizes tourist spots. In addition, in the case of educational geographic information, the generating unit can also use an algorithm that enhances educational value. In this way, by applying different generating algorithms depending on the category of the past image or geographic information, an appropriate VR space can be generated. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI.

[0044] The generation unit can determine the priority of generation based on the submission time of past images and geographic information. The generation unit generates a VR space by using, for example, information that has been submitted recently with a priority. In addition, when old information is used, the generation unit can also determine the priority by taking into account historical value. Furthermore, the generation unit can also use highly relevant information with a priority based on the submission time. For example, the generation unit generates a VR space by using, for example, information that has been submitted recently with a priority. In addition, when old information is used, the generation unit can also determine the priority by taking into account historical value. Furthermore, the generation unit can also use highly relevant information with a priority based on the submission time. Thereby, by determining the priority of generation based on the submission time of past images and geographic information, the VR space can be generated in an appropriate order. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0045] The generation unit can adjust the order of generation based on the relevance of past images and geographic information. The generation unit generates a VR space by using, for example, highly relevant information with priority. The generation unit can also generate information with low relevance with a later date. Furthermore, the generation unit can generate a VR space by using information in an optimal order based on the relevance. For example, the generation unit generates a VR space by using, for example, highly relevant information with priority. The generation unit can also generate information with low relevance with a later date. Furthermore, the generation unit can generate a VR space by using information in an optimal order based on the relevance. Thereby, by adjusting the order of generation based on the relevance of past images and geographic information, a VR space can be generated in an appropriate order. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0046] The providing unit can adjust the level of detail of the provision based on the importance of the generated VR space. For example, in the case of a historically important city, the providing unit performs detailed reproduction. In addition, in the case of a place famous as a tourist destination, the providing unit can also emphasize tourist spots. Furthermore, in the case of being used for educational purposes, the providing unit can also reproduce places with high educational value in detail. For example, in the case of a historically important city, the providing unit performs detailed reproduction. In addition, in the case of a place famous as a tourist destination, the providing unit can also emphasize tourist spots. Furthermore, in the case of being used for educational purposes, the providing unit can also reproduce places with high educational value in detail. Thereby, by adjusting the level of detail of the provision based on the importance of the generated VR space, it is possible to provide a VR space with an appropriate level of detail. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0047] The providing unit can apply different providing algorithms depending on the category of the generated VR space. For example, in the case of historical reproduction, the providing unit uses an algorithm that enhances historical value. In addition, in the case of reproduction of a tourist spot, the providing unit can also use an algorithm that emphasizes tourist spots. In addition, in the case of reproduction for educational purposes, the providing unit can also use an algorithm that enhances educational value. For example, in the case of historical reproduction, the providing unit uses an algorithm that enhances historical value. In addition, in the case of reproduction of a tourist spot, the providing unit can also use an algorithm that emphasizes tourist spots. In addition, in the case of reproduction for educational purposes, the providing unit can also use an algorithm that enhances educational value. In this way, by applying different providing algorithms depending on the category of the generated VR space, an appropriate VR space can be provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0048] The providing unit can determine the priority of provision based on the submission time of the generated VR space. For example, the providing unit provides a recently generated VR space preferentially. In addition, when providing an old VR space, the providing unit can also determine the priority in consideration of historical value. Furthermore, the providing unit can also provide a highly relevant VR space preferentially based on the submission time. For example, the providing unit provides a recently generated VR space preferentially. In addition, when providing an old VR space, the providing unit can also determine the priority in consideration of historical value. Furthermore, the providing unit can also provide a highly relevant VR space preferentially based on the submission time. Thereby, by determining the priority of provision based on the submission time of the generated VR space, the VR space can be provided in an appropriate order. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0049] The providing unit can adjust the order of provision based on the relevance of the generated VR space. For example, the providing unit provides a VR space with high relevance preferentially. The providing unit can also provide a VR space with low relevance later. Furthermore, the providing unit can provide the VR space in an optimal order based on the relevance. For example, the providing unit provides a VR space with high relevance preferentially. The providing unit can also provide a VR space with low relevance later. Furthermore, the providing unit can provide the VR space in an optimal order based on the relevance. Thereby, by adjusting the order of provision based on the relevance of the generated VR space, the VR space can be provided in an appropriate order. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The reception unit can analyze the user's past input history and select the optimal input method. For example, past images and geographic information frequently input by the user in the past are automatically displayed as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest past images and geographic information to be used in a specific time period from the user's past input history. In this way, the optimal input method can be provided by analyzing the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0052] The generation unit can apply different generation algorithms depending on the category of the past images and geographic information. For example, in the case of historical photos, an algorithm specialized for historical reproduction is used. In addition, in the case of photos of tourist spots, the generation unit can also use an algorithm that emphasizes tourist spots. Furthermore, in the case of educational geographic information, the generation unit can also use an algorithm that enhances educational value. In this way, by applying different generation algorithms depending on the category of the past images and geographic information, an appropriate VR space can be generated. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI.

[0053] The providing unit can adjust the level of detail of the provision based on the importance of the generated VR space. For example, in the case of a historically important city, detailed reproduction is performed. In addition, in the case of a place famous as a tourist destination, the providing unit can also emphasize tourist spots. Furthermore, when used for educational purposes, the providing unit can also reproduce places with high educational value in detail. In this way, by adjusting the level of detail of the provision based on the importance of the generated VR space, a VR space with an appropriate level of detail can be provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0054] The providing unit can apply different providing algorithms depending on the category of the generated VR space. For example, in the case of historical reproduction, an algorithm that enhances historical value is used. In addition, in the case of reproduction of a tourist spot, the providing unit can also use an algorithm that emphasizes tourist spots. Furthermore, in the case of reproduction for educational purposes, the providing unit can also use an algorithm that enhances educational value. In this way, by applying different providing algorithms depending on the category of the generated VR space, an appropriate VR space can be provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0055] The providing unit can determine the priority of provision based on the submission time of the generated VR space. For example, the most recently generated VR space is provided preferentially. In addition, when providing an old VR space, the providing unit can determine the priority in consideration of historical value. Furthermore, the providing unit can also provide a VR space with high relevance preferentially based on the submission time. In this way, the VR space can be provided in an appropriate order by determining the priority of provision based on the submission time of the generated VR space. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0056] The process flow of the first embodiment will be briefly described below.

[0057] Step 1: The reception unit receives input of past images and geographic information from the user. The input information from the user includes historical photos, old maps, past satellite images, GPS data, topographical data, map information, etc. For example, the reception unit receives past photos and map data uploaded by the user. It can also receive geographic information input by the user. Step 2: The generation unit uses generation AI to analyze the information received by the reception unit and generate a VR space that recreates past streets and cities, or imaginary cities. The generation unit uses image analysis technology and geographic information analysis technology to analyze the input information in detail and generate the VR space. For example, the generation unit can identify the shape and layout of buildings from past photographs and generate the VR space based on that. It can also analyze geographic information to recreate the topography and layout of features. Step 3: The providing unit provides the VR space generated by the generating unit. The providing unit can incorporate the generated VR space into a game platform, provide it as a simulated trip, or provide it as educational VR. For example, a game can be provided in which the player explores past cityscapes, a user can have the experience of virtually visiting a past city, or students can learn by using a VR space that recreates a past city in a history class.

[0058] (Example 2) The VR space generation system according to the embodiment of the present invention is a system that generates a VR space that realistically reproduces past streets and cities, or imaginary cities, by inputting past images and geographic information. This system uses a generation AI to analyze the input information and automatically generate a VR space. The generated VR space can be used in various VR business situations, such as incorporation into game platforms, simulated travel, and educational VR. The VR space generation system assumes that everyone can use and experience automatic generation of VR space using search and this generation AI. For example, a user inputs past images and geographic information into the system. For example, past photos and map data can be uploaded. This information is input into the generation AI. Next, the generation AI analyzes the input information and generates a VR space for realistically reproducing past streets and cities, or imaginary cities. The generation AI uses image analysis technology and geographic information analysis technology to analyze the input information in detail and construct a VR space. For example, the shape and arrangement of buildings are identified from past photos, and a VR space is generated based on that. The generated VR space can be used for various purposes, such as incorporating it into a game platform, virtual travel, and educational VR. For example, by incorporating it into a game platform, it is possible to provide a game in which the player explores past cityscapes. It is also possible to provide the experience of users virtually visiting past cities as a virtual travel. Furthermore, as educational VR, students can learn more deeply by using a VR space that recreates a past city in a history class. The VR space generation system assumes that everyone can use and experience the automatic generation of VR space using search and book generation AI. For example, if a user searches for "New York in the 1920s," a VR space that recreates New York of that era is automatically generated, and the user can explore that space. This allows past cityscapes and cities, or imaginary cities, to be realistically reproduced, enabling various business developments. This allows the VR space generation system to generate and provide VR space based on user input information.

[0059] The VR space generation system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives input of past images and geographic information from a user. The input information from the user includes, but is not limited to, historical photos, old maps, past satellite images, GPS data, topographical data, and map information. The reception unit receives, for example, past photos and map data uploaded by the user. The reception unit can also receive geographical information input by the user. The generation unit uses a generation AI to analyze the information received by the reception unit and generate a VR space that reproduces past streets and cities, or imaginary cities. The generation unit uses, for example, image analysis technology and geographical information analysis technology to analyze the input information in detail and generate a VR space. For example, the generation AI identifies the shape and arrangement of buildings from past photos and generates a VR space based on the identified shapes and arrangements. The generation unit can also analyze geographical information and reproduce the arrangement of terrain and features. For example, the generation AI reproduces terrain based on topographical data, and reproduces the arrangement of roads and buildings based on map information. The providing unit provides the VR space generated by the generating unit. For example, the providing unit incorporates the generated VR space into a game platform. The providing unit can also provide the generated VR space as a simulated journey. Furthermore, the providing unit can also provide the generated VR space as an educational VR. For example, the providing unit incorporates the generated VR space into a game platform and provides a game in which a player explores past cityscapes. The providing unit also provides the generated VR space as a simulated journey, providing an experience in which a user virtually visits a past city. Furthermore, the providing unit provides the generated VR space as an educational VR, and students can learn more deeply by using the VR space that reproduces a past city in a history class. Thereby, the VR space generating system according to the embodiment can generate and provide a VR space based on the user's input information.

[0060] The reception unit receives input of past images and geographic information from the user. The input information from the user includes, but is not limited to, historical photos, old maps, past satellite images, GPS data, topographical data, map information, and the like. The reception unit receives, for example, past photos and map data uploaded by the user. The reception unit can also receive geographic information input by the user. Specifically, the reception unit provides a function for the user to upload image files and geographic information files through a dedicated interface. This includes a drag-and-drop function and a file selection dialog. The reception unit also has a function for automatically classifying information input by the user and converting it into an appropriate format. For example, image files of different resolutions and formats can be converted into a unified format so that the analysis unit can efficiently process the information. Furthermore, the reception unit extracts metadata of the information input by the user and stores it in a database. This allows the information required for subsequent processing to be quickly referenced. For example, information such as the date and location of the image and the year the map was created is automatically extracted and provided to the analysis unit. The reception unit also has a function of evaluating the quality of the information entered by the user, and if the information contains missing or inappropriate information, notifying the user and encouraging correction. This allows the reception unit to efficiently and accurately receive input information from the user and provide data necessary for subsequent processing.

[0061] The generation unit uses the generation AI to analyze the information received by the reception unit, and generates a VR space that reproduces past streets and cities, or imaginary cities. The generation unit uses, for example, image analysis technology and geographic information analysis technology to analyze the input information in detail and generate the VR space. Specifically, the generation AI identifies the shape and layout of buildings from past photos, and generates the VR space based on that. The generation unit can also analyze geographic information and reproduce the layout of terrain and features. For example, the generation AI reproduces the terrain based on terrain data, and reproduces the layout of roads and buildings based on map information. The generation AI uses deep learning technology to extract the features of buildings and terrain from past photos and map data, and generate a 3D model. For this, an image recognition algorithm and a geographic information system (GIS) are used in combination. For example, the image recognition algorithm identifies the appearance and shape of a building and reproduces it as a 3D model. The GIS also analyzes the terrain data and map information to accurately reproduce the undulations of the terrain and the layout of roads. Furthermore, the generator integrates the generated 3D model into the VR space to provide an environment that the user can explore interactively. This is achieved by using real-time rendering and physical simulation technologies to realize a realistic VR space. For example, the real-time rendering technology calculates the reflection of light and the generation of shadows in real time to provide a realistic image. In addition, the physical simulation technology makes the objects in the VR space behave naturally according to the user's movements and operations. This allows the generator to generate a VR space in which the user can realistically experience past streets and cities.

[0062] The providing unit provides the VR space generated by the generating unit. For example, the providing unit incorporates the generated VR space into a game platform. The providing unit can also provide the generated VR space as a simulated journey. Furthermore, the providing unit can also provide the generated VR space as an educational VR. Specifically, the providing unit incorporates the generated VR space into a game platform and provides a game in which a player explores past cityscapes. The providing unit also provides the generated VR space as a simulated journey, providing the user with an experience of virtually visiting a past city. Furthermore, the providing unit provides the generated VR space as an educational VR, and students can learn more deeply by using a VR space that reproduces a past city in history classes. The providing unit utilizes cloud servers and streaming technology to provide these VR spaces. For example, the generated VR space is stored in a cloud server, and users can access it through the Internet. This allows users to enjoy a realistic VR experience without the need for high-performance hardware. The providing unit also has a function of automatically adjusting the optimal display settings according to the user's device. For example, the provider can set optimal resolutions and frame rates for different devices, such as VR headsets, smartphones, and tablets, to provide a comfortable experience. Furthermore, the provider can collect user feedback and continuously improve the quality and functionality of the VR space. This allows the provider to provide users with high-quality VR spaces and realize flexible services tailored to a variety of uses.

[0063] The generation unit can use image analysis technology or geographic information analysis technology to analyze the input information in detail and generate a VR space. For example, the generation unit uses image analysis technology to analyze the input past photos and identify the shape and arrangement of buildings. For example, the generation unit uses object recognition technology to identify buildings and features in the photos and generate a VR space based on the identified buildings and features. The generation unit can also use image classification technology to extract important elements in the photos and generate a VR space based on the extracted important elements. Furthermore, the generation unit can use segmentation technology to divide different areas in the photos and generate a VR space based on each area. For example, the generation unit divides the areas of buildings, roads, sky, etc. and generates a VR space based on each area. The generation unit also uses geographic information analysis technology to analyze the input geographic information and reproduce the arrangement of the terrain and features. For example, the generation unit uses GIS (geographic information system) analysis technology to analyze terrain data and reproduce the terrain. The generation unit can also reproduce the undulations and characteristics of the terrain using terrain analysis technology. Furthermore, the generation unit can also reproduce the layout of features using spatial data analysis technology. For example, the generation unit reproduces the layout of roads and buildings based on map information. This allows the generation unit to generate a detailed VR space by using image analysis technology and geographic information analysis technology.

[0064] The providing unit can incorporate the generated VR space into a game platform. The providing unit, for example, incorporates the generated VR space into a game platform such as a PC game, a console game, or a mobile game. For example, the providing unit incorporates the generated VR space into a PC game to provide a game in which a player explores a cityscape of the past. The providing unit can also incorporate the generated VR space into a console game to provide a game in which a player adventures in a city of the past. Furthermore, the providing unit can incorporate the generated VR space into a mobile game to provide a game in which a user explores a city of the past on a smartphone or tablet. For example, the providing unit can incorporate the generated VR space into a mobile game, and a user can explore a cityscape of the past by operating the screen with a finger. As a result, the providing unit can use the generated VR space as a game by incorporating it into a game platform.

[0065] The providing unit can provide the generated VR space as a simulated trip. For example, the providing unit provides the generated VR space as a virtual tour of a tourist spot. For example, the providing unit provides the generated VR space as a virtual tour of a tourist spot, providing the user with an experience of virtually visiting a tourist spot. The providing unit can also provide the generated VR space as a reproduction of a historical place. For example, the providing unit provides the generated VR space as a reproduction of a historical place, providing the user with an experience of virtually visiting a city of the past. Furthermore, the providing unit can also provide the generated VR space as a virtual trip. For example, the providing unit provides the generated VR space as a virtual trip, providing the user with an experience of virtually traveling to a city of the past. In this way, the providing unit can provide the generated VR space as a simulated trip, allowing the user to experience a virtual trip.

[0066] The providing unit can provide the generated VR space as educational VR. For example, the providing unit provides the generated VR space as VR for history education. For example, the providing unit provides the generated VR space as VR for history education, so that students can learn history through the experience of virtually visiting cities of the past. The providing unit can also provide the generated VR space as VR for geography education. For example, the providing unit provides the generated VR space as VR for geography education, so that students can virtually learn about the arrangement of topography and features. Furthermore, the providing unit can also provide the generated VR space as VR for science education. For example, the providing unit provides the generated VR space as VR for science education, so that students can virtually learn about scientific phenomena and structures. As a result, the providing unit can use the generated VR space in educational settings by providing it as educational VR.

[0067] The providing unit can automatically generate and provide a VR space based on information searched by the user. The providing unit automatically generates and provides a VR space based on, for example, a keyword searched by the user. For example, when a user searches for "New York in the 1920s," the providing unit automatically generates a VR space that reproduces New York at that time and provides it to the user. In addition, when a user performs an image search, the providing unit can automatically generate and provide a VR space based on the image. For example, when a user searches for a past photo, the providing unit automatically generates a VR space based on the photo and provides it to the user. Furthermore, when a user performs a geographic information search, the providing unit can automatically generate and provide a VR space based on the geographic information. For example, when a user searches for specific geographic information, the providing unit automatically generates a VR space based on the geographic information and provides it to the user. As a result, the providing unit can automatically generate and provide a VR space based on the information searched by the user, thereby providing a VR space according to the user's needs.

[0068] The reception unit can estimate the user's emotion and adjust the input timing of past images and geographic information based on the estimated user's emotion. For example, when the user is relaxed, the reception unit provides detailed input options so that the user can freely input information. In addition, when the user is stressed, the reception unit can also provide a simple interface and minimize the input procedure. In addition, when the user is in a hurry, the reception unit can prioritize voice input and quickly input past images and geographic information. For example, when the user is relaxed, the reception unit provides detailed input options so that the user can freely input information. In addition, when the user is stressed, the reception unit can also provide a simple interface and minimize the input procedure. In addition, when the user is in a hurry, the reception unit can prioritize voice input and quickly input past images and geographic information. This allows more appropriate input by adjusting the input timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0069] The reception unit can analyze the user's past input history and select the optimal input method. For example, the reception unit automatically displays past images and geographic information frequently input by the user in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) used by the user in the past. The reception unit can also predict and suggest past images and geographic information to be used in a specific time period from the user's past input history. For example, the reception unit automatically displays past images and geographic information frequently input by the user in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) used by the user in the past. The reception unit can also predict and suggest past images and geographic information to be used in a specific time period from the user's past input history. In this way, the optimal input method can be provided by analyzing the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0070] The reception unit can perform filtering based on the user's current project and field of interest when inputting past images and geographic information. For example, when the user is working on a historical project, the reception unit preferentially displays related past images and geographic information. In addition, when the user is interested in sightseeing, the reception unit can also preferentially display past images and geographic information of tourist spots. Furthermore, when the user is using the device for educational purposes, the reception unit can also preferentially display past images and geographic information related to education. For example, when the user is working on a historical project, the reception unit preferentially displays related past images and geographic information. In addition, when the user is interested in sightseeing, the reception unit can also preferentially display past images and geographic information of tourist spots. Furthermore, when the user is using the device for educational purposes, the reception unit can also preferentially display past images and geographic information related to education. As a result, highly relevant information can be provided by filtering based on the user's current project and field of interest. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0071] The reception unit can estimate the user's emotion and determine the priority of information to be input based on the estimated user's emotion. For example, when the user is relaxed, the reception unit preferentially inputs detailed information. Also, when the user is stressed, the reception unit can also preferentially input only important information. Furthermore, when the user is in a hurry, the reception unit can also preferentially input the most necessary information. For example, when the user is relaxed, the reception unit preferentially inputs detailed information. Also, when the user is stressed, the reception unit can also preferentially input only important information. Furthermore, when the user is in a hurry, the reception unit can also preferentially input the most necessary information. In this way, by determining the priority of information to be input according to the user's emotion, more appropriate information can be input. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0072] When inputting past images and geographic information, the reception unit can preferentially input highly relevant information in consideration of the geographical location information of the user. For example, the reception unit preferentially displays past images and geographical information related to the user's current location. In addition, when the user is interested in a specific area, the reception unit can also preferentially display information related to the area. Furthermore, when the user is traveling, the reception unit can also preferentially display past images and geographical information related to the travel destination. For example, the reception unit preferentially displays past images and geographical information related to the user's current location. In addition, when the user is interested in a specific area, the reception unit can also preferentially display information related to the area. Furthermore, when the user is traveling, the reception unit can also preferentially display past images and geographical information related to the travel destination. In this way, by considering the geographical location information of the user, highly relevant information can be provided. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0073] The reception unit can analyze the user's social media activity and input related information when inputting past images and geographic information. The reception unit, for example, automatically inputs past photos shared by the user on social media. The reception unit can also preferentially display information related to places the user follows on social media. The reception unit can also preferentially display information related to places the user shows interest in on social media. For example, the reception unit automatically inputs past photos shared by the user on social media. The reception unit can also preferentially display information related to places the user follows on social media. The reception unit can also preferentially display information related to places the user shows interest in on social media. This makes it possible to provide related information by analyzing the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0074] The generation unit can estimate the user's emotion and adjust the generation method of the VR space based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates a VR space that progresses at a leisurely pace. Also, when the user is in a hurry, the generation unit can generate a VR space that emphasizes the shortest route. Furthermore, when the user is excited, the generation unit can generate a VR space with a visually stimulating effect added. For example, when the user is relaxed, the generation unit generates a VR space that progresses at a leisurely pace. Also, when the user is in a hurry, the generation unit can generate a VR space that emphasizes the shortest route. Furthermore, when the user is excited, the generation unit can generate a VR space with a visually stimulating effect added. This allows a more appropriate VR space to be generated by adjusting the generation method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0075] The generation unit can adjust the level of detail of the generation based on the importance of the streetscape or city in the past. For example, in the case of a historically important city, the generation unit performs detailed reproduction. In addition, in the case of a place famous as a tourist destination, the generation unit can also emphasize tourist spots. Furthermore, in the case of being used for educational purposes, the generation unit can also reproduce places with high educational value in detail. For example, in the case of a historically important city, the generation unit performs detailed reproduction. In addition, in the case of a place famous as a tourist destination, the generation unit can also emphasize tourist spots. Furthermore, in the case of being used for educational purposes, the generation unit can also reproduce places with high educational value in detail. Thereby, by adjusting the level of detail of the generation based on the importance of the streetscape or city in the past, a VR space with an appropriate level of detail can be generated. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0076] The generating unit can apply different generating algorithms depending on the category of the past image or geographic information. For example, in the case of a historical photograph, the generating unit uses an algorithm specialized for historical reproduction. In addition, in the case of a photograph of a tourist spot, the generating unit can also use an algorithm that emphasizes tourist spots. In addition, in the case of educational geographic information, the generating unit can also use an algorithm that enhances educational value. For example, in the case of a historical photograph, the generating unit uses an algorithm specialized for historical reproduction. In addition, in the case of a photograph of a tourist spot, the generating unit can also use an algorithm that emphasizes tourist spots. In addition, in the case of educational geographic information, the generating unit can also use an algorithm that enhances educational value. In this way, by applying different generating algorithms depending on the category of the past image or geographic information, an appropriate VR space can be generated. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI.

[0077] The generation unit can estimate the user's emotion and adjust the length of the VR space to be generated based on the estimated emotion of the user. For example, when the user is relaxed, the generation unit generates a longer VR space. Also, when the user is in a hurry, the generation unit can generate a short VR space with the main points. Furthermore, when the user is excited, the generation unit can generate a VR space with a visually stimulating effect added. For example, when the user is relaxed, the generation unit generates a longer VR space. Also, when the user is in a hurry, the generation unit can generate a short VR space with the main points. Furthermore, when the user is excited, the generation unit can generate a VR space with a visually stimulating effect added. This allows the length of the VR space to be generated according to the user's emotion to be adjusted, thereby providing a more appropriate VR space. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0078] The generation unit can determine the priority of generation based on the submission time of past images and geographic information. The generation unit generates a VR space by using, for example, information that has been submitted recently with a priority. In addition, when old information is used, the generation unit can also determine the priority by taking into account historical value. Furthermore, the generation unit can also use highly relevant information with a priority based on the submission time. For example, the generation unit generates a VR space by using, for example, information that has been submitted recently with a priority. In addition, when old information is used, the generation unit can also determine the priority by taking into account historical value. Furthermore, the generation unit can also use highly relevant information with a priority based on the submission time. Thereby, by determining the priority of generation based on the submission time of past images and geographic information, the VR space can be generated in an appropriate order. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0079] The generation unit can adjust the order of generation based on the relevance of past images and geographic information. The generation unit generates a VR space by using, for example, highly relevant information with priority. The generation unit can also generate information with low relevance with a later date. Furthermore, the generation unit can generate a VR space by using information in an optimal order based on the relevance. For example, the generation unit generates a VR space by using, for example, highly relevant information with priority. The generation unit can also generate information with low relevance with a later date. Furthermore, the generation unit can generate a VR space by using information in an optimal order based on the relevance. Thereby, by adjusting the order of generation based on the relevance of past images and geographic information, a VR space can be generated in an appropriate order. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0080] The providing unit can estimate the user's emotion and adjust the method of providing the VR space based on the estimated emotion of the user. For example, when the user is relaxed, the providing unit provides a VR space that progresses at a leisurely pace. Also, when the user is in a hurry, the providing unit can provide a VR space that emphasizes the shortest route. Furthermore, when the user is excited, the providing unit can provide a VR space with a visually stimulating effect added. For example, when the user is relaxed, the providing unit provides a VR space that progresses at a leisurely pace. Also, when the user is in a hurry, the providing unit can provide a VR space that emphasizes the shortest route. Furthermore, when the user is excited, the providing unit can provide a VR space with a visually stimulating effect added. This allows a more appropriate VR space to be provided by adjusting the providing method according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0081] The providing unit can adjust the level of detail of the provision based on the importance of the generated VR space. For example, in the case of a historically important city, the providing unit performs detailed reproduction. In addition, in the case of a place famous as a tourist destination, the providing unit can also emphasize tourist spots. Furthermore, in the case of being used for educational purposes, the providing unit can also reproduce places with high educational value in detail. For example, in the case of a historically important city, the providing unit performs detailed reproduction. In addition, in the case of a place famous as a tourist destination, the providing unit can also emphasize tourist spots. Furthermore, in the case of being used for educational purposes, the providing unit can also reproduce places with high educational value in detail. Thereby, by adjusting the level of detail of the provision based on the importance of the generated VR space, it is possible to provide a VR space with an appropriate level of detail. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0082] The providing unit can apply different providing algorithms depending on the category of the generated VR space. For example, in the case of historical reproduction, the providing unit uses an algorithm that enhances historical value. In addition, in the case of reproduction of a tourist spot, the providing unit can also use an algorithm that emphasizes tourist spots. In addition, in the case of reproduction for educational purposes, the providing unit can also use an algorithm that enhances educational value. For example, in the case of historical reproduction, the providing unit uses an algorithm that enhances historical value. In addition, in the case of reproduction of a tourist spot, the providing unit can also use an algorithm that emphasizes tourist spots. In addition, in the case of reproduction for educational purposes, the providing unit can also use an algorithm that enhances educational value. In this way, by applying different providing algorithms depending on the category of the generated VR space, an appropriate VR space can be provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0083] The providing unit can estimate the user's emotion and determine the priority of the VR space to be provided based on the estimated emotion of the user. For example, when the user is relaxed, the providing unit provides detailed information preferentially. Also, when the user is in a hurry, the providing unit can provide only important information preferentially. Furthermore, when the user is excited, the providing unit can provide a VR space with a visually stimulating effect preferentially. For example, when the user is relaxed, the providing unit provides detailed information preferentially. Also, when the user is in a hurry, the providing unit can provide only important information preferentially. Furthermore, when the user is excited, the providing unit can provide a VR space with a visually stimulating effect preferentially. This allows a more appropriate VR space to be provided by determining the priority of the VR space to be provided according to the emotion of the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0084] The providing unit can determine the priority of provision based on the submission time of the generated VR space. For example, the providing unit provides a recently generated VR space preferentially. In addition, when providing an old VR space, the providing unit can also determine the priority in consideration of historical value. Furthermore, the providing unit can also provide a highly relevant VR space preferentially based on the submission time. For example, the providing unit provides a recently generated VR space preferentially. In addition, when providing an old VR space, the providing unit can also determine the priority in consideration of historical value. Furthermore, the providing unit can also provide a highly relevant VR space preferentially based on the submission time. Thereby, by determining the priority of provision based on the submission time of the generated VR space, the VR space can be provided in an appropriate order. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0085] The providing unit can adjust the order of provision based on the relevance of the generated VR space. For example, the providing unit provides a VR space with high relevance preferentially. The providing unit can also provide a VR space with low relevance later. Furthermore, the providing unit can provide the VR space in an optimal order based on the relevance. For example, the providing unit provides a VR space with high relevance preferentially. The providing unit can also provide a VR space with low relevance later. Furthermore, the providing unit can provide the VR space in an optimal order based on the relevance. Thereby, by adjusting the order of provision based on the relevance of the generated VR space, the VR space can be provided in an appropriate order. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The reception unit can analyze the user's past input history and select the optimal input method. For example, past images and geographic information frequently input by the user in the past are automatically displayed as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest past images and geographic information to be used in a specific time period from the user's past input history. In this way, the optimal input method can be provided by analyzing the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0088] The generation unit can apply different generation algorithms depending on the category of the past images and geographic information. For example, in the case of historical photos, an algorithm specialized for historical reproduction is used. In addition, in the case of photos of tourist spots, the generation unit can also use an algorithm that emphasizes tourist spots. Furthermore, in the case of educational geographic information, the generation unit can also use an algorithm that enhances educational value. In this way, by applying different generation algorithms depending on the category of the past images and geographic information, an appropriate VR space can be generated. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI.

[0089] The providing unit can adjust the level of detail of the provision based on the importance of the generated VR space. For example, in the case of a historically important city, detailed reproduction is performed. In addition, in the case of a place famous as a tourist destination, the providing unit can also emphasize tourist spots. Furthermore, when used for educational purposes, the providing unit can also reproduce places with high educational value in detail. In this way, by adjusting the level of detail of the provision based on the importance of the generated VR space, a VR space with an appropriate level of detail can be provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0090] The providing unit can apply different providing algorithms depending on the category of the generated VR space. For example, in the case of historical reproduction, an algorithm that enhances historical value is used. In addition, in the case of reproduction of a tourist spot, the providing unit can also use an algorithm that emphasizes tourist spots. Furthermore, in the case of reproduction for educational purposes, the providing unit can also use an algorithm that enhances educational value. In this way, by applying different providing algorithms depending on the category of the generated VR space, an appropriate VR space can be provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0091] The providing unit can determine the priority of provision based on the submission time of the generated VR space. For example, the most recently generated VR space is provided preferentially. In addition, when providing an old VR space, the providing unit can determine the priority in consideration of historical value. Furthermore, the providing unit can also provide a VR space with high relevance preferentially based on the submission time. In this way, the VR space can be provided in an appropriate order by determining the priority of provision based on the submission time of the generated VR space. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0092] The reception unit can estimate the user's emotion and adjust the input timing of past images and geographic information based on the estimated user's emotion. For example, when the user is relaxed, detailed input options are provided to allow the user to freely input information. In addition, when the user is stressed, the reception unit can provide a simple interface and minimize the input procedure. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and allow the user to quickly input past images and geographic information. This allows more appropriate input by adjusting the input timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0093] The generation unit can estimate the user's emotion and adjust the generation method of the VR space based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates a VR space that progresses at a leisurely pace. In addition, when the user is in a hurry, the generation unit can also generate a VR space that emphasizes the shortest route. Furthermore, when the user is excited, the generation unit can also generate a VR space with a visually stimulating effect added. In this way, by adjusting the generation method according to the user's emotion, a more appropriate VR space can be generated. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0094] The generation unit can estimate the user's emotion and adjust the length of the VR space to be generated based on the estimated user's emotion. For example, if the user is relaxed, a longer VR space is generated. Also, if the user is in a hurry, the generation unit can generate a short VR space that is to the point. Furthermore, if the user is excited, the generation unit can generate a VR space with a visually stimulating effect. This allows a more appropriate VR space to be provided by adjusting the length of the VR space to be generated according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, 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.

[0095] The providing unit can estimate the user's emotion and adjust the method of providing the VR space based on the estimated user's emotion. For example, when the user is relaxed, the providing unit can provide a VR space that progresses at a leisurely pace. In addition, when the user is in a hurry, the providing unit can also provide a VR space that emphasizes the shortest route. Furthermore, when the user is excited, the providing unit can also provide a VR space with a visually stimulating effect added. In this way, by adjusting the providing method according to the user's emotion, a more appropriate VR space can be provided. The estimation of the emotion is realized using an emotion estimation function using, 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.

[0096] The providing unit can estimate the user's emotion and determine the priority of the VR space to be provided based on the estimated emotion of the user. For example, when the user is relaxed, detailed information is provided preferentially. In addition, when the user is in a hurry, the providing unit can also provide only important information preferentially. Furthermore, when the user is excited, the providing unit can also provide a VR space with a visually stimulating effect preferentially. In this way, by determining the priority of the VR space to be provided according to the emotion of the user, a more appropriate VR space can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0097] The process flow of the second embodiment will be briefly described below.

[0098] Step 1: The reception unit receives input of past images and geographic information from the user. The input information from the user includes historical photos, old maps, past satellite images, GPS data, topographical data, map information, etc. For example, the reception unit receives past photos and map data uploaded by the user. It can also receive geographic information input by the user. Step 2: The generation unit uses generation AI to analyze the information received by the reception unit and generate a VR space that recreates past streets and cities, or imaginary cities. The generation unit uses image analysis technology and geographic information analysis technology to analyze the input information in detail and generate the VR space. For example, the generation unit can identify the shape and layout of buildings from past photographs and generate the VR space based on that. It can also analyze geographic information to recreate the topography and layout of features. Step 3: The providing unit provides the VR space generated by the generating unit. The providing unit can incorporate the generated VR space into a game platform, provide it as a simulated trip, or provide it as educational VR. For example, a game can be provided in which the player explores past cityscapes, a user can have the experience of virtually visiting a past city, or students can learn by using a VR space that recreates a past city in a history class.

[0099] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0100] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0101] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements including the above-mentioned reception 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 control unit 46A of the smart device 14, and receives past photos and map data uploaded by the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes input information using a generation AI to generate a VR space. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the generated VR space as a game platform, a simulated trip, or an educational VR. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0106] 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.

[0107] 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 the voice according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0109] 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 performed in a secure state.

[0110] Fig. 4 shows an example of 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.

[0111] 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.

[0112] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0115] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0116] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0117] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements including the above-mentioned reception 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 control unit 46A of the smart glasses 214, and receives past photos and map data uploaded by the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes input information using a generation AI to generate a VR space. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the generated VR space as a game platform, a simulated trip, or an educational VR. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0122] 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.

[0123] 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 the voice according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0125] 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 performed in a secure state.

[0126] 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.

[0127] 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.

[0128] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0129] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0130] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0131] 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 voice indicating a user input for 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.

[0132] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0133] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements including the above-mentioned reception 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 control unit 46A of the headset type terminal 314, and receives past photos and map data uploaded by the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes input information using a generation AI to generate a VR space. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the generated VR space as a game platform, a simulated trip, or an educational VR. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0138] 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.

[0139] 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 the voice according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0141] 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 performed in a secure state.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0143] 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.

[0144] 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.

[0145] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0146] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0147] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0148] 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 a voice indicating a user input for 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.

[0149] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0150] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0151] Each of the multiple elements including the above-mentioned reception 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 control unit 46A of the robot 414, and receives past photos and map data uploaded by the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes input information using a generation AI to generate a VR space. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the generated VR space as a game platform, a simulated trip, or an educational VR. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0152] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0153] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0155] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0157] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0159] 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.

[0160] 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 Universal Serial Bus (USB) 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.

[0161] In addition, 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 upon request from the data processing device 12.

[0162] 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.

[0163] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0164] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0165] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0166] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0168] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0170] (Appendix 1) a reception unit that receives input of past images and geographic information from a user; a generation unit that analyzes the information received by the reception unit and generates a VR space that reproduces at least one of past streets or cities or a fictional city; A providing unit that provides the VR space generated by the generating unit. A system characterized by: (Appendix 2) The generation unit is Using image analysis technology or geographic information analysis technology, the input information is analyzed in detail and a VR space is generated. 2. The system of claim 1, (Appendix 3) The providing unit is Incorporating the generated VR space into a gaming platform 2. The system of claim 1, (Appendix 4) The providing unit is Providing a virtual journey through the generated VR space 2. The system of claim 1, (Appendix 5) The providing unit is Providing the generated VR space as educational VR 2. The system of claim 1, (Appendix 6) The providing unit is Automatically generate and provide a VR space based on the information searched by the user 2. The system of claim 1, (Appendix 7) The reception unit is The system estimates the user's emotions and adjusts the timing of inputting past images and geographic information based on the estimated user emotions. 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past input history and select the optimal input method 2. The system of claim 1, (Appendix 9) The reception unit is Filter historical imagery and geographic information based on the user's current project or area of ​​interest 2. The system of claim 1, (Appendix 10) The reception unit is Estimate the user's emotions and prioritize the information to be input based on the estimated user's emotions. 2. The system of claim 1, (Appendix 11) The reception unit is When inputting past images or geographic information, the system takes into account the user's geographic location information and inputs information that is highly relevant. 2. The system of claim 1, (Appendix 12) The reception unit is When past images and geographic information are entered, the system analyzes the user's social media activity and inputs relevant information. 2. The system of claim 1, (Appendix 13) The generation unit is Estimate the user's emotions and adjust the way the VR space is generated based on the estimated user emotions. 2. The system of claim 1, (Appendix 14) The generation unit is Adjust the level of detail generated based on the importance of past streetscapes and cities 2. The system of claim 1, (Appendix 15) The generation unit is Apply different generation algorithms depending on the category of past images and geographic information 2. The system of claim 1, (Appendix 16) The generation unit is Estimate the user's emotions and adjust the length of the generated VR space based on the estimated user emotions. 2. The system of claim 1, (Appendix 17) The generation unit is Prioritize generation based on past imagery and geographic information submission dates 2. The system of claim 1, (Appendix 18) The generation unit is Adjust the order of generation based on the relevance of past images and geographic information 2. The system of claim 1, (Appendix 19) The providing unit is Estimate the user's emotions and adjust the way the VR space is provided based on the estimated user's emotions. 2. The system of claim 1, (Appendix 20) The providing unit is Adjust the level of detail provided based on the importance of the generated VR space 2. The system of claim 1, (Appendix 21) The providing unit is Apply different provisioning algorithms depending on the category of the generated VR space 2. The system of claim 1, (Appendix 22) The providing unit is Estimate the user's emotions and prioritize the VR spaces to be provided based on the estimated user emotions. 2. The system of claim 1, (Appendix 23) The providing unit is Prioritize provision based on the time of submission of generated VR spaces 2. The system of claim 1, (Appendix 24) The providing unit is Adjust the order of presentation based on the relevance of the generated VR space 2. The system of claim 1, [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of past images and geographic information from a user; a generation unit that analyzes the information received by the reception unit and generates a VR space that reproduces at least one of past streets or cities or a fictional city; A providing unit that provides the VR space generated by the generating unit. A system characterized by:

2. The generation unit is Using image analysis technology or geographic information analysis technology, the input information is analyzed in detail and a VR space is generated.

2. The system of claim 1 .

3. The providing unit is The VR space generated by the generation unit is incorporated into a game platform.

2. The system of claim 1 .

4. The providing unit is Providing the VR space generated by the generation unit as a virtual journey 2. The system of claim 1 .

5. The providing unit is The VR space generated by the generation unit is provided as educational VR.

2. The system of claim 1 .

6. The providing unit is Automatically generate and provide a VR space based on the information searched by the user 2. The system of claim 1 .

7. The reception unit is The system estimates the user's emotions and adjusts the timing of inputting past images and geographic information based on the estimated user emotions.

2. The system of claim 1 .

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

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