System

Abstract

To provide a system that generates a virtual space close to a real thing and allows users to take photos and videos without having to go to a site.SOLUTION: A system according to the embodiment comprises a reception unit, a generation unit, and a capture unit. The reception unit inputs photo data. The generation unit analyzes the photo data input by the reception unit, and generates a virtual space. The capture unit takes photos and videos in the virtual 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 was difficult to generate a virtual space that was close to the real thing and take photos and videos without going to the actual location.

[0005] The system according to the embodiment aims to generate a virtual space that is close to the real thing and to take photos and videos without having to go to the actual location. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a photographing unit. The reception unit inputs photo data. The generation unit analyzes the photo data input by the reception unit and generates a virtual space. The photographing unit takes photos and videos in the virtual space generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can generate a virtual space that is close to the real thing and can take photos and videos without having to go to the actual location. [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, aperture, and 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 virtual space generation system according to the embodiment of the present invention is a system in which AI generates a virtual space based on multiple actual photo data and enables remote shooting. In this virtual space generation system, a user inputs multiple actual photo data into the system, and AI analyzes these photo data to generate a virtual space. AI can generate a more elaborate virtual space based on the number of photos and the angle of view. For example, when multiple photos of the interior of a restaurant are input, AI generates a virtual space that reproduces the interior of the restaurant based on these photos. In the generated virtual space, the user can take photos and videos from any viewpoint. For example, the user can move to a specific position in the virtual space and take photos from the viewpoint from that position. In addition, the user can move freely in the virtual space and take photos and videos from different viewpoints. With this system, a photographer can take photos remotely without going to the site, as long as he has only a few photos. For example, when shooting the interior of a restaurant or hotel in a remote location, there is no need to go to the site, and a virtual space can be generated and shooting can be performed remotely just by inputting photo data into the system. This is expected to improve the efficiency of shooting and reduce costs. Furthermore, this system can also generate virtual spaces of wide areas such as tourist spots and theme parks. For example, if multiple photos of a tourist spot are input, the AI ​​generates a virtual space of the entire tourist spot based on these photos. The user can move freely within this virtual space and take photos and videos from different perspectives. In this way, a system in which AI generates a virtual space based on multiple actual photo data and enables remote shooting contributes to improving the efficiency of shooting and reducing costs. As a result, the virtual space generation system enables users to shoot remotely, improving the efficiency of shooting and reducing costs.

[0029] The virtual space generation system according to the embodiment includes a reception unit, a generation unit, and a photographing unit. The reception unit receives photo data input by a user. The photo data includes, for example, JPEG format, PNG format, RAW format, and the like, but is not limited to such examples. The reception unit, for example, imports photo data uploaded by a user into the system. The reception unit can also automatically determine the format of the photo data and convert it into an appropriate format. The generation unit analyzes the photo data input by the reception unit and generates a virtual space. The generation unit improves the accuracy of the virtual space based on, for example, the number of photo data and the angle of view. The generation unit uses a generation AI to generate a 3D model based on the photo data and build a virtual space. For example, the generation AI analyzes multiple photo data, identifies the positional relationship between each photo, and generates a 3D model. The generation unit can also use the generation AI to analyze the angle of view of the photo data and adjust the viewpoint of the virtual space. For example, the generation AI analyzes wide-angle photos and telephoto photos, and integrates the respective viewpoints to generate a virtual space. The photographing unit photographs and videos in the virtual space generated by the generation unit. The photographing unit, for example, enables the user to move freely in the virtual space and take photos and videos from different viewpoints. The photographing unit includes a movement unit and a viewpoint unit, and controls movement and change of viewpoint in the virtual space. For example, the movement unit enables the user to move to a specific position in the virtual space, and the viewpoint unit enables the user to take photos and videos from the viewpoint from that position. As a result, the virtual space generation system according to the embodiment enables the user to take photos remotely, thereby realizing efficient shooting and cost reduction.

[0030] The reception unit receives photo data input by the user. Photo data includes, for example, JPEG format, PNG format, RAW format, etc., but is not limited to such examples. The reception unit, for example, takes in photo data uploaded by the user into the system. Specifically, when the user uploads photo data through a web interface or a dedicated application, the reception unit immediately receives the data and stores it in the system. The reception unit can also automatically determine the format of the photo data and convert it into an appropriate format. For example, if the photo uploaded by the user is in JPEG format, it is accepted as is, and if it is in RAW format, the system automatically converts it to JPEG format. This allows the user to easily upload photo data without being concerned about a specific format. Furthermore, the reception unit can check the resolution and quality of the photo data and optimize it as necessary. For example, low-resolution photo data can be processed to improve the resolution using AI. This can provide basic data for the generation unit to generate a high-quality virtual space. The reception unit can also add metadata to the photo data uploaded by the user. For example, the date and time of shooting, location, camera setting information, etc. are automatically acquired and associated with the photo data. This can provide reference information for the generation unit to generate a more accurate virtual space.

[0031] The generation unit analyzes the photo data input by the reception unit and generates a virtual space. The generation unit improves the accuracy of the virtual space based on, for example, the number of photo data and the angle of view. Specifically, the generation unit analyzes multiple photo data, identifies the positional relationship between each photo, and generates a 3D model. The generation AI uses image recognition technology to extract feature points of the photo data and calculates the positional relationship between the photos based on those feature points. For example, it identifies feature points such as the corners of a building and the positions of windows, and builds a 3D model based on them. The generation AI can also analyze the angle of view of the photo data and adjust the viewpoint of the virtual space. For example, it analyzes wide-angle photos and telephoto photos, and generates a virtual space by integrating the respective viewpoints. This allows the user to experience a realistic virtual space based on photos taken from different viewpoints. Furthermore, the generation unit performs texture mapping on the generated 3D model to provide a more realistic virtual space. For example, it applies the color and texture of the photo data to the 3D model to reproduce a look that is close to reality. The generation unit can also simulate the effects of light sources and shadows to reproduce the lighting environment in the virtual space. This allows the user to experience a virtual space that is close to a real environment. Furthermore, the generation unit can also update the generated virtual space in real time. For example, an existing virtual space can be updated based on newly uploaded photo data, and always kept up to date. This allows the user to experience a virtual space that is always based on the latest information.

[0032] The photographing unit takes photos and videos in the virtual space generated by the generating unit. The photographing unit, for example, enables the user to move freely in the virtual space and take photos and videos from different viewpoints. Specifically, the photographing unit includes a moving unit and a viewpoint unit, and controls movement in the virtual space and change of viewpoint. The moving unit enables the user to move to a specific position in the virtual space, and the viewpoint unit enables the user to take photos and videos from the viewpoint from that position. For example, the user can move to the inside of a specific building in the virtual space and take photos of the interior from different viewpoints. In addition, the viewpoint unit adjusts the camera angle and zoom based on the viewpoint selected by the user to provide optimal shooting conditions. This allows the user to enjoy shooting in the virtual space as a real experience. Furthermore, the photographing unit can automatically save the photographed photos and videos so that the user can view and share them later. For example, the photographed data is stored in cloud storage so that the user can access it at any time. In addition, the photographing unit can automatically tag the photographed data so that it can be easily searched later. This allows the user to efficiently manage the photographed data and quickly find the information he or she needs. Furthermore, the shooting unit can provide a live streaming function in the virtual space. For example, while a user moves in the virtual space, the user can broadcast the movement to other users in real time. This allows multiple users to experience and share the virtual space at the same time.

[0033] The generation unit may include an accuracy improvement unit that improves the accuracy of the virtual space based on the number of photos or the angle of view. For example, the accuracy improvement unit can generate a more detailed virtual space the more the number of photos is. For example, when the accuracy improvement unit generates a virtual space based on 10 pieces of photo data, the accuracy improvement unit provides a more accurate virtual space than when the virtual space is generated based on 5 pieces of photo data. In addition, the accuracy improvement unit can generate a wider virtual space the wider the angle of view of the photos is. For example, when the accuracy improvement unit generates a virtual space based on photo data taken with a wide-angle lens, the accuracy improvement unit provides a wider virtual space than when the virtual space is generated based on photo data taken with a standard lens. Thereby, the accuracy improvement unit can improve the accuracy of the virtual space based on the number of photos and the angle of view. A part or all of the above-mentioned processing in the accuracy improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the accuracy improvement unit can perform accuracy improvement using an AI model that inputs the number of photo data and the angle of view and outputs the accuracy of the virtual space.

[0034] The shooting unit may include a moving unit that controls movement in the virtual space. The moving unit, for example, enables the user to move freely in the virtual space. For example, the moving unit provides an interface for the user to move to a specific position in the virtual space. The moving unit may also control the speed and direction at which the user moves. For example, the moving unit enables the user to move as if walking in the virtual space, and also enables the user to move as if flying in the virtual space. In this way, the moving unit controls movement in the virtual space and enables the user to freely change the viewpoint. A part or all of the above-mentioned processing in the moving unit may be performed, for example, using AI, or may be performed without using AI. For example, the moving unit may control movement using an AI model that inputs the user's movement pattern and outputs an optimal movement method.

[0035] The shooting unit may include a viewpoint unit that controls the change of viewpoint in the virtual space. The viewpoint unit, for example, enables the user to take photos and videos from different viewpoints in the virtual space. For example, the viewpoint unit provides an interface for the user to move to a specific position in the virtual space and take photos and videos from the viewpoint from that position. The viewpoint unit may also control the angle and height at which the user changes the viewpoint. For example, the viewpoint unit enables the user to change the viewpoint from a high position to a low position in the virtual space, and also enables the user to change the viewpoint from a wide field of view to a narrow field of view in the virtual space. In this way, the viewpoint unit controls the change of viewpoint in the virtual space and enables the user to take pictures from different viewpoints. A part or all of the above-mentioned processing in the viewpoint unit may be performed, for example, using AI, or may be performed without using AI. For example, the viewpoint unit may control the change of viewpoint using an AI model that inputs the user's viewpoint change pattern and outputs an optimal viewpoint change method.

[0036] The generating unit can generate a virtual space of a wide area such as a tourist spot or a theme park. The generating unit generates a virtual space of a wide area based on, for example, multiple photo data of a tourist spot or a theme park. For example, the generating unit analyzes photo data of major landmarks and facilities of the tourist spot, and builds a virtual space of the entire tourist spot based on the photo data. The generating unit can also analyze photo data of attractions and facilities of the theme park, and build a virtual space of the entire theme park based on the photo data. In this way, the generating unit generates a virtual space of a wide area, and enables a user to move freely within the area and take photos and videos from different perspectives. 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. For example, the generating unit can generate a virtual space using an AI model that inputs photo data of a tourist spot or a theme park and outputs a virtual space of a wide area.

[0037] The photographing unit can enable the user to move to a specific position in the virtual space and take a photo from a viewpoint from that position. The photographing unit provides, for example, an interface for the user to move to a specific position in the virtual space and take a photo from a viewpoint from that position. For example, the photographing unit enables the user to move to a specific coordinate in the virtual space and take a photo from a viewpoint from that coordinate. The photographing unit also enables the user to move to a specific landmark or spot of interest in the virtual space and take a photo from a viewpoint from that position. In this way, the photographing unit enables the user to take a photo from a specific position in the virtual space. A part or all of the above-mentioned processing in the photographing unit may be performed, for example, using AI or may be performed without using AI. For example, the photographing unit can control the shooting using an AI model that inputs the user's movement pattern and viewpoint change pattern and outputs the optimal shooting position.

[0038] The reception unit can analyze the user's past photo data reception history and select the optimal reception method. For example, the reception unit preferentially suggests a reception method that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period from the user's past reception history. Furthermore, the reception unit can analyze the user's past reception history and select the most efficient reception method. This allows the reception unit to select the optimal reception method based on the user's past reception 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. For example, the reception unit can select the reception method using an AI model that inputs the user's past reception history data and outputs the optimal reception method.

[0039] The reception unit can perform filtering based on the user's current project and field of interest when receiving the photo data. For example, the reception unit preferentially receives photo data related to a project currently in progress by the user. The reception unit can also filter and receive highly relevant photo data based on the user's field of interest. Furthermore, the reception unit can also select and receive necessary photo data according to the progress of the user's project. This allows the reception unit to filter the photo data based on the user's project and field of interest. 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. For example, the reception unit can perform filtering using an AI model that inputs the user's project data and field of interest data and outputs optimal photo data.

[0040] When receiving the photo data, the reception unit can preferentially receive data with high relevance in consideration of the geographical location information of the user. For example, the reception unit preferentially receives photo data taken at a location close to the user's current location. The reception unit can also preferentially receive photo data of tourist spots and facilities with high relevance based on the user's geographical location information. Furthermore, the reception unit can also select and receive optimal photo data in consideration of the distance from the user's current location. This allows the reception unit to preferentially receive data with high relevance based on the user's geographical location information. 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. For example, the reception unit can perform reception using an AI model that inputs the user's geographical location information data and outputs optimal photo data.

[0041] The reception unit can analyze the user's social media activity and receive related data when receiving the photo data. For example, the reception unit preferentially receives photo data shared by the user on social media. The reception unit can also receive photo data of places and events of high interest from the user's social media activity. Furthermore, the reception unit can also preferentially receive photo data shared by the user's social media followers. This allows the reception unit to receive related data based on the user's social media activity. 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. For example, the reception unit can perform reception using an AI model that receives the user's social media activity data as input and outputs related photo data.

[0042] The generation unit can adjust the level of detail of the generation based on the importance of the photo data when generating the virtual space. The generation unit generates a detailed virtual space based on, for example, photo data with high importance. The generation unit can also generate a simplified virtual space based on photo data with low importance. Furthermore, the generation unit can also adjust the level of detail of the generation stepwise according to the importance of the photo data. This allows the generation unit to adjust the level of detail of the generation based on the importance of the photo data. 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. For example, the generation unit can adjust the level of detail of the generation using an AI model that inputs the importance of the photo data and outputs the level of detail of the generation.

[0043] The generation unit can apply different generation algorithms depending on the category of photo data when generating the virtual space. For example, the generation unit applies a generation algorithm that reproduces interior details to photo data of a restaurant. The generation unit can also apply a generation algorithm that reproduces a wide area to photo data of a park. Furthermore, the generation unit can apply a generation algorithm that reproduces room details to photo data of a hotel. This allows the generation unit to apply different generation algorithms depending on the category of photo data. 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. For example, the generation unit can apply a generation algorithm using an AI model that inputs the category of photo data and outputs an optimal generation algorithm.

[0044] The generation unit can determine the priority of generation based on the submission time of the photo data when generating the virtual space. For example, the generation unit generates the virtual space by using the most recently submitted photo data preferentially. The generation unit can also generate the virtual space by putting the photo data submitted earlier on hold. Furthermore, the generation unit can also adjust the priority of generation in stages according to the submission time. This allows the generation unit to determine the priority of generation based on the submission time of the photo data. 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. For example, the generation unit can determine the priority of generation using an AI model that inputs the submission time of the photo data and outputs the priority of generation.

[0045] The generation unit can adjust the order of generation based on the relevance of the photo data when generating the virtual space. For example, the generation unit generates the virtual space by using photo data with high relevance preferentially. The generation unit can also generate the virtual space by putting photo data with low relevance on hold. Furthermore, the generation unit can also adjust the order of generation stepwise according to the relevance of the photo data. This allows the generation unit to adjust the order of generation based on the relevance of the photo data. 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. For example, the generation unit can adjust the order of generation using an AI model that inputs the relevance of the photo data and outputs the order of generation.

[0046] The photographing unit can adjust the level of detail of the photograph based on important points in the virtual space when photographing. The photographing unit, for example, focuses on important points and photographs highly detailed photos. The photographing unit can also photograph points with a simplified level of detail for points with low importance. Furthermore, the photographing unit can gradually adjust the level of detail of the photograph according to important points in the virtual space. This allows the photographing unit to adjust the level of detail of the photograph based on important points in the virtual space. Part or all of the above-mentioned processing in the photographing unit may be performed, for example, using AI or may be performed without using AI. For example, the photographing unit can adjust the level of detail of the photograph using an AI model that inputs important points in the virtual space and outputs the level of detail of the photograph.

[0047] The photographing unit can apply different photographing algorithms depending on the category in the virtual space when photographing. For example, when photographing the interior of a restaurant, the photographing unit applies a photographing algorithm optimized for indoor lighting. In addition, when photographing a park, the photographing unit can also apply a photographing algorithm optimized for natural light. Furthermore, when photographing a hotel room, the photographing unit can also apply a photographing algorithm that emphasizes the details of the room. This allows the photographing unit to apply different photographing algorithms depending on the category in the virtual space. Part or all of the above-mentioned processing in the photographing unit may be performed, for example, using AI or may be performed without using AI. For example, the photographing unit can apply a photographing algorithm using an AI model that inputs a category in the virtual space and outputs an optimal photographing algorithm.

[0048] The photographing unit can determine the priority of photographing based on a specific position in the virtual space when photographing. For example, the photographing unit prioritizes photographing from an important position. The photographing unit can also postpone photographing from a position of low importance. Furthermore, the photographing unit can also adjust the priority of photographing stepwise according to the specific position in the virtual space. This allows the photographing unit to determine the priority of photographing based on the specific position in the virtual space. A part or all of the above-mentioned processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can determine the priority of photographing using an AI model that inputs a specific position in the virtual space and outputs the priority of photographing.

[0049] The photographing unit can adjust the order of photographing based on the relevance in the virtual space when photographing. For example, the photographing unit prioritizes photographing from a position with high relevance. The photographing unit can also postpone photographing from a position with low relevance. Furthermore, the photographing unit can also adjust the order of photographing stepwise according to the relevance in the virtual space. This allows the photographing unit to adjust the order of photographing based on the relevance in the virtual space. A part or all of the above-mentioned processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can adjust the order of photographing using an AI model that inputs the relevance in the virtual space and outputs the order of photographing.

[0050] The accuracy improvement unit can select the optimal accuracy improvement method by analyzing the past history of the photo data when improving the accuracy. The accuracy improvement unit selects the optimal accuracy improvement method based on, for example, the history of photo data used in the past. The accuracy improvement unit can also select an accuracy improvement method under specific conditions from the past history. Furthermore, the accuracy improvement unit can analyze the past history of the photo data and select the most effective accuracy improvement method. This allows the accuracy improvement unit to select the optimal accuracy improvement method based on the past history of the photo data. A part or all of the above-mentioned processing in the accuracy improvement unit may be performed, for example, using AI, or may be performed without using AI. For example, the accuracy improvement unit can select the accuracy improvement method using an AI model that inputs the past history of the photo data and outputs the optimal accuracy improvement method.

[0051] The accuracy improvement unit can select the optimal accuracy improvement method in consideration of the geographical location information of the photo data when improving the accuracy. The accuracy improvement unit selects the optimal accuracy improvement method based on, for example, the geographical location information. The accuracy improvement unit can also select an accuracy improvement method suitable for a specific region in consideration of the geographical location information. Furthermore, the accuracy improvement unit can also select the most effective accuracy improvement method based on the geographical location information. This allows the accuracy improvement unit to select the optimal accuracy improvement method based on the geographical location information of the photo data. A part or all of the above-mentioned processing in the accuracy improvement unit may be performed, for example, using AI, or may be performed without using AI. For example, the accuracy improvement unit can select the accuracy improvement method using an AI model that inputs the geographical location information of the photo data and outputs the optimal accuracy improvement method.

[0052] The movement unit can select the optimal movement method by referring to the past movement history in the virtual space when moving. The movement unit selects the optimal movement method based on the past movement history, for example. The movement unit can also select the optimal movement method under specific conditions from the past movement history. Furthermore, the movement unit can analyze the past movement history in the virtual space and select the most effective movement method. This allows the movement unit to select the optimal movement method based on the past movement history in the virtual space. A part or all of the above-mentioned processing in the movement unit may be performed, for example, using AI, or may be performed without using AI. For example, the movement unit can select the movement method using an AI model that inputs the past movement history in the virtual space and outputs the optimal movement method.

[0053] The moving unit can select the optimal moving method in consideration of the geographical location information in the virtual space when moving. The moving unit selects the optimal moving method based on, for example, the geographical location information. The moving unit can also select a moving method suitable for a specific area in consideration of the geographical location information. Furthermore, the moving unit can also select the most effective moving method based on the geographical location information. This allows the moving unit to select the optimal moving method based on the geographical location information in the virtual space. A part or all of the above-mentioned processing in the moving unit may be performed using, for example, AI, or may be performed without using AI. For example, the moving unit can select a moving method using an AI model that inputs geographical location information in the virtual space and outputs the optimal moving method.

[0054] The viewpoint unit can select an optimal viewpoint change method by referring to past viewpoint history in the virtual space when changing the viewpoint. The viewpoint unit selects an optimal viewpoint change method based on, for example, past viewpoint history. The viewpoint unit can also select an optimal viewpoint change method under specific conditions from the past viewpoint history. Furthermore, the viewpoint unit can analyze past viewpoint history in the virtual space and select the most effective viewpoint change method. This allows the viewpoint unit to select an optimal viewpoint change method based on the past viewpoint history in the virtual space. A part or all of the above-mentioned processing in the viewpoint unit may be performed using, for example, AI, or may be performed without using AI. For example, the viewpoint unit can select a viewpoint change method using an AI model that inputs past viewpoint history in the virtual space and outputs an optimal viewpoint change method.

[0055] The viewpoint unit can select an optimal viewpoint change method in consideration of geographical location information in the virtual space when changing the viewpoint. The viewpoint unit selects an optimal viewpoint change method based on, for example, geographical location information. The viewpoint unit can also select a viewpoint change method suitable for a specific region in consideration of geographical location information. Furthermore, the viewpoint unit can also select the most effective viewpoint change method based on the geographical location information. This allows the viewpoint unit to select an optimal viewpoint change method based on the geographical location information in the virtual space. A part or all of the above-mentioned processing in the viewpoint unit may be performed using, for example, AI, or may be performed without using AI. For example, the viewpoint unit can select a viewpoint change method using an AI model that inputs geographical location information in the virtual space and outputs an optimal viewpoint change method.

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

[0057] The reception unit can analyze the user's past photo data reception history and select the optimal reception method. For example, the reception unit preferentially suggests the reception method that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period from the user's past reception history. Furthermore, the reception unit can analyze the user's past reception history and select the most efficient reception method. This allows the reception unit to select the optimal reception method based on the user's past reception 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. For example, the reception unit can select the reception method using an AI model that inputs the user's past reception history data and outputs the optimal reception method.

[0058] The generation unit can adjust the level of detail of the generation based on the importance of the photo data when generating the virtual space. For example, a detailed virtual space is generated based on photo data with high importance. The generation unit can also generate a simplified virtual space based on photo data with low importance. Furthermore, the generation unit can also adjust the level of detail of the generation stepwise according to the importance of the photo data. This allows the generation unit to adjust the level of detail of the generation based on the importance of the photo data. 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. For example, the generation unit can adjust the level of detail of the generation using an AI model that inputs the importance of the photo data and outputs the level of detail of the generation.

[0059] The photographing unit can adjust the level of detail of the photograph based on important points in the virtual space when photographing. For example, a highly detailed photograph is taken by focusing on important points. The photographing unit can also photograph points with a simplified level of detail for points with a low level of importance. Furthermore, the photographing unit can gradually adjust the level of detail of the photograph according to important points in the virtual space. This allows the photographing unit to adjust the level of detail of the photograph based on important points in the virtual space. A part or all of the above-mentioned processing in the photographing unit may be performed, for example, using AI or may be performed without using AI. For example, the photographing unit can adjust the level of detail of the photograph using an AI model that inputs important points in the virtual space and outputs the level of detail of the photograph.

[0060] The movement unit can select the optimal movement method by referring to the past movement history in the virtual space when moving. For example, the optimal movement method is selected based on the past movement history. The movement unit can also select the optimal movement method under specific conditions from the past movement history. Furthermore, the movement unit can analyze the past movement history in the virtual space and select the most effective movement method. This allows the movement unit to select the optimal movement method based on the past movement history in the virtual space. A part or all of the above-mentioned processing in the movement unit may be performed, for example, using AI, or may be performed without using AI. For example, the movement unit can select the movement method using an AI model that inputs the past movement history in the virtual space and outputs the optimal movement method.

[0061] The viewpoint unit can select an optimal viewpoint change method by referring to past viewpoint history in the virtual space when changing the viewpoint. For example, the optimal viewpoint change method is selected based on the past viewpoint history. The viewpoint unit can also select an optimal viewpoint change method under specific conditions from the past viewpoint history. Furthermore, the viewpoint unit can analyze the past viewpoint history in the virtual space and select the most effective viewpoint change method. This allows the viewpoint unit to select an optimal viewpoint change method based on the past viewpoint history in the virtual space. A part or all of the above-mentioned processing in the viewpoint unit may be performed using, for example, AI, or may be performed without using AI. For example, the viewpoint unit can select a viewpoint change method using an AI model that inputs the past viewpoint history in the virtual space and outputs the optimal viewpoint change method.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The reception unit receives photo data input by the user. The photo data includes, but is not limited to, JPEG, PNG, and RAW formats. The reception unit can also import the photo data uploaded by the user into the system, automatically determine the format of the photo data, and convert it into an appropriate format. Step 2: The generation unit analyzes the photo data input by the reception unit and generates a virtual space. The generation unit improves the accuracy of the virtual space based on the number of photo data and the angle of view. Using the generation AI, a 3D model is generated based on the photo data and the virtual space is constructed. The generation AI analyzes multiple photo data, identifies the relative positions of each photo, and generates a 3D model. The generation AI can also analyze the angle of view of the photo data and adjust the viewpoint of the virtual space. Step 3: The capture unit captures photos and videos in the virtual space generated by the generation unit. The capture unit enables the user to move freely within the virtual space and capture photos and videos from different viewpoints. The capture unit includes a movement unit and a viewpoint unit, and controls movement within the virtual space and changes in viewpoint. The movement unit enables the user to move to a specific position within the virtual space, and the viewpoint unit enables the user to capture photos and videos from a viewpoint from that position.

[0064] (Example 2) The virtual space generation system according to the embodiment of the present invention is a system in which AI generates a virtual space based on multiple actual photo data and enables remote shooting. In this virtual space generation system, a user inputs multiple actual photo data into the system, and AI analyzes these photo data to generate a virtual space. AI can generate a more elaborate virtual space based on the number of photos and the angle of view. For example, when multiple photos of the interior of a restaurant are input, AI generates a virtual space that reproduces the interior of the restaurant based on these photos. In the generated virtual space, the user can take photos and videos from any viewpoint. For example, the user can move to a specific position in the virtual space and take photos from the viewpoint from that position. In addition, the user can move freely in the virtual space and take photos and videos from different viewpoints. With this system, a photographer can take photos remotely without going to the site, as long as he has only a few photos. For example, when shooting the interior of a restaurant or hotel in a remote location, there is no need to go to the site, and a virtual space can be generated and shooting can be performed remotely just by inputting photo data into the system. This is expected to improve the efficiency of shooting and reduce costs. Furthermore, this system can also generate virtual spaces of wide areas such as tourist spots and theme parks. For example, if multiple photos of a tourist spot are input, the AI ​​generates a virtual space of the entire tourist spot based on these photos. The user can move freely within this virtual space and take photos and videos from different perspectives. In this way, a system in which AI generates a virtual space based on multiple actual photo data and enables remote shooting contributes to improving the efficiency of shooting and reducing costs. As a result, the virtual space generation system enables users to shoot remotely, improving the efficiency of shooting and reducing costs.

[0065] The virtual space generation system according to the embodiment includes a reception unit, a generation unit, and a photographing unit. The reception unit receives photo data input by a user. The photo data includes, for example, JPEG format, PNG format, RAW format, and the like, but is not limited to such examples. The reception unit, for example, imports photo data uploaded by a user into the system. The reception unit can also automatically determine the format of the photo data and convert it into an appropriate format. The generation unit analyzes the photo data input by the reception unit and generates a virtual space. The generation unit improves the accuracy of the virtual space based on, for example, the number of photo data and the angle of view. The generation unit uses a generation AI to generate a 3D model based on the photo data and build a virtual space. For example, the generation AI analyzes multiple photo data, identifies the positional relationship between each photo, and generates a 3D model. The generation unit can also use the generation AI to analyze the angle of view of the photo data and adjust the viewpoint of the virtual space. For example, the generation AI analyzes wide-angle photos and telephoto photos, and integrates the respective viewpoints to generate a virtual space. The photographing unit photographs and videos in the virtual space generated by the generation unit. The photographing unit, for example, enables the user to move freely in the virtual space and take photos and videos from different viewpoints. The photographing unit includes a movement unit and a viewpoint unit, and controls movement and change of viewpoint in the virtual space. For example, the movement unit enables the user to move to a specific position in the virtual space, and the viewpoint unit enables the user to take photos and videos from the viewpoint from that position. As a result, the virtual space generation system according to the embodiment enables the user to take photos remotely, thereby realizing efficient shooting and cost reduction.

[0066] The reception unit receives photo data input by the user. The photo data includes, but is not limited to, JPEG, PNG, and RAW formats. The reception unit, for example, takes in photo data uploaded by the user into the system. Specifically, when the user uploads photo data through a web interface or a dedicated application, the reception unit immediately receives the data and stores it in the system. The reception unit can also automatically determine the format of the photo data and convert it into an appropriate format. For example, if the photo uploaded by the user is in JPEG format, it is accepted as is, and if it is in RAW format, the system automatically converts it to JPEG format. This allows the user to easily upload photo data without being concerned about a specific format. Furthermore, the reception unit can check the resolution and quality of the photo data and optimize it as necessary. For example, low-resolution photo data can be processed to improve the resolution using AI. This can provide basic data for the generation unit to generate a high-quality virtual space. The reception unit can also add metadata to the photo data uploaded by the user. For example, the date and time of shooting, location, camera setting information, etc. are automatically acquired and associated with the photo data. This can provide reference information for the generation unit to generate a more accurate virtual space.

[0067] The generation unit analyzes the photo data input by the reception unit and generates a virtual space. The generation unit improves the accuracy of the virtual space based on, for example, the number of photo data and the angle of view. Specifically, the generation unit analyzes multiple photo data, identifies the positional relationship between each photo, and generates a 3D model. The generation AI uses image recognition technology to extract feature points of the photo data and calculates the positional relationship between the photos based on those feature points. For example, it identifies feature points such as the corners of a building and the positions of windows, and builds a 3D model based on them. The generation AI can also analyze the angle of view of the photo data and adjust the viewpoint of the virtual space. For example, it analyzes wide-angle photos and telephoto photos, and generates a virtual space by integrating the respective viewpoints. This allows the user to experience a realistic virtual space based on photos taken from different viewpoints. Furthermore, the generation unit performs texture mapping on the generated 3D model to provide a more realistic virtual space. For example, it applies the color and texture of the photo data to the 3D model to reproduce a look that is close to reality. The generation unit can also simulate the effects of light sources and shadows to reproduce the lighting environment in the virtual space. This allows the user to experience a virtual space that is close to a real environment. Furthermore, the generation unit can also update the generated virtual space in real time. For example, an existing virtual space can be updated based on newly uploaded photo data, and always kept up to date. This allows the user to experience a virtual space that is always based on the latest information.

[0068] The photographing unit takes photos and videos in the virtual space generated by the generating unit. The photographing unit, for example, enables the user to move freely in the virtual space and take photos and videos from different viewpoints. Specifically, the photographing unit includes a moving unit and a viewpoint unit, and controls movement in the virtual space and change of viewpoint. The moving unit enables the user to move to a specific position in the virtual space, and the viewpoint unit enables the user to take photos and videos from the viewpoint from that position. For example, the user can move to the inside of a specific building in the virtual space and take photos of the interior from different viewpoints. In addition, the viewpoint unit adjusts the camera angle and zoom based on the viewpoint selected by the user to provide optimal shooting conditions. This allows the user to enjoy shooting in the virtual space as a real experience. Furthermore, the photographing unit can automatically save the photographed photos and videos so that the user can view and share them later. For example, the photographed data is stored in cloud storage so that the user can access it at any time. In addition, the photographing unit can automatically tag the photographed data so that it can be easily searched later. This allows the user to efficiently manage the photographed data and quickly find the information he or she needs. Furthermore, the shooting unit can provide a live streaming function in the virtual space. For example, while a user moves in the virtual space, the user can broadcast the movement to other users in real time. This allows multiple users to experience and share the virtual space at the same time.

[0069] The generation unit may include an accuracy improvement unit that improves the accuracy of the virtual space based on the number of photos or the angle of view. For example, the accuracy improvement unit can generate a more detailed virtual space the more the number of photos is. For example, when the accuracy improvement unit generates a virtual space based on 10 pieces of photo data, the accuracy improvement unit provides a more accurate virtual space than when the virtual space is generated based on 5 pieces of photo data. In addition, the accuracy improvement unit can generate a wider virtual space the wider the angle of view of the photos is. For example, when the accuracy improvement unit generates a virtual space based on photo data taken with a wide-angle lens, the accuracy improvement unit provides a wider virtual space than when the virtual space is generated based on photo data taken with a standard lens. Thereby, the accuracy improvement unit can improve the accuracy of the virtual space based on the number of photos and the angle of view. A part or all of the above-mentioned processing in the accuracy improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the accuracy improvement unit can perform accuracy improvement using an AI model that inputs the number of photo data and the angle of view and outputs the accuracy of the virtual space.

[0070] The shooting unit may include a moving unit that controls movement in the virtual space. The moving unit, for example, enables the user to move freely in the virtual space. For example, the moving unit provides an interface for the user to move to a specific position in the virtual space. The moving unit may also control the speed and direction at which the user moves. For example, the moving unit enables the user to move as if walking in the virtual space, and also enables the user to move as if flying in the virtual space. In this way, the moving unit controls movement in the virtual space and enables the user to freely change the viewpoint. A part or all of the above-mentioned processing in the moving unit may be performed, for example, using AI, or may be performed without using AI. For example, the moving unit may control movement using an AI model that inputs the user's movement pattern and outputs an optimal movement method.

[0071] The shooting unit may include a viewpoint unit that controls the change of viewpoint in the virtual space. The viewpoint unit, for example, enables the user to take photos and videos from different viewpoints in the virtual space. For example, the viewpoint unit provides an interface for the user to move to a specific position in the virtual space and take photos and videos from the viewpoint from that position. The viewpoint unit may also control the angle and height at which the user changes the viewpoint. For example, the viewpoint unit enables the user to change the viewpoint from a high position to a low position in the virtual space, and also enables the user to change the viewpoint from a wide field of view to a narrow field of view in the virtual space. In this way, the viewpoint unit controls the change of viewpoint in the virtual space and enables the user to take pictures from different viewpoints. A part or all of the above-mentioned processing in the viewpoint unit may be performed, for example, using AI, or may be performed without using AI. For example, the viewpoint unit may control the change of viewpoint using an AI model that inputs the user's viewpoint change pattern and outputs an optimal viewpoint change method.

[0072] The generating unit can generate a virtual space of a wide area such as a tourist spot or a theme park. The generating unit generates a virtual space of a wide area based on, for example, multiple photo data of a tourist spot or a theme park. For example, the generating unit analyzes photo data of major landmarks and facilities of the tourist spot, and builds a virtual space of the entire tourist spot based on the photo data. The generating unit can also analyze photo data of attractions and facilities of the theme park, and build a virtual space of the entire theme park based on the photo data. In this way, the generating unit generates a virtual space of a wide area, and enables a user to move freely within the area and take photos and videos from different perspectives. 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. For example, the generating unit can generate a virtual space using an AI model that inputs photo data of a tourist spot or a theme park and outputs a virtual space of a wide area.

[0073] The photographing unit can enable the user to move to a specific position in the virtual space and take a photo from a viewpoint from that position. The photographing unit provides, for example, an interface for the user to move to a specific position in the virtual space and take a photo from a viewpoint from that position. For example, the photographing unit enables the user to move to a specific coordinate in the virtual space and take a photo from a viewpoint from that coordinate. The photographing unit also enables the user to move to a specific landmark or spot of interest in the virtual space and take a photo from a viewpoint from that position. In this way, the photographing unit enables the user to take a photo from a specific position in the virtual space. A part or all of the above-mentioned processing in the photographing unit may be performed, for example, using AI or may be performed without using AI. For example, the photographing unit can control the shooting using an AI model that inputs the user's movement pattern and viewpoint change pattern and outputs the optimal shooting position.

[0074] The reception unit can estimate the user's emotion and adjust the timing of receiving the photo data based on the estimated user's emotion. For example, when the user is excited, the reception unit immediately receives the photo data and quickly starts generating the virtual space. Also, when the user is relaxed, the reception unit can receive the photo data at a slow pace and perform detailed confirmation. Furthermore, when the user is stressed, the reception unit can receive the photo data in a simplified procedure to reduce the burden on the user. This allows the reception unit to adjust the timing of receiving the photo data 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. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data and voice data to the generation AI and cause the generation AI to execute emotion estimation.

[0075] The reception unit can analyze the user's past photo data reception history and select the optimal reception method. For example, the reception unit preferentially suggests a reception method that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period from the user's past reception history. Furthermore, the reception unit can analyze the user's past reception history and select the most efficient reception method. This allows the reception unit to select the optimal reception method based on the user's past reception 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. For example, the reception unit can select the reception method using an AI model that inputs the user's past reception history data and outputs the optimal reception method.

[0076] The reception unit can perform filtering based on the user's current project and field of interest when receiving the photo data. For example, the reception unit preferentially receives photo data related to a project currently in progress by the user. The reception unit can also filter and receive highly relevant photo data based on the user's field of interest. Furthermore, the reception unit can also select and receive necessary photo data according to the progress of the user's project. This allows the reception unit to filter the photo data based on the user's project and field of interest. 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. For example, the reception unit can perform filtering using an AI model that inputs the user's project data and field of interest data and outputs optimal photo data.

[0077] The reception unit can estimate the user's emotion and determine the priority order of the photo data to be received based on the estimated user's emotion. For example, when the user is excited, the reception unit preferentially receives photo data with a high importance. In addition, when the user is relaxed, the reception unit can also preferentially receive photo data that requires detailed confirmation. Furthermore, when the user is stressed, the reception unit can also preferentially receive photo data with a low importance through a simplified procedure. This allows the reception unit to determine the priority order of the photo data 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. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data and voice data to the generation AI and cause the generation AI to execute emotion estimation.

[0078] When receiving the photo data, the reception unit can preferentially receive data with high relevance in consideration of the geographical location information of the user. For example, the reception unit preferentially receives photo data taken at a location close to the user's current location. The reception unit can also preferentially receive photo data of tourist spots and facilities with high relevance based on the user's geographical location information. Furthermore, the reception unit can also select and receive optimal photo data in consideration of the distance from the user's current location. This allows the reception unit to preferentially receive data with high relevance based on the user's geographical location information. 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. For example, the reception unit can perform reception using an AI model that inputs the user's geographical location information data and outputs optimal photo data.

[0079] The reception unit can analyze the user's social media activity and receive related data when receiving the photo data. For example, the reception unit preferentially receives photo data shared by the user on social media. The reception unit can also receive photo data of places and events of high interest from the user's social media activity. Furthermore, the reception unit can also preferentially receive photo data shared by the user's social media followers. This allows the reception unit to receive related data based on the user's social media activity. 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. For example, the reception unit can perform reception using an AI model that receives the user's social media activity data as input and outputs related photo data.

[0080] The generation unit can estimate the user's emotion and adjust the generation method of the virtual space based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates a virtual space that progresses at a leisurely pace. In addition, when the user is in a hurry, the generation unit can also generate a virtual space that emphasizes the shortest route. Furthermore, when the user is excited, the generation unit can generate a virtual space with a visually stimulating effect added. This allows the generation unit to adjust the generation method of the virtual space 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 a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's facial expression data and voice data to the generation AI and cause the generation AI to execute emotion estimation.

[0081] The generation unit can adjust the level of detail of the generation based on the importance of the photo data when generating the virtual space. The generation unit generates a detailed virtual space based on, for example, photo data with high importance. The generation unit can also generate a simplified virtual space based on photo data with low importance. Furthermore, the generation unit can also adjust the level of detail of the generation stepwise according to the importance of the photo data. This allows the generation unit to adjust the level of detail of the generation based on the importance of the photo data. 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. For example, the generation unit can adjust the level of detail of the generation using an AI model that inputs the importance of the photo data and outputs the level of detail of the generation.

[0082] The generation unit can apply different generation algorithms depending on the category of photo data when generating the virtual space. For example, the generation unit applies a generation algorithm that reproduces interior details to photo data of a restaurant. The generation unit can also apply a generation algorithm that reproduces a wide area to photo data of a park. Furthermore, the generation unit can apply a generation algorithm that reproduces room details to photo data of a hotel. This allows the generation unit to apply different generation algorithms depending on the category of photo data. 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. For example, the generation unit can apply a generation algorithm using an AI model that inputs the category of photo data and outputs an optimal generation algorithm.

[0083] The generation unit can estimate the user's emotion and determine the priority of the virtual space to be generated based on the estimated user's emotion. For example, when the user is excited, the generation unit preferentially generates a visually stimulating virtual space. In addition, when the user is relaxed, the generation unit can also preferentially generate a virtual space with a calm atmosphere. Furthermore, when the user is in a hurry, the generation unit can also preferentially generate a virtual space that can be generated quickly. This allows the generation unit to determine the priority of the virtual space 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 a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's facial expression data and voice data to the generation AI and cause the generation AI to execute emotion estimation.

[0084] The generation unit can determine the priority of generation based on the submission time of the photo data when generating the virtual space. For example, the generation unit generates the virtual space by using the most recently submitted photo data preferentially. The generation unit can also generate the virtual space by putting the photo data submitted earlier on hold. Furthermore, the generation unit can also adjust the priority of generation in stages according to the submission time. This allows the generation unit to determine the priority of generation based on the submission time of the photo data. 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. For example, the generation unit can determine the priority of generation using an AI model that inputs the submission time of the photo data and outputs the priority of generation.

[0085] The generation unit can adjust the order of generation based on the relevance of the photo data when generating the virtual space. For example, the generation unit generates the virtual space by using photo data with high relevance preferentially. The generation unit can also generate the virtual space by putting photo data with low relevance on hold. Furthermore, the generation unit can also adjust the order of generation stepwise according to the relevance of the photo data. This allows the generation unit to adjust the order of generation based on the relevance of the photo data. 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. For example, the generation unit can adjust the order of generation using an AI model that inputs the relevance of the photo data and outputs the order of generation.

[0086] The photographing unit can estimate the user's emotion and adjust the expression method of the photograph based on the estimated user's emotion. For example, when the user is relaxed, the photographing unit takes a photo with a soft color tone. Furthermore, when the user is excited, the photographing unit can also take a photo with a vivid color tone. Furthermore, when the user is stressed, the photographing unit can also take a photo with a calm color tone. This allows the photographing unit to adjust the expression method of the photograph 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. A part or all of the above-mentioned processing in the photographing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the photographing unit can input the user's facial expression data and voice data to the generation AI and cause the generation AI to execute emotion estimation.

[0087] The photographing unit can adjust the level of detail of the photograph based on important points in the virtual space when photographing. The photographing unit, for example, focuses on important points and photographs highly detailed photos. The photographing unit can also photograph points with a simplified level of detail for points with low importance. Furthermore, the photographing unit can gradually adjust the level of detail of the photograph according to important points in the virtual space. This allows the photographing unit to adjust the level of detail of the photograph based on important points in the virtual space. Part or all of the above-mentioned processing in the photographing unit may be performed, for example, using AI or may be performed without using AI. For example, the photographing unit can adjust the level of detail of the photograph using an AI model that inputs important points in the virtual space and outputs the level of detail of the photograph.

[0088] The photographing unit can apply different photographing algorithms depending on the category in the virtual space when photographing. For example, when photographing the interior of a restaurant, the photographing unit applies a photographing algorithm optimized for indoor lighting. In addition, when photographing a park, the photographing unit can also apply a photographing algorithm optimized for natural light. Furthermore, when photographing a hotel room, the photographing unit can also apply a photographing algorithm that emphasizes the details of the room. This allows the photographing unit to apply different photographing algorithms depending on the category in the virtual space. Part or all of the above-mentioned processing in the photographing unit may be performed, for example, using AI or may be performed without using AI. For example, the photographing unit can apply a photographing algorithm using an AI model that inputs a category in the virtual space and outputs an optimal photographing algorithm.

[0089] The photographing unit can estimate the user's emotion and adjust the length of the photographing based on the estimated user's emotion. For example, when the user is in a hurry, the photographing unit completes the photographing in a short time. Also, when the user is relaxed, the photographing unit can take detailed photographs over a long period of time. Furthermore, when the user is excited, the photographing unit can take photographs for an appropriate length of time. This allows the photographing unit to adjust the length of the photographing 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, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the photographing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the photographing unit can input the user's facial expression data and voice data to the generation AI and cause the generation AI to execute emotion estimation.

[0090] The photographing unit can determine the priority of photographing based on a specific position in the virtual space when photographing. For example, the photographing unit prioritizes photographing from an important position. The photographing unit can also postpone photographing from a position of low importance. Furthermore, the photographing unit can also adjust the priority of photographing stepwise according to the specific position in the virtual space. This allows the photographing unit to determine the priority of photographing based on the specific position in the virtual space. A part or all of the above-mentioned processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can determine the priority of photographing using an AI model that inputs a specific position in the virtual space and outputs the priority of photographing.

[0091] The photographing unit can adjust the order of photographing based on the relevance in the virtual space when photographing. For example, the photographing unit prioritizes photographing from a position with high relevance. The photographing unit can also postpone photographing from a position with low relevance. Furthermore, the photographing unit can also adjust the order of photographing stepwise according to the relevance in the virtual space. This allows the photographing unit to adjust the order of photographing based on the relevance in the virtual space. A part or all of the above-mentioned processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can adjust the order of photographing using an AI model that inputs the relevance in the virtual space and outputs the order of photographing.

[0092] The accuracy improvement unit can estimate the user's emotion and adjust the accuracy improvement method based on the estimated user's emotion. For example, when the user is relaxed, the accuracy improvement unit performs a detailed analysis to improve the accuracy. Also, when the user is in a hurry, the accuracy improvement unit can perform a quick analysis to improve the accuracy. Furthermore, when the user is excited, the accuracy improvement unit can add a visually stimulating effect to improve the accuracy. This allows the accuracy improvement unit to adjust the accuracy improvement 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 a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the accuracy improvement unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the accuracy improvement unit can input the user's facial expression data and voice data to the generation AI and cause the generation AI to execute emotion estimation.

[0093] The accuracy improvement unit can select the optimal accuracy improvement method by analyzing the past history of the photo data when improving the accuracy. The accuracy improvement unit selects the optimal accuracy improvement method based on, for example, the history of photo data used in the past. The accuracy improvement unit can also select an accuracy improvement method under specific conditions from the past history. Furthermore, the accuracy improvement unit can analyze the past history of the photo data and select the most effective accuracy improvement method. This allows the accuracy improvement unit to select the optimal accuracy improvement method based on the past history of the photo data. A part or all of the above-mentioned processing in the accuracy improvement unit may be performed, for example, using AI, or may be performed without using AI. For example, the accuracy improvement unit can select the accuracy improvement method using an AI model that inputs the past history of the photo data and outputs the optimal accuracy improvement method.

[0094] The accuracy improvement unit can estimate the user's emotion and determine the priority of accuracy improvement based on the estimated user's emotion. For example, when the user is excited, the accuracy improvement unit prioritizes accuracy improvement with a high degree of importance. In addition, when the user is relaxed, the accuracy improvement unit can also prioritize detailed accuracy improvement. Furthermore, when the user is in a hurry, the accuracy improvement unit can also prioritize quick accuracy improvement. This allows the accuracy improvement unit to determine the priority of accuracy improvement 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 a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the accuracy improvement unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the accuracy improvement unit can input the user's facial expression data and voice data to the generation AI and cause the generation AI to execute emotion estimation.

[0095] The accuracy improvement unit can select the optimal accuracy improvement method in consideration of the geographical location information of the photo data when improving the accuracy. The accuracy improvement unit selects the optimal accuracy improvement method based on, for example, the geographical location information. The accuracy improvement unit can also select an accuracy improvement method suitable for a specific region in consideration of the geographical location information. Furthermore, the accuracy improvement unit can also select the most effective accuracy improvement method based on the geographical location information. This allows the accuracy improvement unit to select the optimal accuracy improvement method based on the geographical location information of the photo data. A part or all of the above-mentioned processing in the accuracy improvement unit may be performed, for example, using AI, or may be performed without using AI. For example, the accuracy improvement unit can select the accuracy improvement method using an AI model that inputs the geographical location information of the photo data and outputs the optimal accuracy improvement method.

[0096] The movement unit can estimate the user's emotion and adjust the movement method in the virtual space based on the estimated user's emotion. For example, when the user is relaxed, the movement unit provides a leisurely movement method. Furthermore, when the user is in a hurry, the movement unit can also provide a quick movement method. Furthermore, when the user is excited, the movement unit can also provide a visually stimulating movement method. This allows the movement unit to adjust the movement method in the virtual space 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. A part or all of the above-mentioned processing in the movement unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the movement unit can input the user's facial expression data and voice data to the generation AI and cause the generation AI to execute emotion estimation.

[0097] The movement unit can select the optimal movement method by referring to the past movement history in the virtual space when moving. The movement unit selects the optimal movement method based on the past movement history, for example. The movement unit can also select the optimal movement method under specific conditions from the past movement history. Furthermore, the movement unit can analyze the past movement history in the virtual space and select the most effective movement method. This allows the movement unit to select the optimal movement method based on the past movement history in the virtual space. A part or all of the above-mentioned processing in the movement unit may be performed, for example, using AI, or may be performed without using AI. For example, the movement unit can select the movement method using an AI model that inputs the past movement history in the virtual space and outputs the optimal movement method.

[0098] The movement unit can estimate the user's emotion and determine the priority of movement based on the estimated user's emotion. For example, when the user is excited, the movement unit prioritizes movement to important points. In addition, when the user is relaxed, the movement unit can also prioritize movement to detailed points. Furthermore, when the user is in a hurry, the movement unit can also prioritize quick movement. This allows the movement unit to determine the priority of movement 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. A part or all of the above-mentioned processing in the movement unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the movement unit can input the user's facial expression data and voice data to the generation AI and cause the generation AI to execute emotion estimation.

[0099] The moving unit can select the optimal moving method in consideration of the geographical location information in the virtual space when moving. The moving unit selects the optimal moving method based on, for example, the geographical location information. The moving unit can also select a moving method suitable for a specific area in consideration of the geographical location information. Furthermore, the moving unit can also select the most effective moving method based on the geographical location information. This allows the moving unit to select the optimal moving method based on the geographical location information in the virtual space. A part or all of the above-mentioned processing in the moving unit may be performed using, for example, AI, or may be performed without using AI. For example, the moving unit can select a moving method using an AI model that inputs geographical location information in the virtual space and outputs the optimal moving method.

[0100] The viewpoint unit can estimate the user's emotion and adjust the viewpoint change method based on the estimated user's emotion. For example, the viewpoint unit provides a relaxed viewpoint change method when the user is relaxed. In addition, the viewpoint unit can also provide a quick viewpoint change method when the user is in a hurry. Furthermore, the viewpoint unit can also provide a visually stimulating viewpoint change method when the user is excited. This allows the viewpoint unit to adjust the viewpoint change 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 a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the viewpoint unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the viewpoint unit can input the user's facial expression data and voice data to the generation AI and cause the generation AI to execute emotion estimation.

[0101] The viewpoint unit can select an optimal viewpoint change method by referring to past viewpoint history in the virtual space when changing the viewpoint. The viewpoint unit selects an optimal viewpoint change method based on, for example, past viewpoint history. The viewpoint unit can also select an optimal viewpoint change method under specific conditions from the past viewpoint history. Furthermore, the viewpoint unit can analyze past viewpoint history in the virtual space and select the most effective viewpoint change method. This allows the viewpoint unit to select an optimal viewpoint change method based on the past viewpoint history in the virtual space. A part or all of the above-mentioned processing in the viewpoint unit may be performed using, for example, AI, or may be performed without using AI. For example, the viewpoint unit can select a viewpoint change method using an AI model that inputs past viewpoint history in the virtual space and outputs an optimal viewpoint change method.

[0102] The viewpoint unit can estimate the user's emotion and determine the priority of the viewpoint change based on the estimated user's emotion. For example, when the user is excited, the viewpoint unit prioritizes the viewpoint change to an important point. In addition, when the user is relaxed, the viewpoint unit can also prioritize the viewpoint change to a detailed point. Furthermore, when the user is in a hurry, the viewpoint unit can also prioritize a quick viewpoint change. This allows the viewpoint unit to determine the priority of the viewpoint change 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 a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the viewpoint unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the viewpoint unit can input the user's facial expression data and voice data to the generation AI and cause the generation AI to execute emotion estimation.

[0103] The viewpoint unit can select an optimal viewpoint change method in consideration of geographical location information in the virtual space when changing the viewpoint. The viewpoint unit selects an optimal viewpoint change method based on, for example, geographical location information. The viewpoint unit can also select a viewpoint change method suitable for a specific region in consideration of geographical location information. Furthermore, the viewpoint unit can also select the most effective viewpoint change method based on the geographical location information. This allows the viewpoint unit to select an optimal viewpoint change method based on the geographical location information in the virtual space. A part or all of the above-mentioned processing in the viewpoint unit may be performed using, for example, AI, or may be performed without using AI. For example, the viewpoint unit can select a viewpoint change method using an AI model that inputs geographical location information in the virtual space and outputs an optimal viewpoint change method.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The reception unit can analyze the user's past photo data reception history and select the optimal reception method. For example, the reception unit preferentially suggests the reception method that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period from the user's past reception history. Furthermore, the reception unit can analyze the user's past reception history and select the most efficient reception method. This allows the reception unit to select the optimal reception method based on the user's past reception 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. For example, the reception unit can select the reception method using an AI model that inputs the user's past reception history data and outputs the optimal reception method.

[0106] The generation unit can adjust the level of detail of the generation based on the importance of the photo data when generating the virtual space. For example, a detailed virtual space is generated based on photo data with high importance. The generation unit can also generate a simplified virtual space based on photo data with low importance. Furthermore, the generation unit can also adjust the level of detail of the generation stepwise according to the importance of the photo data. This allows the generation unit to adjust the level of detail of the generation based on the importance of the photo data. 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. For example, the generation unit can adjust the level of detail of the generation using an AI model that inputs the importance of the photo data and outputs the level of detail of the generation.

[0107] The photographing unit can adjust the level of detail of the photograph based on important points in the virtual space when photographing. For example, a highly detailed photograph is taken by focusing on important points. The photographing unit can also photograph points with a simplified level of detail for points with a low level of importance. Furthermore, the photographing unit can gradually adjust the level of detail of the photograph according to important points in the virtual space. This allows the photographing unit to adjust the level of detail of the photograph based on important points in the virtual space. A part or all of the above-mentioned processing in the photographing unit may be performed, for example, using AI or may be performed without using AI. For example, the photographing unit can adjust the level of detail of the photograph using an AI model that inputs important points in the virtual space and outputs the level of detail of the photograph.

[0108] The movement unit can select the optimal movement method by referring to the past movement history in the virtual space when moving. For example, the optimal movement method is selected based on the past movement history. The movement unit can also select the optimal movement method under specific conditions from the past movement history. Furthermore, the movement unit can analyze the past movement history in the virtual space and select the most effective movement method. This allows the movement unit to select the optimal movement method based on the past movement history in the virtual space. A part or all of the above-mentioned processing in the movement unit may be performed, for example, using AI, or may be performed without using AI. For example, the movement unit can select the movement method using an AI model that inputs the past movement history in the virtual space and outputs the optimal movement method.

[0109] The viewpoint unit can select an optimal viewpoint change method by referring to past viewpoint history in the virtual space when changing the viewpoint. For example, the optimal viewpoint change method is selected based on the past viewpoint history. The viewpoint unit can also select an optimal viewpoint change method under specific conditions from the past viewpoint history. Furthermore, the viewpoint unit can analyze the past viewpoint history in the virtual space and select the most effective viewpoint change method. This allows the viewpoint unit to select an optimal viewpoint change method based on the past viewpoint history in the virtual space. A part or all of the above-mentioned processing in the viewpoint unit may be performed using, for example, AI, or may be performed without using AI. For example, the viewpoint unit can select a viewpoint change method using an AI model that inputs the past viewpoint history in the virtual space and outputs the optimal viewpoint change method.

[0110] The reception unit can estimate the user's emotion and adjust the timing of receiving the photo data based on the estimated user's emotion. For example, when the user is excited, the reception unit immediately receives the photo data and quickly starts generating the virtual space. Also, when the user is relaxed, the reception unit can receive the photo data at a slow pace and perform detailed confirmation. Furthermore, when the user is stressed, the reception unit can receive the photo data in a simplified procedure to reduce the burden on the user. This allows the reception unit to adjust the timing of receiving the photo data 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. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data and voice data to the generation AI and cause the generation AI to execute emotion estimation.

[0111] The generation unit can estimate the user's emotion and adjust the method of generating the virtual space based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates a virtual space that progresses at a leisurely pace. In addition, when the user is in a hurry, the generation unit can also generate a virtual space that emphasizes the shortest route. Furthermore, when the user is excited, the generation unit can generate a virtual space with a visually stimulating effect added. This allows the generation unit to adjust the method of generating the virtual space 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 a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's facial expression data and voice data to the generation AI and cause the generation AI to execute emotion estimation.

[0112] The photographing unit can estimate the user's emotion and adjust the expression method of the photograph based on the estimated user's emotion. For example, when the user is relaxed, the photographing unit takes a photo with a soft color tone. In addition, when the user is excited, the photographing unit can also take a photo with a vivid color tone. Furthermore, when the user is stressed, the photographing unit can also take a photo with a calm color tone. This allows the photographing unit to adjust the expression method of the photograph 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 a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the photographing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the photographing unit can input the user's facial expression data and voice data to the generation AI and cause the generation AI to execute emotion estimation.

[0113] The accuracy improvement unit can estimate the user's emotion and adjust the accuracy improvement method based on the estimated user's emotion. For example, when the user is relaxed, a detailed analysis is performed to improve the accuracy. In addition, when the user is in a hurry, the accuracy improvement unit can also perform a quick analysis to improve the accuracy. Furthermore, when the user is excited, the accuracy improvement unit can add a visually stimulating effect to improve the accuracy. This allows the accuracy improvement unit to adjust the accuracy improvement 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 a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the accuracy improvement unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the accuracy improvement unit can input the user's facial expression data and voice data to the generation AI and cause the generation AI to execute emotion estimation.

[0114] The movement unit can estimate the user's emotion and adjust the movement method in the virtual space based on the estimated user's emotion. For example, when the user is relaxed, the movement unit can provide a leisurely movement method. In addition, when the user is in a hurry, the movement unit can also provide a quick movement method. Furthermore, when the user is excited, the movement unit can also provide a visually stimulating movement method. This allows the movement unit to adjust the movement method in the virtual space 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. A part or all of the above-mentioned processing in the movement unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the movement unit can input the user's facial expression data and voice data to the generation AI and cause the generation AI to execute emotion estimation.

[0115] The process flow of the second embodiment will be briefly described below.

[0116] Step 1: The reception unit receives photo data input by the user. The photo data includes, but is not limited to, JPEG, PNG, and RAW formats. The reception unit can also import the photo data uploaded by the user into the system, automatically determine the format of the photo data, and convert it into an appropriate format. Step 2: The generation unit analyzes the photo data input by the reception unit and generates a virtual space. The generation unit improves the accuracy of the virtual space based on the number of photo data and the angle of view. Using the generation AI, a 3D model is generated based on the photo data and the virtual space is constructed. The generation AI analyzes multiple photo data, identifies the relative positions of each photo, and generates a 3D model. The generation AI can also analyze the angle of view of the photo data and adjust the viewpoint of the virtual space. Step 3: The capture unit captures photos and videos in the virtual space generated by the generation unit. The capture unit enables the user to move freely within the virtual space and capture photos and videos from different viewpoints. The capture unit includes a movement unit and a viewpoint unit, and controls movement within the virtual space and changes in viewpoint. The movement unit enables the user to move to a specific position within the virtual space, and the viewpoint unit enables the user to capture photos and videos from a viewpoint from that position.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements including the above-mentioned reception unit, generation unit, and photographing 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 imports photo data uploaded by the user into the system. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the photo data to generate a virtual space. The photographing unit is realized, for example, by the control unit 46A of the smart device 14, and enables photographs and videos to be taken in the virtual space. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements including the above-mentioned reception unit, generation unit, and photographing 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 imports photo data uploaded by a user into the system. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the photo data to generate a virtual space. The photographing unit is realized, for example, by the control unit 46A of the smart glasses 214, and enables photographs and videos to be taken in the virtual space. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements including the above-mentioned reception unit, generation unit, and photographing 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 takes in the photo data uploaded by the user into the system. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the photo data to generate a virtual space. The photographing unit is realized, for example, by the control unit 46A of the headset type terminal 314, and enables photographs and videos to be taken in the virtual space. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements including the above-mentioned reception unit, generation unit, and photographing 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 takes in the photo data uploaded by the user into the system. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the photo data to generate a virtual space. The photographing unit is realized, for example, by the control unit 46A of the robot 414, and enables photographs and videos to be taken in the virtual space. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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 on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Appendix 1) A reception section for inputting photographic data; a generation unit that analyzes the photo data input by the reception unit and generates a virtual space; A photographing unit that takes pictures and videos in the virtual space generated by the generating unit. A system characterized by: (Appendix 2) The generation unit is Equipped with an accuracy improvement unit that improves the accuracy of the virtual space based on the number of photos or the angle of view 2. The system of claim 1, (Appendix 3) The imaging unit includes: Equipped with a movement unit that controls movement within the virtual space 2. The system of claim 1, (Appendix 4) The imaging unit includes: Equipped with a viewpoint unit that controls changes in viewpoint within the virtual space 2. The system of claim 1, (Appendix 5) The generation unit is Generate virtual spaces of large areas such as tourist destinations or theme parks 2. The system of claim 1, (Appendix 6) The imaging unit includes: Allows users to move to a specific location in a virtual space and take a photo from that location's perspective 2. The system of claim 1, (Appendix 7) The reception unit is To estimate a user's emotion and adjust a timing of receiving photo data based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past photo data reception history and select the optimal reception method 2. The system of claim 1, (Appendix 9) The reception unit is Filtering photo data as it arrives based on the user's current projects and interests 2. The system of claim 1, (Appendix 10) The reception unit is A user's emotion is estimated, and a priority order for the photo data to be accepted is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 11) The reception unit is When accepting photo data, the system takes into account the user's geographic location information and prioritizes accepting data that is highly relevant 2. The system of claim 1, (Appendix 12) The reception unit is When receiving photo data, analyze the user's social media activity and receive related data. 2. The system of claim 1, (Appendix 13) The generation unit is Estimating a user's emotions and adjusting the method of generating a virtual space based on the estimated user's emotions 2. The system of claim 1, (Appendix 14) The generation unit is When generating a virtual space, the level of detail is adjusted based on the importance of the photo data. 2. The system of claim 1, (Appendix 15) The generation unit is When generating virtual space, different generation algorithms are applied depending on the category of photo data. 2. The system of claim 1, (Appendix 16) The generation unit is The user's emotions are estimated, and a priority order for the virtual space to be generated is determined based on the estimated user's emotions. 2. The system of claim 1, (Appendix 17) The generation unit is When generating virtual space, the priority of generation is determined based on the time of submission of photo data. 2. The system of claim 1, (Appendix 18) The generation unit is When generating a virtual space, the order of generation is adjusted based on the relevance of the photo data. 2. The system of claim 1, (Appendix 19) The imaging unit includes: Estimate the user's emotions and adjust the way the image is expressed based on the estimated user's emotions 2. The system of claim 1, (Appendix 20) The imaging unit includes: When shooting, adjust the level of detail in the shot based on key points in the virtual space 2. The system of claim 1, (Appendix 21) The imaging unit includes: When taking a photo, different shooting algorithms are applied depending on the category in the virtual space. 2. The system of claim 1, (Appendix 22) The imaging unit includes: Estimating a user's emotions and adjusting the length of shooting based on the estimated user's emotions 2. The system of claim 1, (Appendix 23) The imaging unit includes: When taking photos, prioritize shots based on specific locations in virtual space 2. The system of claim 1, (Appendix 24) The imaging unit includes: When taking photos, adjust the order of shots based on their relevance in the virtual space 2. The system of claim 1, (Appendix 25) The accuracy improvement unit is Estimate the user's emotions and adjust the accuracy improvement method based on the estimated user emotions. 3. The system of claim 2, (Appendix 26) The accuracy improvement unit is When improving accuracy, the past history of the photo data is analyzed to select the optimal improvement method. 3. The system of claim 2, (Appendix 27) The accuracy improvement unit is Estimate user emotions and determine priority for improving accuracy based on the estimated user emotions 3. The system of claim 2, (Appendix 28) The accuracy improvement unit is During refinement, the geographic location of the photo data is taken into account to select the optimal refinement method. 3. The system of claim 2, (Appendix 29) The moving unit is Estimating a user's emotions and adjusting the way of moving within a virtual space based on the estimated user's emotions 4. The system of claim 3. (Appendix 30) The moving unit is When moving, the system selects the optimal method of movement by referring to past movement history in the virtual space. 4. The system of claim 3. (Appendix 31) The moving unit is Estimate user's emotions and prioritize movements based on the estimated user's emotions 4. The system of claim 3. (Appendix 32) The moving unit is When moving, the optimal method of movement is selected taking into account geographical location information in the virtual space. 4. The system of claim 3. (Appendix 33) The viewpoint unit includes: Estimate the user's emotion and adjust the viewpoint change method based on the estimated user's emotion. 5. The system of claim 4. (Appendix 34) The viewpoint unit includes: When changing the viewpoint, the system selects the optimal method for changing the viewpoint by referring to the past viewpoint history in the virtual space. 5. The system of claim 4. (Appendix 35) The viewpoint unit includes: Estimate the user's emotions and prioritize viewpoint changes based on the estimated user emotions. 5. The system of claim 4. (Appendix 36) The viewpoint unit includes: When changing the viewpoint, the optimal method of changing the viewpoint is selected taking into account the geographical location information in the virtual space. 5. The system of claim 4. [Explanation of symbols]

[0189] 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 section for inputting photographic data; a generation unit that analyzes the photo data input by the reception unit and generates a virtual space; A photographing unit that takes pictures and videos in the virtual space generated by the generating unit. A system characterized by:

2. The generation unit is Equipped with an accuracy improvement unit that improves the accuracy of the virtual space based on the number of photos or the angle of view 2. The system of claim 1 .

3. The imaging unit includes: Equipped with a movement unit that controls movement within the virtual space 2. The system of claim 1 .

4. The imaging unit includes: Equipped with a viewpoint unit that controls changes in viewpoint within the virtual space 2. The system of claim 1 .

5. The generation unit is Generate virtual spaces of large areas such as tourist destinations or theme parks 2. The system of claim 1 .

6. The imaging unit includes: Allows users to move to a specific location in a virtual space and take a photo from that location's perspective 2. The system of claim 1 .

7. The reception unit is To estimate a user's emotion and adjust a timing of receiving photo data based on the estimated user's emotion.

2. The system of claim 1 .

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

9. The reception unit is Filtering photo data as it arrives based on the user's current projects and interests 2. The system of claim 1 .

Images