System

Abstract

An object of a system according to an embodiment is to generate a virtual environment by utilizing image or moving image data, and to enable a user to interact with a past space or person.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, an access unit, and an interaction unit. The reception unit receives an image or a moving image from a user. The analysis unit analyzes the image or the moving image received by the reception unit. The generation unit generates a virtual environment based on the data analyzed by the analysis unit. The access unit provides access for the user to access the virtual environment generated by the generation unit. The interaction unit manages an interaction in the virtual environment accessed by the access unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have the problem that they simply store image and video data, and do not adequately utilize it to generate virtual environments.

[0005] The system according to the embodiment aims to generate a virtual environment using image and video data, allowing users to interact with past spaces and people. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, an access unit, and an interaction unit. The reception unit receives images or videos from a user. The analysis unit analyzes the images or videos received by the reception unit. The generation unit generates a virtual environment based on the data analyzed by the analysis unit. The access unit provides access for the user to access the virtual environment generated by the generation unit. The interaction unit manages interactions within the virtual environment accessed by the access unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate a virtual environment using image and video data, allowing users to interact with spaces and people from the past. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention recreates past spaces using a cloud app for storing images and videos. In this system, users upload images and videos to the cloud app, and a generation AI analyzes the data to generate a virtual environment. The generated virtual environment is designed to allow users to travel back in time and meet beloved people, deceased pets, and family members. This allows the system to recreate the user's past memories and reunite with beloved people and pets. For example, a user can enter a virtual environment and relive memories of a family trip, allowing them to relive happy times from the past. Reuniting with deceased pets and family members can also provide emotional healing.

[0029] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, an access unit, and an interaction unit. The reception unit receives images or videos from a user. For example, the reception unit can receive images or videos in formats such as JPEG, PNG, MP4, and AVI. The analysis unit uses a generation AI to analyze the images or videos received by the reception unit. For example, the analysis unit uses an image recognition algorithm or video analysis technology to understand the content of the images or videos. The generation unit uses the generation AI to generate a virtual environment based on the data generated by the analysis unit. For example, the generation unit recreates a past space using 3D modeling or VR technology. The access unit provides an interface through which a user can access the virtual environment. For example, the access unit allows the user to access the virtual environment using a web browser or a VR headset. The interaction unit manages the user's actions and interactions within the virtual environment. For example, the interaction unit manages the user's actions and interactions within the virtual environment using user movement tracking or voice commands. As a result, the system according to the embodiment analyzes the user's images and videos, generates a virtual environment, and allows the user to access and interact with it.

[0030] The analysis unit can understand the content of an image or video and generate data for recreating a past space. The analysis unit can understand the content of the image using, for example, image recognition technology. For example, it can recognize people and objects in the image and analyze their positions and relationships. The analysis unit can also understand the content of a video using video analysis technology. For example, it can analyze movements in the video and capture changes over time. Furthermore, the analysis unit can analyze text data related to the image or video using natural language processing technology. For example, it can analyze descriptions and comments on the image or video to understand the content. This allows the analysis unit to understand the content of the image or video and generate data for recreating a past space.

[0031] The generation unit can generate a virtual environment based on the data generated by the analysis unit. The generation unit generates the virtual environment using, for example, 3D modeling technology. For example, based on the data generated by the analysis unit, a past space is reproduced as a 3D model. The generation unit can also generate the virtual environment using simulation technology. For example, past events or situations are simulated and reproduced in the virtual environment. Furthermore, the generation unit can also generate the virtual environment using VR technology. For example, a user can use a VR headset to move freely within the virtual environment. This allows the generation unit to generate the virtual environment based on the data generated by the analysis unit.

[0032] The access unit can provide an interface for a user to access the virtual environment. The access unit can provide an interface for accessing the virtual environment using, for example, a web browser. For example, the user can access the virtual environment through the web browser and experience a past space. The access unit can also provide an interface for accessing the virtual environment using a VR headset. For example, the user can wear the VR headset and move freely within the virtual environment. The access unit can also provide an interface for accessing the virtual environment using a voice interface. For example, the user can perform operations within the virtual environment using voice commands. In this way, the access unit can provide an interface for a user to access the virtual environment.

[0033] The interaction unit can manage the user's actions or interactions within the virtual environment. The interaction unit can manage the user's actions using, for example, motion tracking technology. For example, the interaction unit can track the user's hand or head movements and reflect the actions within the virtual environment. The interaction unit can also manage the user's interactions using voice commands. For example, the user can perform operations within the virtual environment using voice commands. Furthermore, the interaction unit can manage an interaction log and accumulate the user's past interaction data. For example, the interaction unit can record the operations and actions performed by the user in the past and use the data for future interactions. This allows the interaction unit to manage the user's actions and interactions within the virtual environment.

[0034] The reception unit can analyze the user's past upload history and select an appropriate reception method. The reception unit can, for example, use data mining technology to analyze the user's past upload history. For example, the reception unit can identify time periods in which the user frequently uploaded in the past and send a notification encouraging the user to upload during those time periods. The reception unit can also preferentially suggest upload methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can also encourage the user to upload images or videos related to specific events or anniversaries based on the user's past upload history. This allows the reception unit to analyze the user's past upload history and select an appropriate reception method.

[0035] When receiving images and videos, the reception unit can filter them based on the user's current project or area of ​​interest. The reception unit, for example, identifies the user's current project using data from a project management tool. For example, the reception unit preferentially receives only images and videos related to the user's ongoing project. The reception unit can also identify the user's area of ​​interest using the user's search history. For example, the reception unit can filter and receive related images and videos based on the user's area of ​​interest (travel, pets, etc.). Furthermore, if the user has expressed interest in a particular theme using keyword matching technology, the reception unit can preferentially receive images and videos related to that theme. This allows the reception unit to filter based on the user's current project or area of ​​interest.

[0036] When accepting images or videos, the acceptance unit can select the optimal acceptance means depending on the user's input method. The acceptance unit supports voice input using, for example, voice recognition technology. For example, when a user uses voice input, it supports uploading of images or videos using voice recognition technology. The acceptance unit can also use text input to enable the user to complete uploading of images or videos with simple text input. For example, when a user uses text input, it enables the user to complete uploading of images or videos with simple text input. Furthermore, the acceptance unit can also use image recognition technology to automatically select and accept related images or videos. For example, when a user uses images, it automatically selects and accepts related images or videos using image recognition technology. This allows the acceptance unit to select the optimal acceptance means depending on the user's input method.

[0037] When accepting images and videos, the acceptance unit can prioritize accepting highly relevant data in consideration of the user's geographical location information. The acceptance unit acquires the user's geographical location information using, for example, GPS data. For example, it prioritizes accepting images and videos related to the user's current location. The acceptance unit can also acquire the user's geographical location information using IP address location information. For example, it prioritizes accepting images and videos related to places the user has visited in the past. Furthermore, the acceptance unit can also prioritize accepting images and videos related to places the user plans to visit in the future. For example, if the user is planning a trip, it prioritizes accepting images and videos related to the travel destination. This allows the acceptance unit to prioritize accepting highly relevant data in consideration of the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity and receive related data when receiving images and videos. The reception unit, for example, analyzes the content of social media posts. For example, the reception unit automatically receives images and videos shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and receive related images and videos. For example, if the user posts about a specific event, the reception unit preferentially receives images and videos related to that event. Furthermore, the reception unit can receive related images and videos by referring to the activity of the user's friends on social media. For example, the reception unit receives related data based on images and videos shared by the user's friends. In this way, the reception unit can analyze the user's social media activity and receive related data.

[0039] The reception unit can customize the reception method by reflecting the user's past feedback when receiving images or videos. The reception unit acquires past feedback using, for example, user reviews or survey results. For example, the reception unit preferentially suggests reception methods that the user has preferred in the past. The reception unit can also provide an optimal reception procedure based on the user's past feedback. For example, the reception unit customizes the reception method to avoid reception methods that the user has been dissatisfied with in the past. Furthermore, the reception unit can customize the reception method based on the user's past behavioral patterns. For example, the reception unit preferentially suggests upload methods that the user has frequently used in the past. This allows the reception unit to customize the reception method by reflecting the user's past feedback.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the image or video. The analysis unit evaluates the importance of the image or video based on, for example, user ratings or data frequency. For example, the analysis unit performs a detailed analysis of images or videos related to important events. The analysis unit can also perform a concise analysis of images or videos related to everyday occurrences. Furthermore, the analysis unit can perform a detailed analysis of images or videos in which the user is particularly interested. For example, if the user is interested in a particular theme, the analysis unit performs a detailed analysis of images and videos related to that theme. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the image or video.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the image or video. For example, the analysis unit selects an analysis algorithm based on the image category or video category. For example, the analysis unit can apply a landscape recognition algorithm to images or videos related to travel. The analysis unit can also apply an animal recognition algorithm to images or videos related to pets. Furthermore, the analysis unit can apply a face recognition algorithm to images or videos related to family events. For example, a face recognition algorithm can be applied to family group photos or videos to identify individual people. This allows the analysis unit to apply different analysis algorithms depending on the category of the image or video.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit obtains past analysis results, for example, by using past analysis logs or user feedback. For example, the analysis unit adjusts the analysis algorithm based on analysis results that the user preferred in the past. The analysis unit can also improve the accuracy of the analysis based on the user's past feedback. For example, the analysis algorithm can be adjusted to avoid analysis results that the user was dissatisfied with in the past. Furthermore, the analysis unit can improve the accuracy of the analysis based on the user's past behavioral patterns. For example, the analysis unit preferentially applies analysis methods that the user frequently used in the past. This allows the analysis unit to improve the accuracy of the analysis by referring to the user's past analysis results.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time when the images and videos were taken. The analysis unit obtains the time when the images and videos were taken, for example, by analyzing metadata or using timestamps. For example, the analysis unit prioritizes analyzing recently taken images and videos. The analysis unit can also prioritize analyzing images and videos related to a particular event or anniversary. Furthermore, the analysis unit can prioritize analyzing images and videos taken during a period in which the user is particularly interested. For example, if the user is interested in a particular period, the analysis unit prioritizes analyzing images and videos taken during that period. This allows the analysis unit to determine the priority of analysis based on the time when the images and videos were taken.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of images and videos. The analysis unit evaluates the relevance of images and videos based on, for example, the similarity of content or the user's level of interest. For example, images and videos related to the same event are analyzed together. The analysis unit can also prioritize analysis of images and videos related to themes in which the user is particularly interested. Furthermore, the analysis unit can prioritize analysis of highly relevant images and videos based on the user's past analysis results. For example, images and videos related to themes that the user has previously liked are prioritized for analysis. This allows the analysis unit to adjust the order of analysis based on the relevance of images and videos.

[0045] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit evaluates the level of expertise based on, for example, the user's occupation or past learning history. For example, if the user has technical expertise, the analysis unit provides analysis results using detailed technical terms. Also, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the use of optimal technical terms based on the user's past feedback. For example, technical terms that the user has preferred in the past are used preferentially. This allows the analysis unit to adjust the use of technical terms in the analysis according to the user's level of expertise.

[0046] The generation unit can adjust the level of detail of the virtual environment based on the importance of the analyzed data during generation. The generation unit evaluates the importance of the analyzed data based on, for example, user ratings or the frequency of the data. For example, a virtual environment related to an important event is reproduced in detail. The generation unit can also reproduce a virtual environment related to everyday occurrences in a concise manner. Furthermore, the generation unit can reproduce a virtual environment in which the user is particularly interested in in detail. For example, if the user is interested in a particular theme, the generation unit reproduces a virtual environment related to that theme in detail. This allows the generation unit to adjust the level of detail of the virtual environment based on the importance of the analyzed data.

[0047] The generation unit can apply different generation algorithms depending on the category of the virtual environment during generation. For example, the generation unit selects a generation algorithm based on the category of the virtual environment. For example, a landscape reproduction algorithm can be applied to a travel-related virtual environment. The generation unit can also apply an animal reproduction algorithm to a pet-related virtual environment. Furthermore, the generation unit can apply a person reproduction algorithm to a virtual environment related to a family event. For example, a person reproduction algorithm can be applied to a family group photo or video to identify each person. This allows the generation unit to apply different generation algorithms depending on the category of the virtual environment.

[0048] During generation, the generation unit can improve the accuracy of the virtual environment by referring to the user's past generation results. The generation unit acquires past generation results, for example, by using past generation logs or user feedback. For example, the generation unit adjusts the generation algorithm based on virtual environments that the user previously preferred. The generation unit can also improve the accuracy of the virtual environment based on the user's past feedback. For example, the generation algorithm can be adjusted to avoid virtual environments that the user previously dissatisfied with. Furthermore, the generation unit can improve the accuracy of the virtual environment based on the user's past behavioral patterns. For example, the generation unit preferentially applies generation methods that the user frequently used in the past. This allows the generation unit to improve the accuracy of the virtual environment by referring to the user's past generation results.

[0049] During generation, the generation unit may prioritize virtual environments based on the time at which the analyzed data was captured. The generation unit may obtain the time at which the analyzed data was captured, for example, by analyzing metadata or using a timestamp. For example, the generation unit may prioritize generating virtual environments based on recently captured data. The generation unit may also prioritize generating virtual environments based on data related to a particular event or anniversary. Furthermore, the generation unit may prioritize generating virtual environments based on data captured during a period in which the user is particularly interested. For example, if the user is interested in a particular period, the generation unit may prioritize generating virtual environments based on data captured during that period. This allows the generation unit to prioritize virtual environments based on the time at which the analyzed data was captured.

[0050] The generation unit can adjust the order of the virtual environments based on the relevance of the analyzed data during generation. The generation unit evaluates the relevance of the analyzed data based on, for example, the similarity of the content or the user's interest. For example, the generation unit generates a virtual environment by grouping data related to the same event. The generation unit can also preferentially incorporate data related to a theme in which the user is particularly interested into the virtual environment. Furthermore, the generation unit can preferentially incorporate highly relevant data into the virtual environment based on the user's past generation results. For example, data related to a theme that the user has previously liked is preferentially incorporated into the virtual environment. This allows the generation unit to adjust the order of the virtual environments based on the relevance of the analyzed data.

[0051] The generation unit can adjust the use of technical terminology in the virtual environment according to the user's level of expertise during generation. The generation unit evaluates the level of expertise based on, for example, the user's occupation or past learning history. For example, if the user has technical expertise, the generation unit generates the virtual environment using detailed technical terminology. Alternatively, if the user does not have technical expertise, the generation unit can generate the virtual environment using simple language. Furthermore, the generation unit can adjust the use of optimal technical terminology based on the user's past feedback. For example, the generation unit can prioritize the use of technical terminology that the user has previously preferred. This allows the generation unit to adjust the use of technical terminology in the virtual environment according to the user's level of expertise.

[0052] The access unit can adjust the level of detail of access based on the importance of the virtual environment at the time of access. The access unit evaluates the importance of the virtual environment based on, for example, a user's rating or the frequency of data. For example, a virtual environment related to an important event provides detailed access information. The access unit can also provide concise access information for a virtual environment related to everyday occurrences. Furthermore, the access unit can provide detailed access information for a virtual environment in which the user is particularly interested. For example, if the user shows interest in a particular theme, the access unit accesses virtual environments related to that theme in detail. This allows the access unit to adjust the level of detail of access based on the importance of the virtual environment.

[0053] The access unit can apply different access algorithms depending on the category of the virtual environment when accessing the virtual environment. For example, the access unit selects an access algorithm based on the category of the virtual environment. For example, a landscape reproduction algorithm is applied to a virtual environment related to travel. The access unit can also apply an animal reproduction algorithm to a virtual environment related to pets. Furthermore, the access unit can apply a person reproduction algorithm to a virtual environment related to a family event. For example, a person reproduction algorithm is applied to a family group photo or video to identify each person. This allows the access unit to apply different access algorithms depending on the category of the virtual environment.

[0054] When accessing, the access unit can improve the accuracy of access by referring to the user's past access results. The access unit obtains past access results, for example, by using past access logs or user feedback. For example, the access unit adjusts the access algorithm based on the user's preferred access methods in the past. The access unit can also improve the accuracy of access based on the user's past feedback. For example, the access algorithm is adjusted to avoid access methods that the user has been dissatisfied with in the past. Furthermore, the access unit can improve the accuracy of access based on the user's past behavioral patterns. For example, the access unit preferentially applies access methods that the user has frequently used in the past. This allows the access unit to improve the accuracy of access by referring to the user's past access results.

[0055] At the time of access, the access unit can determine the priority of access based on the time of creation of the virtual environment. The access unit obtains the time of creation of the virtual environment, for example, by analyzing metadata or using a timestamp. For example, the access unit can preferentially access a recently created virtual environment. The access unit can also preferentially access a virtual environment related to a particular event or anniversary. Furthermore, the access unit can preferentially access a virtual environment created during a period in which the user is particularly interested. For example, if the user shows interest in a particular period, the access unit preferentially accesses a virtual environment created during that period. This allows the access unit to determine the priority of access based on the time of creation of the virtual environment.

[0056] The access unit can adjust the order of access based on the relevance of the virtual environments at the time of access. The access unit evaluates the relevance of the virtual environments based on, for example, the similarity of the content or the user's interest. For example, virtual environments related to the same event are accessed together. The access unit can also preferentially access virtual environments related to a theme in which the user is particularly interested. Furthermore, the access unit can preferentially access highly relevant virtual environments based on the user's past access results. For example, virtual environments related to a theme that the user has previously liked are preferentially accessed. This allows the access unit to adjust the order of access based on the relevance of the virtual environments.

[0057] The access unit can adjust the use of technical terms in the access depending on the user's level of expertise at the time of access. The access unit evaluates the level of expertise based on, for example, the user's occupation or past learning history. For example, if the user has technical expertise, the access unit provides the access information using detailed technical terms. Also, if the user does not have technical expertise, the access unit can provide the access information in simple language. Furthermore, the access unit can adjust the use of optimal technical terms based on the user's past feedback. For example, the access unit preferentially uses technical terms that the user has previously preferred. This allows the access unit to adjust the use of technical terms in the access depending on the user's level of expertise.

[0058] The interaction unit can adjust the level of interaction detail based on the importance of the virtual environment during interaction. The interaction unit can evaluate the importance of the virtual environment based on, for example, a user's rating or the frequency of data. For example, a virtual environment related to an important event can provide detailed interaction information. The interaction unit can also provide concise interaction information for a virtual environment related to everyday events. Furthermore, the interaction unit can provide detailed interaction information for a virtual environment in which the user is particularly interested. For example, if the user is interested in a particular theme, the interaction unit can interact in detail with a virtual environment related to that theme. This allows the interaction unit to adjust the level of interaction detail based on the importance of the virtual environment.

[0059] The interaction unit can apply different interaction algorithms depending on the category of the virtual environment during interaction. For example, the interaction unit selects an interaction algorithm based on the category of the virtual environment. For example, a landscape reproduction algorithm can be applied to a travel-related virtual environment. The interaction unit can also apply an animal reproduction algorithm to a pet-related virtual environment. Furthermore, the interaction unit can apply a person reproduction algorithm to a virtual environment related to a family event. For example, a person reproduction algorithm can be applied to a family group photo or video to identify each person. This allows the interaction unit to apply different interaction algorithms depending on the category of the virtual environment.

[0060] The interaction unit can improve the accuracy of interaction during interaction by referring to the user's past interaction results. The interaction unit acquires past interaction results, for example, by using past interaction logs or user feedback. For example, the interaction unit adjusts the interaction algorithm based on interaction methods that the user has previously preferred. The interaction unit can also improve the accuracy of interaction based on the user's past feedback. For example, the interaction algorithm can be adjusted to avoid interaction methods that the user has previously dissatisfied with. Furthermore, the interaction unit can improve the accuracy of interaction based on the user's past behavioral patterns. For example, the interaction unit preferentially applies interaction methods that the user has frequently used in the past. In this way, the interaction unit can improve the accuracy of interaction by referring to the user's past interaction results.

[0061] During interaction, the interaction unit can determine an interaction priority based on the time when the virtual environment was created. The interaction unit obtains the time when the virtual environment was created, for example, by analyzing metadata or using a timestamp. For example, the interaction unit prioritizes interaction with a recently created virtual environment. The interaction unit can also prioritize interaction with a virtual environment related to a particular event or anniversary. Furthermore, the interaction unit can prioritize interaction with a virtual environment created during a period in which the user is particularly interested. For example, if the user is interested in a particular period, the interaction unit prioritizes interaction with a virtual environment created during that period. This allows the interaction unit to determine an interaction priority based on the time when the virtual environment was created.

[0062] The interaction unit can adjust the order of interactions based on the relevance of the virtual environments during interaction. The interaction unit evaluates the relevance of the virtual environments based on, for example, the similarity of their contents or the user's interest. For example, the interaction unit collectively interacts with virtual environments related to the same event. The interaction unit can also prioritize interaction with virtual environments related to a theme in which the user is particularly interested. Furthermore, the interaction unit can prioritize interaction with highly relevant virtual environments based on the user's past interaction results. For example, the interaction unit prioritizes interaction with virtual environments related to a theme that the user has previously liked. This allows the interaction unit to adjust the order of interactions based on the relevance of the virtual environments.

[0063] The interaction unit can adjust the use of technical terms in the interaction according to the user's level of expertise during the interaction. The interaction unit evaluates the level of expertise based on, for example, the user's occupation or past learning history. For example, if the user has technical expertise, the interaction unit can provide interaction information using detailed technical terms. Also, if the user does not have technical expertise, the interaction unit can provide interaction information in simple language. Furthermore, the interaction unit can adjust the use of optimal technical terms based on the user's past feedback. For example, technical terms that the user has previously preferred can be used preferentially. This allows the interaction unit to adjust the use of technical terms in the interaction according to the user's level of expertise.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The reception unit can analyze the user's past upload history and select an appropriate reception method. For example, it can use data mining technology to analyze the user's past upload history, identify time periods in which the user frequently uploaded in the past, and send a notification encouraging the user to upload during those time periods. It can also prioritize upload methods (voice, text, etc.) that the user has used in the past. It can also encourage the user to upload images and videos related to specific events or anniversaries based on the user's past upload history. This allows the reception unit to analyze the user's past upload history and select an appropriate reception method.

[0066] When accepting images and videos, the acceptance unit can prioritize accepting highly relevant data in consideration of the user's geographical location information. For example, the acceptance unit can acquire the user's geographical location information using GPS data and prioritize accepting images and videos related to the user's current location. The acceptance unit can also acquire the user's geographical location information using IP address location information and prioritize accepting images and videos related to places the user has visited in the past. Furthermore, the acceptance unit can prioritize accepting images and videos related to places the user plans to visit in the future. For example, if the user is planning a trip, the acceptance unit can prioritize accepting images and videos related to the travel destination. This allows the acceptance unit to prioritize accepting highly relevant data in consideration of the user's geographical location information.

[0067] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the image or video. For example, the importance of the image or video can be evaluated based on user ratings or data frequency, and images or videos related to important events can be analyzed in detail. Images or videos related to everyday events can also be analyzed briefly. Furthermore, images or videos in which the user is particularly interested can be analyzed in detail. For example, if the user is interested in a particular theme, images and videos related to that theme can be analyzed in detail. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the image or video.

[0068] The generator can adjust the level of detail of the virtual environment based on the importance of the analyzed data during generation. For example, the generator can evaluate the importance of the analyzed data based on the user's ratings and the frequency of the data, and a virtual environment related to an important event can be reproduced in detail. A virtual environment related to everyday occurrences can be reproduced briefly. Furthermore, a virtual environment in which the user is particularly interested can be reproduced in detail. For example, if the user is interested in a particular theme, a virtual environment related to that theme can be reproduced in detail. This allows the generator to adjust the level of detail of the virtual environment based on the importance of the analyzed data.

[0069] The access unit can adjust the level of detail of access based on the importance of the virtual environment at the time of access. For example, the importance of a virtual environment can be evaluated based on a user's rating or the frequency of data, and detailed access information can be provided for a virtual environment related to an important event. Also, concise access information can be provided for a virtual environment related to everyday events. Furthermore, detailed access information can be provided for a virtual environment in which the user is particularly interested. For example, if a user shows interest in a particular theme, virtual environments related to that theme can be accessed in detail. This allows the access unit to adjust the level of detail of access based on the importance of the virtual environment.

[0070] The interaction unit can adjust the level of detail of the interaction based on the importance of the virtual environment during interaction. For example, the importance of the virtual environment can be evaluated based on the user's ratings and the frequency of data, and a virtual environment related to an important event can provide detailed interaction information. A virtual environment related to everyday events can also provide concise interaction information. Furthermore, a virtual environment in which the user is particularly interested can provide detailed interaction information. For example, if the user is interested in a particular theme, a virtual environment related to that theme can be interacted with in detail. This allows the interaction unit to adjust the level of detail of the interaction based on the importance of the virtual environment.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The reception unit receives images or videos from a user. For example, images and videos in formats such as JPEG, PNG, MP4, and AVI can be received. Step 2: The analysis unit uses the generation AI to analyze the image or video received by the reception unit. For example, it uses image recognition algorithms or video analysis technology to understand the content of the image or video. Step 3: The generator uses AI to generate a virtual environment based on the data generated by the analyzer. For example, it uses 3D modeling and VR technology to recreate a past space. Step 4: The access unit provides an interface for the user to access the virtual environment. For example, the user can access the virtual environment using a web browser or a VR headset. Step 5: The interaction unit manages the user's actions and interactions within the virtual environment, for example, by tracking the user's actions and using voice commands.

[0073] (Example 2) A system according to an embodiment of the present invention recreates past spaces using a cloud app for storing images and videos. In this system, users upload images and videos to the cloud app, and a generation AI analyzes the data to generate a virtual environment. The generated virtual environment is designed to allow users to travel back in time and meet beloved people, deceased pets, and family members. This allows the system to recreate the user's past memories and reunite with beloved people and pets. For example, a user can enter a virtual environment and relive memories of a family trip, allowing them to relive happy times from the past. Reuniting with deceased pets and family members can also provide emotional healing.

[0074] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, an access unit, and an interaction unit. The reception unit receives images or videos from a user. For example, the reception unit can receive images or videos in formats such as JPEG, PNG, MP4, and AVI. The analysis unit uses a generation AI to analyze the images or videos received by the reception unit. For example, the analysis unit uses an image recognition algorithm or video analysis technology to understand the content of the images or videos. The generation unit uses the generation AI to generate a virtual environment based on the data generated by the analysis unit. For example, the generation unit recreates a past space using 3D modeling or VR technology. The access unit provides an interface through which a user can access the virtual environment. For example, the access unit allows the user to access the virtual environment using a web browser or a VR headset. The interaction unit manages the user's actions and interactions within the virtual environment. For example, the interaction unit manages the user's actions and interactions within the virtual environment using user movement tracking or voice commands. As a result, the system according to the embodiment analyzes the user's images and videos, generates a virtual environment, and allows the user to access and interact with it.

[0075] The analysis unit can understand the content of an image or video and generate data for recreating a past space. The analysis unit can understand the content of the image using, for example, image recognition technology. For example, it can recognize people and objects in the image and analyze their positions and relationships. The analysis unit can also understand the content of a video using video analysis technology. For example, it can analyze movements in the video and capture changes over time. Furthermore, the analysis unit can analyze text data related to the image or video using natural language processing technology. For example, it can analyze descriptions and comments on the image or video to understand the content. This allows the analysis unit to understand the content of the image or video and generate data for recreating a past space.

[0076] The generation unit can generate a virtual environment based on the data generated by the analysis unit. The generation unit generates the virtual environment using, for example, 3D modeling technology. For example, based on the data generated by the analysis unit, a past space is reproduced as a 3D model. The generation unit can also generate the virtual environment using simulation technology. For example, past events or situations are simulated and reproduced in the virtual environment. Furthermore, the generation unit can also generate the virtual environment using VR technology. For example, a user can use a VR headset to move freely within the virtual environment. This allows the generation unit to generate the virtual environment based on the data generated by the analysis unit.

[0077] The access unit can provide an interface for a user to access the virtual environment. The access unit can provide an interface for accessing the virtual environment using, for example, a web browser. For example, the user can access the virtual environment through the web browser and experience a past space. The access unit can also provide an interface for accessing the virtual environment using a VR headset. For example, the user can wear the VR headset and move freely within the virtual environment. The access unit can also provide an interface for accessing the virtual environment using a voice interface. For example, the user can perform operations within the virtual environment using voice commands. In this way, the access unit can provide an interface for a user to access the virtual environment.

[0078] The interaction unit can manage the user's actions or interactions within the virtual environment. The interaction unit can manage the user's actions using, for example, motion tracking technology. For example, the interaction unit can track the user's hand or head movements and reflect the actions within the virtual environment. The interaction unit can also manage the user's interactions using voice commands. For example, the user can perform operations within the virtual environment using voice commands. Furthermore, the interaction unit can manage an interaction log and accumulate the user's past interaction data. For example, the interaction unit can record the operations and actions performed by the user in the past and use the data for future interactions. This allows the interaction unit to manage the user's actions and interactions within the virtual environment.

[0079] The reception unit can estimate the user's emotions and adjust the timing of receiving images or videos based on the estimated user emotions. The reception unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the reception unit can adjust the timing of receiving images or videos based on the user's emotions using real-time analysis. For example, the reception unit can encourage the user to upload images or videos during times when the user is relaxed. This allows the reception unit to adjust the timing of receiving images or videos based on the user's emotions.

[0080] The reception unit can analyze the user's past upload history and select an appropriate reception method. The reception unit can, for example, use data mining technology to analyze the user's past upload history. For example, the reception unit can identify time periods in which the user frequently uploaded in the past and send a notification encouraging the user to upload during those time periods. The reception unit can also preferentially suggest upload methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can also encourage the user to upload images or videos related to specific events or anniversaries based on the user's past upload history. This allows the reception unit to analyze the user's past upload history and select an appropriate reception method.

[0081] When receiving images and videos, the reception unit can filter them based on the user's current project or area of ​​interest. The reception unit, for example, identifies the user's current project using data from a project management tool. For example, the reception unit preferentially receives only images and videos related to the user's ongoing project. The reception unit can also identify the user's area of ​​interest using the user's search history. For example, the reception unit can filter and receive related images and videos based on the user's area of ​​interest (travel, pets, etc.). Furthermore, if the user has expressed interest in a particular theme using keyword matching technology, the reception unit can preferentially receive images and videos related to that theme. This allows the reception unit to filter based on the user's current project or area of ​​interest.

[0082] When accepting images or videos, the acceptance unit can select the optimal acceptance means depending on the user's input method. The acceptance unit supports voice input using, for example, voice recognition technology. For example, when a user uses voice input, it supports uploading of images or videos using voice recognition technology. The acceptance unit can also use text input to enable the user to complete uploading of images or videos with simple text input. For example, when a user uses text input, it enables the user to complete uploading of images or videos with simple text input. Furthermore, the acceptance unit can also use image recognition technology to automatically select and accept related images or videos. For example, when a user uses images, it automatically selects and accepts related images or videos using image recognition technology. This allows the acceptance unit to select the optimal acceptance means depending on the user's input method.

[0083] The reception unit can estimate the user's emotions and determine the priority of images and videos to be received based on the estimated user's emotions. The reception unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the reception unit can determine the priority of images and videos to be received based on the user's emotions using real-time analysis. For example, if the user is feeling depressed, the reception unit can preferentially receive images and videos that have a soothing effect. This allows the reception unit to determine the priority of images and videos to be received based on the user's emotions.

[0084] When accepting images and videos, the acceptance unit can prioritize accepting highly relevant data in consideration of the user's geographical location information. The acceptance unit acquires the user's geographical location information using, for example, GPS data. For example, it prioritizes accepting images and videos related to the user's current location. The acceptance unit can also acquire the user's geographical location information using IP address location information. For example, it prioritizes accepting images and videos related to places the user has visited in the past. Furthermore, the acceptance unit can also prioritize accepting images and videos related to places the user plans to visit in the future. For example, if the user is planning a trip, it prioritizes accepting images and videos related to the travel destination. This allows the acceptance unit to prioritize accepting highly relevant data in consideration of the user's geographical location information.

[0085] The reception unit can analyze the user's social media activity and receive related data when receiving images and videos. The reception unit, for example, analyzes the content of social media posts. For example, the reception unit automatically receives images and videos shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and receive related images and videos. For example, if the user posts about a specific event, the reception unit preferentially receives images and videos related to that event. Furthermore, the reception unit can receive related images and videos by referring to the activity of the user's friends on social media. For example, the reception unit receives related data based on images and videos shared by the user's friends. In this way, the reception unit can analyze the user's social media activity and receive related data.

[0086] The reception unit can customize the reception method by reflecting the user's past feedback when receiving images or videos. The reception unit acquires past feedback using, for example, user reviews or survey results. For example, the reception unit preferentially suggests reception methods that the user has preferred in the past. The reception unit can also provide an optimal reception procedure based on the user's past feedback. For example, the reception unit customizes the reception method to avoid reception methods that the user has been dissatisfied with in the past. Furthermore, the reception unit can customize the reception method based on the user's past behavioral patterns. For example, the reception unit preferentially suggests upload methods that the user has frequently used in the past. This allows the reception unit to customize the reception method by reflecting the user's past feedback.

[0087] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the analysis unit can adjust the way the analysis is presented based on the user's emotions. For example, if the user is relaxed, the analysis unit provides analysis results that progress at a leisurely pace. This allows the analysis unit to adjust the way the analysis is presented based on the user's emotions.

[0088] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the image or video. The analysis unit evaluates the importance of the image or video based on, for example, user ratings or data frequency. For example, the analysis unit performs a detailed analysis of images or videos related to important events. The analysis unit can also perform a concise analysis of images or videos related to everyday occurrences. Furthermore, the analysis unit can perform a detailed analysis of images or videos in which the user is particularly interested. For example, if the user is interested in a particular theme, the analysis unit performs a detailed analysis of images and videos related to that theme. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the image or video.

[0089] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the image or video. For example, the analysis unit selects an analysis algorithm based on the image category or video category. For example, the analysis unit can apply a landscape recognition algorithm to images or videos related to travel. The analysis unit can also apply an animal recognition algorithm to images or videos related to pets. Furthermore, the analysis unit can apply a face recognition algorithm to images or videos related to family events. For example, a face recognition algorithm can be applied to family group photos or videos to identify individual people. This allows the analysis unit to apply different analysis algorithms depending on the category of the image or video.

[0090] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit obtains past analysis results, for example, by using past analysis logs or user feedback. For example, the analysis unit adjusts the analysis algorithm based on analysis results that the user preferred in the past. The analysis unit can also improve the accuracy of the analysis based on the user's past feedback. For example, the analysis algorithm can be adjusted to avoid analysis results that the user was dissatisfied with in the past. Furthermore, the analysis unit can improve the accuracy of the analysis based on the user's past behavioral patterns. For example, the analysis unit preferentially applies analysis methods that the user frequently used in the past. This allows the analysis unit to improve the accuracy of the analysis by referring to the user's past analysis results.

[0091] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. The analysis unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the analysis unit can adjust the length of the analysis based on the user's emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. This allows the analysis unit to adjust the length of the analysis based on the user's emotions.

[0092] During analysis, the analysis unit can determine the priority of analysis based on the time when the images and videos were taken. The analysis unit obtains the time when the images and videos were taken, for example, by analyzing metadata or using timestamps. For example, the analysis unit prioritizes analyzing recently taken images and videos. The analysis unit can also prioritize analyzing images and videos related to a particular event or anniversary. Furthermore, the analysis unit can prioritize analyzing images and videos taken during a period in which the user is particularly interested. For example, if the user is interested in a particular period, the analysis unit prioritizes analyzing images and videos taken during that period. This allows the analysis unit to determine the priority of analysis based on the time when the images and videos were taken.

[0093] During analysis, the analysis unit can adjust the order of analysis based on the relevance of images and videos. The analysis unit evaluates the relevance of images and videos based on, for example, the similarity of content or the user's level of interest. For example, images and videos related to the same event are analyzed together. The analysis unit can also prioritize analysis of images and videos related to themes in which the user is particularly interested. Furthermore, the analysis unit can prioritize analysis of highly relevant images and videos based on the user's past analysis results. For example, images and videos related to themes that the user has previously liked are prioritized for analysis. This allows the analysis unit to adjust the order of analysis based on the relevance of images and videos.

[0094] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit evaluates the level of expertise based on, for example, the user's occupation or past learning history. For example, if the user has technical expertise, the analysis unit provides analysis results using detailed technical terms. Also, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the use of optimal technical terms based on the user's past feedback. For example, technical terms that the user has preferred in the past are used preferentially. This allows the analysis unit to adjust the use of technical terms in the analysis according to the user's level of expertise.

[0095] The generation unit can estimate the user's emotion and adjust the method for generating the virtual environment based on the estimated user's emotion. The generation unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the user's facial expression can be captured with a camera and the emotion can be estimated using an emotion estimation algorithm. The generation unit can also estimate the user's emotion using voice analysis technology. For example, the tone and speed of the user's voice can be analyzed to estimate the emotion. Furthermore, the generation unit can adjust the method for generating the virtual environment based on the user's emotion. For example, if the user is relaxed, the generation unit generates a virtual environment that progresses at a leisurely pace. This allows the generation unit to adjust the method for generating the virtual environment based on the user's emotion.

[0096] The generation unit can adjust the level of detail of the virtual environment based on the importance of the analyzed data during generation. The generation unit evaluates the importance of the analyzed data based on, for example, user ratings or the frequency of the data. For example, a virtual environment related to an important event is reproduced in detail. The generation unit can also reproduce a virtual environment related to everyday occurrences in a concise manner. Furthermore, the generation unit can reproduce a virtual environment in which the user is particularly interested in in detail. For example, if the user is interested in a particular theme, the generation unit reproduces a virtual environment related to that theme in detail. This allows the generation unit to adjust the level of detail of the virtual environment based on the importance of the analyzed data.

[0097] The generation unit can apply different generation algorithms depending on the category of the virtual environment during generation. For example, the generation unit selects a generation algorithm based on the category of the virtual environment. For example, a landscape reproduction algorithm can be applied to a travel-related virtual environment. The generation unit can also apply an animal reproduction algorithm to a pet-related virtual environment. Furthermore, the generation unit can apply a person reproduction algorithm to a virtual environment related to a family event. For example, a person reproduction algorithm can be applied to a family group photo or video to identify each person. This allows the generation unit to apply different generation algorithms depending on the category of the virtual environment.

[0098] During generation, the generation unit can improve the accuracy of the virtual environment by referring to the user's past generation results. The generation unit acquires past generation results, for example, by using past generation logs or user feedback. For example, the generation unit adjusts the generation algorithm based on virtual environments that the user previously preferred. The generation unit can also improve the accuracy of the virtual environment based on the user's past feedback. For example, the generation algorithm can be adjusted to avoid virtual environments that the user previously dissatisfied with. Furthermore, the generation unit can improve the accuracy of the virtual environment based on the user's past behavioral patterns. For example, the generation unit preferentially applies generation methods that the user frequently used in the past. This allows the generation unit to improve the accuracy of the virtual environment by referring to the user's past generation results.

[0099] The generation unit can estimate the user's emotion and adjust the length of the virtual environment based on the estimated user's emotion. The generation unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the user's facial expression can be captured with a camera and the emotion can be estimated using an emotion estimation algorithm. The generation unit can also estimate the user's emotion using voice analysis technology. For example, the emotion can be estimated by analyzing the tone and speed of the user's voice. Furthermore, the generation unit can adjust the length of the virtual environment based on the user's emotion. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point virtual environment. This allows the generation unit to adjust the length of the virtual environment based on the user's emotion.

[0100] During generation, the generation unit may prioritize virtual environments based on the time at which the analyzed data was captured. The generation unit may obtain the time at which the analyzed data was captured, for example, by analyzing metadata or using a timestamp. For example, the generation unit may prioritize generating virtual environments based on recently captured data. The generation unit may also prioritize generating virtual environments based on data related to a particular event or anniversary. Furthermore, the generation unit may prioritize generating virtual environments based on data captured during a period in which the user is particularly interested. For example, if the user is interested in a particular period, the generation unit may prioritize generating virtual environments based on data captured during that period. This allows the generation unit to prioritize virtual environments based on the time at which the analyzed data was captured.

[0101] The generation unit can adjust the order of the virtual environments based on the relevance of the analyzed data during generation. The generation unit evaluates the relevance of the analyzed data based on, for example, the similarity of the content or the user's interest. For example, the generation unit generates a virtual environment by grouping data related to the same event. The generation unit can also preferentially incorporate data related to a theme in which the user is particularly interested into the virtual environment. Furthermore, the generation unit can preferentially incorporate highly relevant data into the virtual environment based on the user's past generation results. For example, data related to a theme that the user has previously liked is preferentially incorporated into the virtual environment. This allows the generation unit to adjust the order of the virtual environments based on the relevance of the analyzed data.

[0102] The generation unit can adjust the use of technical terminology in the virtual environment according to the user's level of expertise during generation. The generation unit evaluates the level of expertise based on, for example, the user's occupation or past learning history. For example, if the user has technical expertise, the generation unit generates the virtual environment using detailed technical terminology. Alternatively, if the user does not have technical expertise, the generation unit can generate the virtual environment using simple language. Furthermore, the generation unit can adjust the use of optimal technical terminology based on the user's past feedback. For example, the generation unit can prioritize the use of technical terminology that the user has previously preferred. This allows the generation unit to adjust the use of technical terminology in the virtual environment according to the user's level of expertise.

[0103] The access unit can estimate the user's emotions and adjust the manner in which the access is expressed based on the estimated user's emotions. The access unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the user's facial expressions can be captured with a camera and the emotion can be estimated using an emotion estimation algorithm. The access unit can also estimate the user's emotions using voice analysis technology. For example, the user's emotions can be estimated by analyzing the tone and speed of the user's voice. Furthermore, the access unit can adjust the manner in which the access is expressed based on the user's emotions. For example, if the user is nervous, an interface with calming colors can be provided to reduce visual stress. This allows the access unit to adjust the manner in which the access is expressed based on the user's emotions.

[0104] The access unit can adjust the level of detail of access based on the importance of the virtual environment at the time of access. The access unit evaluates the importance of the virtual environment based on, for example, a user's rating or the frequency of data. For example, a virtual environment related to an important event provides detailed access information. The access unit can also provide concise access information for a virtual environment related to everyday occurrences. Furthermore, the access unit can provide detailed access information for a virtual environment in which the user is particularly interested. For example, if the user shows interest in a particular theme, the access unit accesses virtual environments related to that theme in detail. This allows the access unit to adjust the level of detail of access based on the importance of the virtual environment.

[0105] The access unit can apply different access algorithms depending on the category of the virtual environment when accessing the virtual environment. For example, the access unit selects an access algorithm based on the category of the virtual environment. For example, a landscape reproduction algorithm is applied to a virtual environment related to travel. The access unit can also apply an animal reproduction algorithm to a virtual environment related to pets. Furthermore, the access unit can apply a person reproduction algorithm to a virtual environment related to a family event. For example, a person reproduction algorithm is applied to a family group photo or video to identify each person. This allows the access unit to apply different access algorithms depending on the category of the virtual environment.

[0106] When accessing, the access unit can improve the accuracy of access by referring to the user's past access results. The access unit obtains past access results, for example, by using past access logs or user feedback. For example, the access unit adjusts the access algorithm based on the user's preferred access methods in the past. The access unit can also improve the accuracy of access based on the user's past feedback. For example, the access algorithm is adjusted to avoid access methods that the user has been dissatisfied with in the past. Furthermore, the access unit can improve the accuracy of access based on the user's past behavioral patterns. For example, the access unit preferentially applies access methods that the user has frequently used in the past. This allows the access unit to improve the accuracy of access by referring to the user's past access results.

[0107] The access unit can estimate the user's emotions and adjust the length of the access based on the estimated user's emotions. The access unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the user's facial expressions can be captured with a camera and the emotion can be estimated using an emotion estimation algorithm. The access unit can also estimate the user's emotions using voice analysis technology. For example, the user's emotions can be estimated by analyzing the tone and speed of the user's voice. Furthermore, the access unit can adjust the length of the access based on the user's emotions. For example, if the user is in a hurry, the access unit can provide short, to-the-point access information. This allows the access unit to adjust the length of the access based on the user's emotions.

[0108] At the time of access, the access unit can determine the priority of access based on the time of creation of the virtual environment. The access unit obtains the time of creation of the virtual environment, for example, by analyzing metadata or using a timestamp. For example, the access unit can preferentially access a recently created virtual environment. The access unit can also preferentially access a virtual environment related to a particular event or anniversary. Furthermore, the access unit can preferentially access a virtual environment created during a period in which the user is particularly interested. For example, if the user shows interest in a particular period, the access unit preferentially accesses a virtual environment created during that period. This allows the access unit to determine the priority of access based on the time of creation of the virtual environment.

[0109] The access unit can adjust the order of access based on the relevance of the virtual environments at the time of access. The access unit evaluates the relevance of the virtual environments based on, for example, the similarity of the content or the user's interest. For example, virtual environments related to the same event are accessed together. The access unit can also preferentially access virtual environments related to a theme in which the user is particularly interested. Furthermore, the access unit can preferentially access highly relevant virtual environments based on the user's past access results. For example, virtual environments related to a theme that the user has previously liked are preferentially accessed. This allows the access unit to adjust the order of access based on the relevance of the virtual environments.

[0110] The access unit can adjust the use of technical terms in the access depending on the user's level of expertise at the time of access. The access unit evaluates the level of expertise based on, for example, the user's occupation or past learning history. For example, if the user has technical expertise, the access unit provides the access information using detailed technical terms. Also, if the user does not have technical expertise, the access unit can provide the access information in simple language. Furthermore, the access unit can adjust the use of optimal technical terms based on the user's past feedback. For example, the access unit preferentially uses technical terms that the user has previously preferred. This allows the access unit to adjust the use of technical terms in the access depending on the user's level of expertise.

[0111] The interaction unit can estimate the user's emotion and adjust the interaction expression method based on the estimated user's emotion. The interaction unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the user's facial expression can be captured with a camera and the emotion can be estimated using an emotion estimation algorithm. The interaction unit can also estimate the user's emotion using voice analysis technology. For example, the emotion can be estimated by analyzing the tone and speed of the user's voice. Furthermore, the interaction unit can adjust the interaction expression method based on the user's emotion. For example, if the user is nervous, an interface with calm colors can be provided to reduce visual stress. This allows the interaction unit to adjust the interaction expression method based on the user's emotion.

[0112] The interaction unit can adjust the level of interaction detail based on the importance of the virtual environment during interaction. The interaction unit can evaluate the importance of the virtual environment based on, for example, a user's rating or the frequency of data. For example, a virtual environment related to an important event can provide detailed interaction information. The interaction unit can also provide concise interaction information for a virtual environment related to everyday events. Furthermore, the interaction unit can provide detailed interaction information for a virtual environment in which the user is particularly interested. For example, if the user is interested in a particular theme, the interaction unit can interact in detail with a virtual environment related to that theme. This allows the interaction unit to adjust the level of interaction detail based on the importance of the virtual environment.

[0113] The interaction unit can apply different interaction algorithms depending on the category of the virtual environment during interaction. For example, the interaction unit selects an interaction algorithm based on the category of the virtual environment. For example, a landscape reproduction algorithm can be applied to a travel-related virtual environment. The interaction unit can also apply an animal reproduction algorithm to a pet-related virtual environment. Furthermore, the interaction unit can apply a person reproduction algorithm to a virtual environment related to a family event. For example, a person reproduction algorithm can be applied to a family group photo or video to identify each person. This allows the interaction unit to apply different interaction algorithms depending on the category of the virtual environment.

[0114] The interaction unit can improve the accuracy of interaction during interaction by referring to the user's past interaction results. The interaction unit acquires past interaction results, for example, by using past interaction logs or user feedback. For example, the interaction unit adjusts the interaction algorithm based on interaction methods that the user has previously preferred. The interaction unit can also improve the accuracy of interaction based on the user's past feedback. For example, the interaction algorithm can be adjusted to avoid interaction methods that the user has previously dissatisfied with. Furthermore, the interaction unit can improve the accuracy of interaction based on the user's past behavioral patterns. For example, the interaction unit preferentially applies interaction methods that the user has frequently used in the past. In this way, the interaction unit can improve the accuracy of interaction by referring to the user's past interaction results.

[0115] The interaction unit can estimate the user's emotion and adjust the length of the interaction based on the estimated user's emotion. The interaction unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the user's facial expression can be captured with a camera and the emotion can be estimated using an emotion estimation algorithm. The interaction unit can also estimate the user's emotion using voice analysis technology. For example, the emotion can be estimated by analyzing the tone and speed of the user's voice. Furthermore, the interaction unit can adjust the length of the interaction based on the user's emotion. For example, if the user is in a hurry, the interaction unit can provide short, to-the-point interaction information. This allows the interaction unit to adjust the length of the interaction based on the user's emotion.

[0116] During interaction, the interaction unit can determine an interaction priority based on the time when the virtual environment was created. The interaction unit obtains the time when the virtual environment was created, for example, by analyzing metadata or using a timestamp. For example, the interaction unit prioritizes interaction with a recently created virtual environment. The interaction unit can also prioritize interaction with a virtual environment related to a particular event or anniversary. Furthermore, the interaction unit can prioritize interaction with a virtual environment created during a period in which the user is particularly interested. For example, if the user is interested in a particular period, the interaction unit prioritizes interaction with a virtual environment created during that period. This allows the interaction unit to determine an interaction priority based on the time when the virtual environment was created.

[0117] The interaction unit can adjust the order of interactions based on the relevance of the virtual environments during interaction. The interaction unit evaluates the relevance of the virtual environments based on, for example, the similarity of their contents or the user's interest. For example, the interaction unit collectively interacts with virtual environments related to the same event. The interaction unit can also prioritize interaction with virtual environments related to a theme in which the user is particularly interested. Furthermore, the interaction unit can prioritize interaction with highly relevant virtual environments based on the user's past interaction results. For example, the interaction unit prioritizes interaction with virtual environments related to a theme that the user has previously liked. This allows the interaction unit to adjust the order of interactions based on the relevance of the virtual environments.

[0118] The interaction unit can adjust the use of technical terms in the interaction according to the user's level of expertise during the interaction. The interaction unit evaluates the level of expertise based on, for example, the user's occupation or past learning history. For example, if the user has technical expertise, the interaction unit can provide interaction information using detailed technical terms. Also, if the user does not have technical expertise, the interaction unit can provide interaction information in simple language. Furthermore, the interaction unit can adjust the use of optimal technical terms based on the user's past feedback. For example, technical terms that the user has previously preferred can be used preferentially. This allows the interaction unit to adjust the use of technical terms in the interaction according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, generation unit, access unit, and interaction unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives images and videos from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the images and videos using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a virtual environment based on the analyzed data. The access unit is realized by the output device 40 of the smart device 14 and provides an interface for the user to access the virtual environment. The interaction unit is realized by the control unit 46A of the smart device 14 and manages the user's actions and interactions within the virtual environment. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, access unit, and interaction unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives images and videos from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes images and videos using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a virtual environment based on the analyzed data. The access unit is realized by the speaker 240 of the smart glasses 214 and provides an interface for the user to access the virtual environment. The interaction unit is realized by the control unit 46A of the smart glasses 214 and manages the user's actions and interactions within the virtual environment. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, access unit, and interaction unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives images and videos from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the images and videos using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a virtual environment based on the analyzed data. The access unit is realized by the display 343 of the headset-type terminal 314 and provides an interface for the user to access the virtual environment. The interaction unit is realized by the control unit 46A of the headset-type terminal 314 and manages the user's actions and interactions within the virtual environment. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, access unit, and interaction unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives images and videos from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the images and videos using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a virtual environment based on the analyzed data. The access unit is realized by the speaker 240 of the robot 414 and provides an interface for the user to access the virtual environment. The interaction unit is realized by the control unit 46A of the robot 414 and manages the user's actions and interactions within the virtual environment.

[0119] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0120] The reception unit can analyze the user's past upload history and select an appropriate reception method. For example, it can use data mining technology to analyze the user's past upload history, identify time periods in which the user frequently uploaded in the past, and send a notification encouraging the user to upload during those time periods. It can also prioritize upload methods (voice, text, etc.) that the user has used in the past. It can also encourage the user to upload images and videos related to specific events or anniversaries based on the user's past upload history. This allows the reception unit to analyze the user's past upload history and select an appropriate reception method.

[0121] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, the user's emotions can be estimated using facial expression recognition technology, by capturing the user's facial expressions with a camera, and by using an emotion estimation algorithm to estimate the emotions. Alternatively, the user's emotions can be estimated using voice analysis technology, by analyzing the tone and speed of the user's voice to estimate the emotions. Furthermore, the way the analysis is presented can be adjusted based on the user's emotions, and if the user is relaxed, analysis results that progress at a leisurely pace can be provided. This allows the analysis unit to adjust the way the analysis is presented based on the user's emotions.

[0122] The generation unit can estimate the user's emotion and adjust the method for generating the virtual environment based on the estimated user's emotion. For example, the user's emotion can be estimated using facial expression recognition technology, by capturing the user's facial expression with a camera, and by using an emotion estimation algorithm. Alternatively, the user's emotion can be estimated using voice analysis technology, by analyzing the tone and speed of the user's voice. Furthermore, the method for generating the virtual environment can be adjusted based on the user's emotion, so that if the user is relaxed, a virtual environment that progresses at a leisurely pace can be generated. This allows the generation unit to adjust the method for generating the virtual environment based on the user's emotion.

[0123] The access unit can estimate the user's emotions and adjust the manner in which the access is expressed based on the estimated user's emotions. For example, the user's emotions can be estimated using facial expression recognition technology, by capturing the user's facial expressions with a camera, and then using an emotion estimation algorithm to estimate the emotions. Alternatively, the user's emotions can be estimated using voice analysis technology, by analyzing the tone and speed of the user's voice to estimate the emotions. Furthermore, the manner in which the access is expressed can be adjusted based on the user's emotions. For example, if the user is nervous, a calm interface can be provided to reduce visual stress. This allows the access unit to adjust the manner in which the access is expressed based on the user's emotions.

[0124] The interaction unit can estimate the user's emotion and adjust the interaction expression method based on the estimated user's emotion. For example, the user's emotion can be estimated using facial expression recognition technology, by capturing the user's facial expression with a camera, and then using an emotion estimation algorithm to estimate the emotion. Alternatively, the user's emotion can be estimated using voice analysis technology, by analyzing the tone and speed of the user's voice to estimate the emotion. Furthermore, the interaction expression method can be adjusted based on the user's emotion. For example, if the user is nervous, an interface with calm colors can be provided to reduce visual stress. This allows the interaction unit to adjust the interaction expression method based on the user's emotion.

[0125] When accepting images and videos, the acceptance unit can prioritize accepting highly relevant data in consideration of the user's geographical location information. For example, the acceptance unit can acquire the user's geographical location information using GPS data and prioritize accepting images and videos related to the user's current location. The acceptance unit can also acquire the user's geographical location information using IP address location information and prioritize accepting images and videos related to places the user has visited in the past. Furthermore, the acceptance unit can prioritize accepting images and videos related to places the user plans to visit in the future. For example, if the user is planning a trip, the acceptance unit can prioritize accepting images and videos related to the travel destination. This allows the acceptance unit to prioritize accepting highly relevant data in consideration of the user's geographical location information.

[0126] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the image or video. For example, the importance of the image or video can be evaluated based on user ratings or data frequency, and images or videos related to important events can be analyzed in detail. Images or videos related to everyday events can also be analyzed briefly. Furthermore, images or videos in which the user is particularly interested can be analyzed in detail. For example, if the user is interested in a particular theme, images and videos related to that theme can be analyzed in detail. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the image or video.

[0127] The generator can adjust the level of detail of the virtual environment based on the importance of the analyzed data during generation. For example, the generator can evaluate the importance of the analyzed data based on the user's ratings and the frequency of the data, and a virtual environment related to an important event can be reproduced in detail. A virtual environment related to everyday occurrences can be reproduced briefly. Furthermore, a virtual environment in which the user is particularly interested can be reproduced in detail. For example, if the user is interested in a particular theme, a virtual environment related to that theme can be reproduced in detail. This allows the generator to adjust the level of detail of the virtual environment based on the importance of the analyzed data.

[0128] The access unit can adjust the level of detail of access based on the importance of the virtual environment at the time of access. For example, the importance of a virtual environment can be evaluated based on a user's rating or the frequency of data, and detailed access information can be provided for a virtual environment related to an important event. Also, concise access information can be provided for a virtual environment related to everyday events. Furthermore, detailed access information can be provided for a virtual environment in which the user is particularly interested. For example, if a user shows interest in a particular theme, virtual environments related to that theme can be accessed in detail. This allows the access unit to adjust the level of detail of access based on the importance of the virtual environment.

[0129] The interaction unit can adjust the level of detail of the interaction based on the importance of the virtual environment during interaction. For example, the importance of the virtual environment can be evaluated based on the user's ratings and the frequency of data, and a virtual environment related to an important event can provide detailed interaction information. A virtual environment related to everyday events can also provide concise interaction information. Furthermore, a virtual environment in which the user is particularly interested can provide detailed interaction information. For example, if the user is interested in a particular theme, a virtual environment related to that theme can be interacted with in detail. This allows the interaction unit to adjust the level of detail of the interaction based on the importance of the virtual environment.

[0130] The processing flow of the second embodiment will be briefly explained below.

[0131] Step 1: The reception unit receives images or videos from a user. For example, images and videos in formats such as JPEG, PNG, MP4, and AVI can be received. Step 2: The analysis unit uses the generation AI to analyze the image or video received by the reception unit. For example, it uses image recognition algorithms or video analysis technology to understand the content of the image or video. Step 3: The generator uses AI to generate a virtual environment based on the data generated by the analyzer. For example, it uses 3D modeling and VR technology to recreate a past space. Step 4: The access unit provides an interface for the user to access the virtual environment. For example, the user can access the virtual environment using a web browser or a VR headset. Step 5: The interaction unit manages the user's actions and interactions within the virtual environment, for example, by tracking the user's actions and using voice commands.

[0132] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0137] 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.

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

[0139] 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.

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

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0153] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0154] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0155] 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.

[0156] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0158] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0159] 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.

[0160] 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.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0162] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0164] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0166] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0169] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0170] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0171] 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.

[0172] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0173] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0174] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0175] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0176] 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.

[0177] 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.

[0178] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0179] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0180] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0181] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0182] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0183] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0184] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0185] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0186] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0187] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0188] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0189] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0190] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0191] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0192] 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.

[0193] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0194] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0195] 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.

[0196] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0197] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0198] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0199] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0200] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0201] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0202] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0203] [Explanation of symbols]

[0204] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives images or videos from a user; an analysis unit that analyzes the image or video accepted by the acceptance unit; a generation unit that generates a virtual environment based on the data analyzed by the analysis unit; an access unit for allowing a user to access the virtual environment generated by the generation unit; an interaction unit that manages interactions within the virtual environment accessed by the access unit; A system characterized by:

2. The analysis unit Understanding the content of images or videos and generating data to recreate past spaces 2. The system of claim 1.

3. The generation unit Generate a virtual environment based on the data generated by the analysis unit 2. The system of claim 1.

4. The access unit is Provides an interface for users to access the virtual environment 2. The system of claim 1.

5. The interaction unit includes: Managing user behavior or interactions within a virtual environment 2. The system of claim 1.

6. The reception unit To estimate a user's emotion and adjust the timing of receiving an image or video based on the estimated user's emotion.

2. The system of claim 1.

7. The reception unit Analyze the user's upload history and select the appropriate reception method.

2. The system of claim 1.

8. The reception unit Filtering images and videos as they arrive based on the user's current projects and interests 2. The system of claim 1.

9. The reception unit When accepting images or videos, select the most appropriate method of acceptance depending on the user's input method.

2. The system of claim 1.

10. The reception unit Estimate the user's emotions and prioritize the images and videos to be accepted based on the estimated user emotions.

2. The system of claim 1.

Images