system

Abstract

An object of the system according to the embodiment is to convert 2D photographs into 3D to enable realistic experience in a Metaverse space.SOLUTION: A system according to an embodiment includes a 2D photograph acquirer, a 3D converter, a Metaverse reproducer, and a VR experience unit. The 2D photograph acquiring unit acquires a 2D photograph from a user. The 3D conversion unit converts the 2D photograph acquired by the 2D photograph acquiring unit into 3D. The Metaverse reproduction unit reproduces the 3D photograph converted by the 3D conversion unit in a Metaverse space. The VR experience unit allows the user to experience the 3D photograph reproduced by the Metaverse reproduction unit via the VR devices.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] Existing technology is limited in its ability to recreate memories using 2D photos, and there is room for improvement in providing a more vivid and realistic experience.

[0005] The system according to the embodiment aims to convert 2D photos into 3D and enable a realistic experience in the Metaverse space. [Means for solving the problem]

[0006] The system according to the embodiment includes a 2D photo acquisition unit, a 3D conversion unit, a metaverse reproduction unit, and a VR experience unit. The 2D photo acquisition unit acquires 2D photos from a user. The 3D conversion unit converts the 2D photos acquired by the 2D photo acquisition unit into 3D. The metaverse reproduction unit reproduces the 3D photos converted by the 3D conversion unit in a metaverse space. The VR experience unit allows the user to experience the 3D photos reproduced by the metaverse reproduction unit via a VR device. [Effects of the Invention]

[0007] The system according to the embodiment can convert 2D photos into 3D, allowing them to be experienced realistically in the Metaverse space. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The experiential album providing metaverse system according to an embodiment of the present invention is a system that converts 2D photos stored in an album into 3D photos using AI technology and then reproduces them in the metaverse space, allowing users to realistically experience past memories through a VR device. As a result, the experiential album providing metaverse system allows users to experience past memories more vividly and share them with others.

[0029] The experiential album provision metaverse system according to the embodiment includes a 2D photo acquisition unit, a 3D conversion unit, a metaverse reproduction unit, and a VR experience unit. The 2D photo acquisition unit acquires 2D photos from a user. For example, it acquires photos in JPEG or PNG format uploaded by the user. The 2D photo acquisition unit can also directly acquire photos taken by the user with a smartphone or camera. The 2D photo acquisition unit can also acquire photos from cloud storage. For example, it can acquire photos stored by the user on Google Photos or Dropbox. The 3D conversion unit converts the 2D photos acquired by the 2D photo acquisition unit into 3D. For example, the generation AI analyzes the depth of the 2D photos using a depth estimation algorithm and generates a 3D model. The generation AI can also convert the 2D photos into 3D using stereo vision technology. The generation AI analyzes the three-dimensionality of the 2D photos using a machine learning model and generates a 3D model. For example, the generation AI receives multiple 2D photos as input and generates a 3D model from those photos. The metaverse reproduction unit reproduces the 3D photos converted by the 3D conversion unit in the metaverse space. For example, the generation AI places 3D photos on a virtual reality platform so that the user can access them using a VR device. The metaverse reproduction unit can also arrange the 3D photos taking into account the characteristics of the user interface. For example, the 3D photos can be intuitively arranged to allow the user to easily operate them. The VR experience unit allows the user to experience the 3D photos reproduced by the metaverse reproduction unit through a VR device. For example, the VR experience unit can display the 3D photos to the user using a head-mounted display. The VR experience unit can also allow the user to touch the 3D photos using VR gloves. The VR experience unit can also provide the user with an explanation of the 3D photos using an audio guide. For example, the VR experience unit can provide the user with audio information related to the 3D photos while the user is viewing them. This allows the experiential album provision metaverse system according to the embodiment to realistically experience past memories. For example, a user can 3D-encode family photos and reproduce them in the metaverse space, allowing them to realistically experience memories with their family.Users can also create 3D photos of their travel destinations and recreate them in the Metaverse space, giving them the experience of visiting those places again.

[0030] The 3D conversion unit automatically complements the background information of 2D photos, generating more detailed 3D models. For example, the 3D conversion unit uses a generation AI to analyze the background information of a 2D photo and automatically complement missing parts. For example, if a part of a landscape photo is cut off, that part is estimated and complemented. The 3D conversion unit also uses a generation AI to recognize objects in the background of the 2D photo and incorporate them into the 3D model. For example, detailed information such as buildings and trees is added. The 3D conversion unit also uses a generation AI to analyze the light reflection and shadow information of the 2D photo and reproduce realistic light and shadow in the 3D model. For example, the intensity of sunlight and the position of shadows are accurately reproduced. This allows for the generation of more detailed 3D models.

[0031] The 3D conversion unit can estimate the movements of people in 2D photos and generate dynamic 3D models. For example, the generation AI in the 3D conversion unit analyzes the posture and movements of people in 2D photos and generates dynamic 3D models. For example, it recreates walking postures and waving movements. The generation AI in the 3D conversion unit also analyzes multiple 2D photos and continuously recreates people's movements. For example, it recreates dance movements from consecutive photos in a 3D model. The generation AI in the 3D conversion unit also analyzes the facial expressions of people in 2D photos and reflects dynamic facial changes in the 3D model. For example, it realistically recreates smiling or surprised expressions. This allows for the generation of dynamic 3D models.

[0032] The 3D conversion unit can estimate audio information from 2D photos and add audio to 3D models. For example, the generation AI in the 3D conversion unit analyzes scenes in 2D photos and estimates related audio information. For example, the sound of waves is added to a photo of a beach. The generation AI in the 3D conversion unit also analyzes the mouth movements of people in a photo and adds the estimated conversation content as audio. For example, laughter is added to a photo of smiling people. The generation AI in the 3D conversion unit also generates environmental sounds based on the background information of the 2D photo and adds them to the 3D model. For example, birds chirping is added to a photo of a forest. This makes it possible to add audio to 3D models.

[0033] The 3D conversion unit can estimate scent information from 2D photos and add scents to 3D models. For example, the generation AI in the 3D conversion unit analyzes the scene in the 2D photo and estimates related scent information. For example, it adds the scent of flowers to a photo of a flower field. The generation AI in the 3D conversion unit also recognizes objects in the photo and generates a scent based on that. For example, it adds the scent of ingredients to a photo of food. The generation AI in the 3D conversion unit also generates environmental scents based on the background information of the 2D photo and adds them to the 3D model. For example, it adds the scent of trees to a photo of a forest. This makes it possible to add scents to 3D models.

[0034] The metaverse reproduction unit automatically generates environmental sounds within the metaverse space, providing a more realistic experience. For example, the generation AI of the metaverse reproduction unit analyzes scenes within the metaverse space and automatically generates related environmental sounds. For example, the sound of cars and people talking is added to urban scenes. The generation AI of the metaverse reproduction unit also generates environmental sounds according to the weather and time of day within the metaverse space. For example, the sound of rain and thunder is added on rainy days. The generation AI of the metaverse reproduction unit also generates environmental sounds based on objects within the metaverse space. For example, the sound of flowing water is added near a river. This provides a more realistic experience.

[0035] The metaverse reproduction unit automatically sets the weather and time of day within the metaverse space, providing an experience that is close to reality. For example, the generation AI in the metaverse reproduction unit analyzes a scene within the metaverse space and automatically sets the appropriate weather and time of day. For example, sunny skies and sunsets are set for beach scenes. The generation AI in the metaverse reproduction unit also recreates the weather and time of day at the time based on the user's past photo data. For example, it recreates the weather at the time a travel photo was taken. The generation AI in the metaverse reproduction unit also dynamically changes the weather and time of day within the metaverse space, providing a real-time experience. For example, it recreates the transition from day to night. This allows for an experience that is close to reality.

[0036] The metaverse reproduction unit can generate interactive objects in the metaverse space and add a function that reacts when the user touches them. For example, the generation AI generates interactive objects in the metaverse space and adds a function that reacts when the user touches them. For example, opening a door makes it possible to see the inside of a room. The metaverse reproduction unit also adds a function that plays audio or video when an object in the metaverse space is touched. For example, clicking on a photo plays a story about the moment. The metaverse reproduction unit also provides a function where the generation AI analyzes the user's actions and the interactive object reacts in real time. For example, a character reacts when the user waves their hand. This makes it possible to generate interactive objects that react when the user touches them.

[0037] The metaverse reproduction unit enables multiple users to access the metaverse space simultaneously, providing a real-time collaborative experience. For example, the generation AI of the metaverse reproduction unit enables multiple users to access the metaverse space simultaneously, providing a real-time collaborative experience. For example, exploring a virtual space with friends. The metaverse reproduction unit also adds a function to support communication between multiple users in the metaverse space. For example, communicating using voice chat or text chat. The metaverse reproduction unit also analyzes the actions of multiple users to provide a real-time collaborative experience. For example, it adds a function to play a game together. This enables multiple users to access the metaverse space simultaneously, providing a real-time collaborative experience.

[0038] The VR experience unit can automatically provide additional information related to past memories. For example, the generation AI in the VR experience unit automatically provides contemporary news related to past memories. For example, it displays news from the day a photo was taken. The generation AI in the VR experience unit also automatically provides contemporary music related to past memories. For example, it plays hit songs from the year the photo was taken. The generation AI in the VR experience unit also automatically provides contemporary cultural and event information related to past memories. For example, it displays trends and events from the time the photo was taken. This allows the automatic provision of additional information related to past memories.

[0039] The VR experience unit can add a function that highlights specific events or occurrences selected by the user when recreating past memories. For example, the VR experience unit adds a function that enables the generation AI to highlight specific events or occurrences selected by the user. For example, when recreating wedding photos, important moments are highlighted. The VR experience unit also adds information related to specific events or occurrences selected by the user. For example, when recreating travel photos, detailed information about places visited is displayed. The VR experience unit also adds effects that highlight specific events or occurrences based on the user's selection by the generation AI. For example, a fireworks effect is added to birthday photos. This allows the VR experience unit to add a function that highlights specific events or occurrences.

[0040] The VR experience unit can add a biofeedback function to recreate emotions experienced by the user when recreating past memories. For example, the generation AI in the VR experience unit adds the biofeedback function to the memory recreation based on the user's past emotional data. For example, it analyzes heart rate and galvanic skin response to recreate emotions. The VR experience unit also uses the biofeedback function to recreate emotions experienced by the user in real time. For example, it recreates an increase in heart rate at the moment the user becomes excited. The generation AI in the VR experience unit also customizes the memory recreation based on the user's biofeedback data. For example, it recreates the breathing rhythm at the moment the user relaxes. This allows the biofeedback function to be added to recreate emotions experienced by the user.

[0041] The VR experience unit automatically generates background information for the memories to be shared when sharing the experience with others, deepening the experience. For example, the generation AI in the VR experience unit automatically generates background information for the memories to be shared when sharing the experience with others. For example, when sharing travel photos, detailed information about the places visited is displayed. The generation AI also automatically generates episodes and stories related to the memories to be shared when sharing the experience with others. For example, when sharing family photos, stories about family memories are displayed. The generation AI also adds interactive elements based on the background information for the memories to be shared when sharing the experience with others. For example, while looking at photos with friends, the events of that time are recreated. This automatically generates background information for the memories to be shared, deepening the experience.

[0042] The VR experience unit can add interactive games and quizzes related to the memories to be shared when sharing a VR experience with others. For example, the generation AI adds interactive games related to the memories to be shared when sharing a VR experience with others. For example, while looking at travel photos, it poses quizzes about the locations. The VR experience unit also adds interactive quizzes related to the memories to be shared when sharing a VR experience with others. For example, while looking at family photos, it poses quizzes about family. The VR experience unit also generates interactive games and quizzes related to the memories to be shared in real time when sharing a VR experience with others. For example, it automatically generates games to enjoy with friends. This makes it possible to add interactive games and quizzes related to the memories to be shared.

[0043] The VR experience unit can enrich the experience by automatically generating music and videos related to the memories to be shared when sharing a VR experience with others. For example, the generation AI in the VR experience unit automatically generates music related to the memories to be shared when sharing a VR experience with others. For example, while looking at travel photos, music related to the location is played. The generation AI in the VR experience unit also automatically generates videos related to the memories to be shared when sharing a VR experience with others. For example, while looking at family photos, a video recreating the events of that time is played. The generation AI in the VR experience unit also generates music and videos related to the memories to be shared in real time when sharing a VR experience with others. For example, it automatically generates videos to enjoy with friends. This allows the automatic generation of music and videos related to the memories to be shared, enriching the experience.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The experiential album provision metaverse system can also be equipped with a health management unit that acquires the user's health data and customizes the experience. For example, it can monitor the user's heart rate and blood pressure in real time and display a warning if an abnormality is detected during the experience. The health management unit can also suggest experiences based on the user's health condition. For example, it can provide a calming scene for a user who needs to relax. The health management unit can also provide feedback after the experience based on the user's health data. For example, it can display a graph of heart rate fluctuations during the experience and evaluate the relaxation effect. This makes it possible to provide a safe and optimal experience based on the user's health condition.

[0046] The experiential album provision metaverse system can further include a behavior analysis unit that analyzes the user's past behavioral data and customizes the experience. For example, it can analyze data on places the user has visited or events they have attended in the past and suggest experiences based on that. The behavior analysis unit can also analyze the user's interests and preferences to provide the optimal experience. For example, it can recreate scenes related to places the user has visited frequently in the past. The behavior analysis unit can also provide post-experience feedback based on the user's behavioral data. For example, it can analyze which scene the user enjoyed most and reflect that in the next experience. This makes it possible to provide a personalized experience based on the user's past behavior.

[0047] The experiential album providing metaverse system can further provide historical background information related to the user's past memories. For example, it can display historical events and cultural background related to the time when a photo selected by the user was taken. The historical background information section can also provide detailed explanations of the history and culture of places the user has visited. For example, it can introduce historical landmarks and cultural events related to photos of travel destinations. The historical background information section can also provide information tailored to the user's interests. For example, it can display information related to historical themes that interest the user. This allows for the provision of a wealth of historical background information related to the user's past memories.

[0048] The experiential album providing metaverse system can further provide future prediction information related to the user's past memories. For example, it displays future prediction information related to the location or event of a photo selected by the user. The future prediction information unit can also introduce future developments and plans for places visited by the user. For example, it can provide information on urban redevelopment plans and new tourist attractions. The future prediction information unit can also provide information on future trends and technologies according to the user's interests. For example, it can display future predictions for fields in which the user is interested. This makes it possible to provide future prediction information related to the user's past memories.

[0049] The experiential album providing metaverse system can further provide educational information related to the user's past memories. For example, it can display scientific knowledge or educational information related to a photo selected by the user. The educational information section can also provide information about the natural environment and ecosystem of a place visited by the user. For example, it can introduce information about the flora and fauna related to a photo of a natural park. The educational information section can also provide learning content according to the user's interests. For example, it can display information about science or history that interests the user. This makes it possible to provide educational information related to the user's past memories.

[0050] The experiential album provision metaverse system can also generate artwork related to the user's past memories. For example, AI generates artwork based on photos selected by the user and displays it in the metaverse space. The art generation unit can also suggest artwork based on themes related to the user's memories. For example, it can generate paintings or sculptures based on family photos. The art generation unit can also select an art style based on the user's interests. For example, it can generate artwork by selecting a style such as impressionism or abstract painting. This makes it possible to provide unique artwork related to the user's past memories.

[0051] The experiential album provision metaverse system can also generate music playlists related to the user's past memories. For example, a playlist can be created based on music that was popular around the time the user selected photos were taken. The music playlist generation unit can also suggest music based on themes related to the user's memories. For example, relaxing music related to travel photos can be provided. The music playlist generation unit can also select music genres according to the user's interests. For example, a playlist can be created by selecting genres such as classical or pop. This makes it possible to provide a music playlist related to the user's past memories.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The 2D photo acquisition unit acquires 2D photos from the user. For example, it acquires JPEG or PNG photos uploaded by the user. It can also acquire photos taken by the user directly with a smartphone or camera. It can also acquire photos from cloud storage. For example, it acquires photos saved by the user in Google Photos or Dropbox. Step 2: The 3D conversion unit converts the 2D photos acquired by the 2D photo acquisition unit into 3D. For example, the generation AI can analyze the depth of the 2D photos using a depth estimation algorithm and generate a 3D model. It can also convert 2D photos into 3D using stereo vision technology or machine learning models. It can also receive multiple 2D photos as input and generate a 3D model from them. Step 3: The Metaverse Reproduction Unit reproduces the 3D photo converted by the 3D Conversion Unit in the Metaverse space. For example, the Generative AI can place the 3D photo on a virtual reality platform so that users can access it using a VR device. It can also intuitively place the 3D photo taking into account the characteristics of the user interface. Step 4: The VR experience unit allows the user to experience the 3D photo reproduced by the metaverse reproduction unit through a VR device. For example, the 3D photo is displayed to the user using a head-mounted display. It is also possible to allow the user to touch the 3D photo using VR gloves. Furthermore, an explanation of the 3D photo can be provided to the user using an audio guide.

[0054] (Example 2) The experiential album providing metaverse system according to an embodiment of the present invention is a system that converts 2D photos stored in an album into 3D photos using AI technology and then reproduces them in the metaverse space, allowing users to realistically experience past memories through a VR device. As a result, the experiential album providing metaverse system allows users to experience past memories more vividly and share them with others.

[0055] The experiential album provision metaverse system according to the embodiment includes a 2D photo acquisition unit, a 3D conversion unit, a metaverse reproduction unit, and a VR experience unit. The 2D photo acquisition unit acquires 2D photos from a user. For example, it acquires photos in JPEG or PNG format uploaded by the user. The 2D photo acquisition unit can also directly acquire photos taken by the user with a smartphone or camera. The 2D photo acquisition unit can also acquire photos from cloud storage. For example, it can acquire photos stored by the user on Google Photos or Dropbox. The 3D conversion unit converts the 2D photos acquired by the 2D photo acquisition unit into 3D. For example, the generation AI analyzes the depth of the 2D photos using a depth estimation algorithm and generates a 3D model. The generation AI can also convert the 2D photos into 3D using stereo vision technology. The generation AI analyzes the three-dimensionality of the 2D photos using a machine learning model and generates a 3D model. For example, the generation AI receives multiple 2D photos as input and generates a 3D model from those photos. The metaverse reproduction unit reproduces the 3D photos converted by the 3D conversion unit in the metaverse space. For example, the generation AI places 3D photos on a virtual reality platform so that the user can access them using a VR device. The metaverse reproduction unit can also arrange the 3D photos taking into account the characteristics of the user interface. For example, the 3D photos can be intuitively arranged to allow the user to easily operate them. The VR experience unit allows the user to experience the 3D photos reproduced by the metaverse reproduction unit through a VR device. For example, the VR experience unit can display the 3D photos to the user using a head-mounted display. The VR experience unit can also allow the user to touch the 3D photos using VR gloves. The VR experience unit can also provide the user with an explanation of the 3D photos using an audio guide. For example, the VR experience unit can provide the user with audio information related to the 3D photos while the user is viewing them. This allows the experiential album provision metaverse system according to the embodiment to realistically experience past memories. For example, a user can 3D-encode family photos and reproduce them in the metaverse space, allowing them to realistically experience memories with their family.Users can also create 3D photos of their travel destinations and recreate them in the Metaverse space, giving them the experience of visiting those places again.

[0056] The 3D conversion unit automatically complements the background information of 2D photos, generating more detailed 3D models. For example, the 3D conversion unit uses a generation AI to analyze the background information of a 2D photo and automatically complement missing parts. For example, if a part of a landscape photo is cut off, that part is estimated and complemented. The 3D conversion unit also uses a generation AI to recognize objects in the background of the 2D photo and incorporate them into the 3D model. For example, detailed information such as buildings and trees is added. The 3D conversion unit also uses a generation AI to analyze the light reflection and shadow information of the 2D photo and reproduce realistic light and shadow in the 3D model. For example, the intensity of sunlight and the position of shadows are accurately reproduced. This allows for the generation of more detailed 3D models.

[0057] The 3D conversion unit can estimate the movements of people in 2D photos and generate dynamic 3D models. For example, the generation AI in the 3D conversion unit analyzes the posture and movements of people in 2D photos and generates dynamic 3D models. For example, it recreates walking postures and waving movements. The generation AI in the 3D conversion unit also analyzes multiple 2D photos and continuously recreates people's movements. For example, it recreates dance movements from consecutive photos in a 3D model. The generation AI in the 3D conversion unit also analyzes the facial expressions of people in 2D photos and reflects dynamic facial changes in the 3D model. For example, it realistically recreates smiling or surprised expressions. This allows for the generation of dynamic 3D models.

[0058] The 3D conversion unit can use the emotion estimation function to analyze the emotions of people in 2D photos and generate 3D models based on those emotions. For example, the generation AI in the 3D conversion unit analyzes the facial expressions of people in 2D photos and estimates their emotions. For example, smiling and sad expressions are quantified as emotion scores. The 3D conversion unit also uses the emotion estimation function to generate 3D models based on the emotions of people in the photos. For example, a 3D model with a bright expression is generated for a smiling person. The 3D conversion unit also uses the emotion estimation data to reflect backgrounds and environments that correspond to the emotions of people in the photos in the 3D model. For example, a bright background is added for the emotion of joy. This makes it possible to generate 3D models based on emotions.

[0059] The 3D conversion unit can estimate audio information from 2D photos and add audio to 3D models. For example, the generation AI in the 3D conversion unit analyzes scenes in 2D photos and estimates related audio information. For example, the sound of waves is added to a photo of a beach. The generation AI in the 3D conversion unit also analyzes the mouth movements of people in a photo and adds the estimated conversation content as audio. For example, laughter is added to a photo of smiling people. The generation AI in the 3D conversion unit also generates environmental sounds based on the background information of the 2D photo and adds them to the 3D model. For example, birds chirping is added to a photo of a forest. This makes it possible to add audio to 3D models.

[0060] The 3D conversion unit can estimate scent information from 2D photos and add scents to 3D models. For example, the generation AI in the 3D conversion unit analyzes the scene in the 2D photo and estimates related scent information. For example, it adds the scent of flowers to a photo of a flower field. The generation AI in the 3D conversion unit also recognizes objects in the photo and generates a scent based on that. For example, it adds the scent of ingredients to a photo of food. The generation AI in the 3D conversion unit also generates environmental scents based on the background information of the 2D photo and adds them to the 3D model. For example, it adds the scent of trees to a photo of a forest. This makes it possible to add scents to 3D models.

[0061] The 3D conversion unit uses the emotion estimation function to analyze the user's emotions when selecting a photo and can suggest the optimal 3D conversion. For example, the 3D conversion unit uses the emotion estimation function to analyze the user's emotions in real time when selecting a photo. For example, if the user selects a photo of a smiling face, the unit analyzes that emotion. The 3D conversion unit then uses the generation AI to suggest the optimal 3D conversion based on the user's emotion data. For example, it would suggest a bright 3D model for a photo in which the user expresses joy. The 3D conversion unit also uses the emotion estimation function to present the optimal 3D conversion options based on the emotion score of the photo selected by the user. For example, it could add special effects to photos with a high emotion score. This allows the unit to suggest the optimal 3D conversion based on the user's emotions.

[0062] The metaverse reproduction unit automatically generates environmental sounds within the metaverse space, providing a more realistic experience. For example, the generation AI of the metaverse reproduction unit analyzes scenes within the metaverse space and automatically generates related environmental sounds. For example, the sound of cars and people talking is added to urban scenes. The generation AI of the metaverse reproduction unit also generates environmental sounds according to the weather and time of day within the metaverse space. For example, the sound of rain and thunder is added on rainy days. The generation AI of the metaverse reproduction unit also generates environmental sounds based on objects within the metaverse space. For example, the sound of flowing water is added near a river. This provides a more realistic experience.

[0063] The metaverse reproduction unit automatically sets the weather and time of day within the metaverse space, providing an experience that is close to reality. For example, the generation AI in the metaverse reproduction unit analyzes a scene within the metaverse space and automatically sets the appropriate weather and time of day. For example, sunny skies and sunsets are set for beach scenes. The generation AI in the metaverse reproduction unit also recreates the weather and time of day at the time based on the user's past photo data. For example, it recreates the weather at the time a travel photo was taken. The generation AI in the metaverse reproduction unit also dynamically changes the weather and time of day within the metaverse space, providing a real-time experience. For example, it recreates the transition from day to night. This allows for an experience that is close to reality.

[0064] The metaverse reproduction unit can use the emotion estimation function to customize the metaverse space according to the user's emotions. For example, the metaverse reproduction unit uses the emotion estimation function to analyze the user's emotions in real time and customize the metaverse space according to those emotions. For example, if the user wants to relax, it provides a quiet environment. The metaverse reproduction unit also customizes the objects and background in the metaverse space using the generation AI based on the user's emotion data. For example, if the user is feeling happy, it provides a space with bright colors. The metaverse reproduction unit also uses the emotion estimation function to add music and sound effects to the metaverse space according to the user's emotions. For example, if the user is feeling sad, it provides soothing music. This allows the metaverse space to be customized according to the user's emotions.

[0065] The metaverse reproduction unit can generate interactive objects in the metaverse space and add a function that reacts when the user touches them. For example, the generation AI generates interactive objects in the metaverse space and adds a function that reacts when the user touches them. For example, opening a door makes it possible to see the inside of a room. The metaverse reproduction unit also adds a function that plays audio or video when an object in the metaverse space is touched. For example, clicking on a photo plays a story about the moment. The metaverse reproduction unit also provides a function where the generation AI analyzes the user's actions and the interactive object reacts in real time. For example, a character reacts when the user waves their hand. This makes it possible to generate interactive objects that react when the user touches them.

[0066] The metaverse reproduction unit enables multiple users to access the metaverse space simultaneously, providing a real-time collaborative experience. For example, the generation AI of the metaverse reproduction unit enables multiple users to access the metaverse space simultaneously, providing a real-time collaborative experience. For example, exploring a virtual space with friends. The metaverse reproduction unit also adds a function to support communication between multiple users in the metaverse space. For example, communicating using voice chat or text chat. The metaverse reproduction unit also analyzes the actions of multiple users to provide a real-time collaborative experience. For example, it adds a function to play a game together. This enables multiple users to access the metaverse space simultaneously, providing a real-time collaborative experience.

[0067] The metaverse reproduction unit uses the emotion estimation function to analyze the emotions a user experiences within the metaverse space, and can provide an optimal experience. For example, the metaverse reproduction unit uses the emotion estimation function to analyze the emotions a user experiences within the metaverse space in real time. For example, if the user feels surprised, a new element is added. The metaverse reproduction unit also customizes the experience within the metaverse space based on the user's emotion data using the generation AI. For example, if the user wants to relax, a quiet environment is provided. The metaverse reproduction unit also uses the emotion estimation function to add interactive elements to the metaverse space according to the user's emotions. For example, if the user wants to have fun, a game element is added. This makes it possible to provide an optimal experience based on the user's emotions.

[0068] The VR experience unit can automatically provide additional information related to past memories. For example, the generation AI in the VR experience unit automatically provides contemporary news related to past memories. For example, it displays news from the day a photo was taken. The generation AI in the VR experience unit also automatically provides contemporary music related to past memories. For example, it plays hit songs from the year the photo was taken. The generation AI in the VR experience unit also automatically provides contemporary cultural and event information related to past memories. For example, it displays trends and events from the time the photo was taken. This allows the automatic provision of additional information related to past memories.

[0069] The VR experience unit can use the emotion estimation function to recreate memories based on the user's emotions. For example, the VR experience unit uses the emotion estimation function to analyze the user's emotions in real time and recreate memories based on those emotions. For example, if the user is feeling happy, a cheerful scene is recreated. The VR experience unit also customizes the recreation of past memories using the generation AI based on the user's emotion data. For example, if the user is feeling sad, a soothing scene is recreated. The VR experience unit also uses the emotion estimation function to add music and sound effects to the recreation of memories that correspond to the user's emotions. For example, if the user wants to relax, quiet music is provided. This allows memories to be recreated based on the user's emotions.

[0070] The VR experience unit can add a function that highlights specific events or occurrences selected by the user when recreating past memories. For example, the VR experience unit adds a function that enables the generation AI to highlight specific events or occurrences selected by the user. For example, when recreating wedding photos, important moments are highlighted. The VR experience unit also adds information related to specific events or occurrences selected by the user. For example, when recreating travel photos, detailed information about places visited is displayed. The VR experience unit also adds effects that highlight specific events or occurrences based on the user's selection by the generation AI. For example, a fireworks effect is added to birthday photos. This allows the VR experience unit to add a function that highlights specific events or occurrences.

[0071] The VR experience unit can add a biofeedback function to recreate emotions experienced by the user when recreating past memories. For example, the generation AI in the VR experience unit adds the biofeedback function to the memory recreation based on the user's past emotional data. For example, it analyzes heart rate and galvanic skin response to recreate emotions. The VR experience unit also uses the biofeedback function to recreate emotions experienced by the user in real time. For example, it recreates an increase in heart rate at the moment the user becomes excited. The generation AI in the VR experience unit also customizes the memory recreation based on the user's biofeedback data. For example, it recreates the breathing rhythm at the moment the user relaxes. This allows the biofeedback function to be added to recreate emotions experienced by the user.

[0072] The VR experience unit uses the emotion estimation function to analyze the emotions the user feels when recreating past memories and can suggest the optimal recreating method. For example, the VR experience unit uses the emotion estimation function to analyze the emotions the user feels when recreating past memories in real time. For example, if the user feels happy, it suggests a cheerful scene. The VR experience unit also uses the generation AI to suggest the optimal recreating method based on the user's emotion data. For example, if the user feels sad, it suggests a soothing scene. The VR experience unit also uses the emotion estimation function to add music and sound effects to the recreating method according to the user's emotions. For example, if the user wants to relax, it suggests quiet music. This makes it possible to suggest the optimal recreating method based on the user's emotions.

[0073] The VR experience unit performs emotional analysis in real time when sharing an experience with others, and can provide the optimal experience. For example, the generation AI in the VR experience unit performs emotional analysis in real time when sharing an experience with others, and can provide the optimal experience. For example, when exploring a virtual space with a friend, the experience is customized based on emotional data. The VR experience unit also uses emotional analysis to provide optimal music and sound effects when sharing an experience with others. For example, it automatically generates music for enjoying with friends. The VR experience unit also adds interactive elements based on emotional data when sharing an experience with others. For example, when playing a game with a friend, it adds effects according to emotions. This allows the optimal experience to be provided when sharing an experience with others.

[0074] The VR experience unit automatically generates background information for the memories to be shared when sharing the experience with others, deepening the experience. For example, the generation AI in the VR experience unit automatically generates background information for the memories to be shared when sharing the experience with others. For example, when sharing travel photos, detailed information about the places visited is displayed. The generation AI also automatically generates episodes and stories related to the memories to be shared when sharing the experience with others. For example, when sharing family photos, stories about family memories are displayed. The generation AI also adds interactive elements based on the background information for the memories to be shared when sharing the experience with others. For example, while looking at photos with friends, the events of that time are recreated. This automatically generates background information for the memories to be shared, deepening the experience.

[0075] The VR experience unit can use the emotion estimation function to analyze emotions when sharing an experience with others and suggest the optimal sharing method. For example, the VR experience unit can use the emotion estimation function to analyze emotions when sharing an experience with others in real time and suggest the optimal sharing method. For example, it can suggest scenes to enjoy with friends. The VR experience unit also suggests the optimal sharing method based on the emotion data from the generation AI when sharing an experience with others. For example, it can suggest scenes to relax with family. The VR experience unit also uses the emotion estimation function to suggest music and sound effects according to the emotions when sharing an experience with others. For example, it can suggest music to enjoy with friends. In this way, it is possible to analyze emotions when sharing an experience with others and suggest the optimal sharing method.

[0076] The VR experience unit can add interactive games and quizzes related to the memories to be shared when sharing a VR experience with others. For example, the generation AI adds interactive games related to the memories to be shared when sharing a VR experience with others. For example, while looking at travel photos, it poses quizzes about the locations. The VR experience unit also adds interactive quizzes related to the memories to be shared when sharing a VR experience with others. For example, while looking at family photos, it poses quizzes about family. The VR experience unit also generates interactive games and quizzes related to the memories to be shared in real time when sharing a VR experience with others. For example, it automatically generates games to enjoy with friends. This makes it possible to add interactive games and quizzes related to the memories to be shared.

[0077] The VR experience unit can enrich the experience by automatically generating music and videos related to the memories to be shared when sharing a VR experience with others. For example, the generation AI in the VR experience unit automatically generates music related to the memories to be shared when sharing a VR experience with others. For example, while looking at travel photos, music related to the location is played. The generation AI in the VR experience unit also automatically generates videos related to the memories to be shared when sharing a VR experience with others. For example, while looking at family photos, a video recreating the events of that time is played. The generation AI in the VR experience unit also generates music and videos related to the memories to be shared in real time when sharing a VR experience with others. For example, it automatically generates videos to enjoy with friends. This allows the automatic generation of music and videos related to the memories to be shared, enriching the experience.

[0078] The VR experience unit can use the emotion estimation function to analyze emotions when sharing an experience with others and suggest the optimal sharing method. For example, the VR experience unit can use the emotion estimation function to analyze emotions when sharing an experience with others in real time and suggest the optimal sharing method. For example, it can suggest scenes to enjoy with friends. The VR experience unit also suggests the optimal sharing method based on the emotion data from the generation AI when sharing an experience with others. For example, it can suggest scenes to relax with family. The VR experience unit also uses the emotion estimation function to suggest music and sound effects according to the emotions when sharing an experience with others. For example, it can suggest music to enjoy with friends. In this way, it is possible to analyze emotions when sharing an experience with others and suggest the optimal sharing method.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The experiential album provision metaverse system can also be equipped with a health management unit that acquires the user's health data and customizes the experience. For example, it can monitor the user's heart rate and blood pressure in real time and display a warning if an abnormality is detected during the experience. The health management unit can also suggest experiences based on the user's health condition. For example, it can provide a calming scene for a user who needs to relax. The health management unit can also provide feedback after the experience based on the user's health data. For example, it can display a graph of heart rate fluctuations during the experience and evaluate the relaxation effect. This makes it possible to provide a safe and optimal experience based on the user's health condition.

[0081] The experiential album provision metaverse system can further include a behavior analysis unit that analyzes the user's past behavioral data and customizes the experience. For example, it can analyze data on places the user has visited or events they have attended in the past and suggest experiences based on that. The behavior analysis unit can also analyze the user's interests and preferences to provide the optimal experience. For example, it can recreate scenes related to places the user has visited frequently in the past. The behavior analysis unit can also provide post-experience feedback based on the user's behavioral data. For example, it can analyze which scene the user enjoyed most and reflect that in the next experience. This makes it possible to provide a personalized experience based on the user's past behavior.

[0082] The experiential album provision metaverse system can further include an emotion analysis unit that estimates the user's emotions and customizes the experience based on the estimated emotions. For example, it can analyze the user's facial expressions and voice when selecting photos and suggest an experience based on those emotions. The emotion analysis unit can also dynamically change the scene being experienced based on the user's emotional data. For example, if the user expresses surprise, it can add a new element. The emotion analysis unit can also provide music and sound effects according to the user's emotions. For example, if the user wants to relax, it can play quiet music. This makes it possible to provide an optimal experience based on the user's emotions.

[0083] The experiential album provision metaverse system can further estimate a user's emotions and customize interactions with other users based on the estimated emotions. For example, it can analyze the emotions a user feels when sharing an experience with friends and suggest interactions based on those emotions. The emotion analysis unit can also optimize a shared experience based on the emotional data of multiple users. For example, if everyone is having fun, it can add more exciting elements. The emotion analysis unit can also suggest communication methods based on the user's emotions. For example, if a user wants to relax, it can encourage calm conversation. This makes it possible to optimize interactions with other users based on emotions.

[0084] The experiential album provision metaverse system can further estimate the user's emotions and provide post-experience feedback based on the estimated emotions. For example, it can analyze the emotions the user expressed during the experience and provide feedback based on those emotions. The emotion analysis unit can also suggest the next experience based on the user's emotional data. For example, it can customize the next experience based on the scenes the user enjoyed. The emotion analysis unit can also provide advice based on the user's emotions. For example, if relaxation is needed, it can suggest an experience that has a relaxing effect. This makes it possible to provide feedback and suggestions for the next experience based on the user's emotions.

[0085] The experiential album providing metaverse system can further provide historical background information related to the user's past memories. For example, it can display historical events and cultural background related to the time when a photo selected by the user was taken. The historical background information section can also provide detailed explanations of the history and culture of places the user has visited. For example, it can introduce historical landmarks and cultural events related to photos of travel destinations. The historical background information section can also provide information tailored to the user's interests. For example, it can display information related to historical themes that interest the user. This allows for the provision of a wealth of historical background information related to the user's past memories.

[0086] The experiential album providing metaverse system can further provide future prediction information related to the user's past memories. For example, it displays future prediction information related to the location or event of a photo selected by the user. The future prediction information unit can also introduce future developments and plans for places visited by the user. For example, it can provide information on urban redevelopment plans and new tourist attractions. The future prediction information unit can also provide information on future trends and technologies according to the user's interests. For example, it can display future predictions for fields in which the user is interested. This makes it possible to provide future prediction information related to the user's past memories.

[0087] The experiential album providing metaverse system can further provide educational information related to the user's past memories. For example, it can display scientific knowledge or educational information related to a photo selected by the user. The educational information section can also provide information about the natural environment and ecosystem of a place visited by the user. For example, it can introduce information about the flora and fauna related to a photo of a natural park. The educational information section can also provide learning content according to the user's interests. For example, it can display information about science or history that interests the user. This makes it possible to provide educational information related to the user's past memories.

[0088] The experiential album provision metaverse system can also generate artwork related to the user's past memories. For example, AI generates artwork based on photos selected by the user and displays it in the metaverse space. The art generation unit can also suggest artwork based on themes related to the user's memories. For example, it can generate paintings or sculptures based on family photos. The art generation unit can also select an art style based on the user's interests. For example, it can generate artwork by selecting a style such as impressionism or abstract painting. This makes it possible to provide unique artwork related to the user's past memories.

[0089] The experiential album provision metaverse system can also generate music playlists related to the user's past memories. For example, a playlist can be created based on music that was popular around the time the user selected photos were taken. The music playlist generation unit can also suggest music based on themes related to the user's memories. For example, relaxing music related to travel photos can be provided. The music playlist generation unit can also select music genres according to the user's interests. For example, a playlist can be created by selecting genres such as classical or pop. This makes it possible to provide a music playlist related to the user's past memories.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The 2D photo acquisition unit acquires 2D photos from the user. For example, it acquires JPEG or PNG photos uploaded by the user. It can also acquire photos taken by the user directly with a smartphone or camera. It can also acquire photos from cloud storage. For example, it acquires photos saved by the user in Google Photos or Dropbox. Step 2: The 3D conversion unit converts the 2D photos acquired by the 2D photo acquisition unit into 3D. For example, the generation AI can analyze the depth of the 2D photos using a depth estimation algorithm and generate a 3D model. It can also convert 2D photos into 3D using stereo vision technology or machine learning models. It can also receive multiple 2D photos as input and generate a 3D model from them. Step 3: The Metaverse Reproduction Unit reproduces the 3D photo converted by the 3D Conversion Unit in the Metaverse space. For example, the Generative AI can place the 3D photo on a virtual reality platform so that users can access it using a VR device. It can also intuitively place the 3D photo taking into account the characteristics of the user interface. Step 4: The VR experience unit allows the user to experience the 3D photo reproduced by the metaverse reproduction unit through a VR device. For example, the 3D photo is displayed to the user using a head-mounted display. It is also possible to allow the user to touch the 3D photo using VR gloves. Furthermore, an explanation of the 3D photo can be provided to the user using an audio guide.

[0092] 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.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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. [Explanation of symbols]

[0159] 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 2D photo acquisition unit that acquires a 2D photo from a user; a 3D conversion unit that converts the 2D photo acquired by the 2D photo acquisition unit into a 3D photo; a metaverse reproduction unit that reproduces the 3D photo converted by the 3D conversion unit in a metaverse space; a VR experience unit that allows a user to experience the 3D photo reproduced by the metaverse reproduction unit through a VR device. A system characterized by:

2. The 3D conversion unit Automatically complements the background information of the 2D photo to generate a more detailed 3D model 2. The system of claim 1.

3. The 3D conversion unit Estimate the movement of the person in the 2D photo and generate a dynamic 3D model 2. The system of claim 1.

4. The 3D conversion unit Analyze the emotions of the person in the 2D photo and generate a 3D model based on that emotion 2. The system of claim 1.

5. The 3D conversion unit Estimating audio information from the 2D photograph and adding audio to the 3D model 2. The system of claim 1.

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