system

Abstract

The system according to this embodiment aims to convert 2D photographs into 3D images and allow them to be experienced in a metaverse space. [Solution] The system according to the embodiment comprises a reception unit, a conversion unit, a reproduction unit, and an experience unit. The reception unit uploads a 2D photograph. The conversion unit analyzes the 2D photograph uploaded by the reception unit and converts it into 3D. The reproduction unit reproduces the 3D photograph converted by the conversion unit in a metaverse space. The experience unit experiences the metaverse space reproduced by the reproduction unit via a VR device.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is not easy to convert a 2D photo into 3D and experience it in the metaverse space.

[0005] The system according to the embodiment aims to convert a 2D photo into 3D and experience it in the metaverse space.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a conversion unit, a reproduction unit, and an experience unit. The reception unit uploads a 2D photograph. The conversion unit analyzes the 2D photograph uploaded by the reception unit and converts it into 3D. The reproduction unit reproduces the 3D photograph converted by the conversion unit in a metaverse space. The experience unit experiences the metaverse space reproduced by the reproduction unit through a VR device. [Effects of the Invention]

[0007] The system according to this embodiment can convert 2D photographs into 3D images, which can then be experienced in a metaverse space. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The experiential album-providing metaverse system according to an embodiment of the present invention is a system that allows users to realistically experience past memories via a VR device by converting 2D photos from an album into 3D photos using AI and then reproducing them in a metaverse space. In this system, users upload 2D photos from an album, the AI ​​analyzes the 2D photos and converts them into 3D photos, and the converted 3D photos are reproduced in a metaverse space, allowing users to experience that space via a VR device. For example, users can upload various memorable photos, such as family trip photos or graduation photos. These photos are input to the AI, which analyzes each element of the photo and reproduces depth and three-dimensionality. For example, it generates 3D photos by considering the position of people and the perspective of the background. The generated 3D photos are reproduced in a metaverse space, and users can experience that space by wearing a VR device. For example, family trip photos can be made 3D, allowing users to experience them as if they were actually there. Furthermore, this system can also be used to share experiences with others. For example, graduation photos can be made 3D with friends and experienced together in the metaverse space. This allows users to share not only memories but also experiences. In this way, the experiential photo album metaverse system not only allows users to experience past memories more vividly, but also enables them to share experiences with others through the photos they share. As a result, the experiential photo album metaverse system allows users to realistically experience past memories and share them with others.

[0029] The experiential album-providing metaverse system according to this embodiment comprises a reception unit, a conversion unit, a reproduction unit, and an experience unit. The reception unit is the part for users to upload 2D photos for their albums. For example, users can upload various memorable photos, such as family trip photos or graduation photos. The reception unit inputs the uploaded 2D photos into the AI. The conversion unit is the part that uses the AI ​​to analyze the uploaded 2D photos and convert them into 3D photos. For example, the conversion unit analyzes each element of the photo and reproduces depth and three-dimensionality. The conversion unit generates a 3D photo, taking into account, for example, the position of people and the perspective of the background. The reproduction unit is the part that reproduces the 3D photo converted by the conversion unit in the metaverse space. For example, the reproduction unit places the generated 3D photo in the metaverse space so that the user can experience that space. The experience unit is the part for experiencing the metaverse space reproduced by the reproduction unit via a VR device. For example, the experience unit allows the user to experience the metaverse space by wearing a VR device. As a result, the experiential album-providing metaverse system according to this embodiment allows users to realistically experience past memories and share them with others.

[0030] The experiential photo album metaverse system includes a shared section for sharing experiences with others. The shared section is a part for sharing experiences with others. For example, the shared section allows users to create 3D versions of graduation photos with friends and experience them together in the metaverse space. The shared section has the functionality to share experiences in real time. For example, the shared section can share the metaverse space a user is experiencing with others in real time. The shared section also has the functionality to share recorded experiences. For example, the shared section can record the metaverse space a user has experienced and share that recording with others. In this way, the experiential photo album metaverse system allows users to share experiences with others.

[0031] The experiential album-providing metaverse system includes a quality enhancement unit to improve the quality of 3D photographs. This unit is responsible for improving the quality of 3D photographs. For example, it can improve the quality of 3D photographs using image correction technology. This includes, for instance, increasing resolution and adjusting colors. The quality enhancement unit can also improve the quality of 3D photographs using noise reduction technology. For example, it can remove noise from 3D photographs to produce sharper images. Furthermore, the quality enhancement unit can improve the quality of 3D photographs using detail enhancement technology. For example, it can enhance the details of 3D photographs to produce more realistic images. Thus, the experiential album-providing metaverse system can improve the quality of 3D photographs.

[0032] The conversion unit can analyze each element of a photograph and reproduce depth or three-dimensionality. For example, the conversion unit analyzes each element of a photograph and reproduces depth and three-dimensionality. For example, the conversion unit generates a 3D photograph by considering the position of people and the perspective of the background. The conversion unit can also reproduce depth using depth map generation technology. For example, the conversion unit calculates the depth of each element of the photograph and generates a depth map. Furthermore, the conversion unit can reproduce three-dimensionality using 3D model construction technology. For example, the conversion unit constructs each element of the photograph as a 3D model and reproduces three-dimensionality. In this way, the conversion unit can generate more realistic 3D photographs by reproducing the depth and three-dimensionality of the photograph.

[0033] The reproduction unit can reproduce 3D photographs in the metaverse space. For example, the reproduction unit can reproduce 3D photographs in the metaverse space. For example, the reproduction unit can place the generated 3D photographs in the metaverse space, allowing the user to experience that space. The reproduction unit can also reproduce 3D photographs using rendering technology. For example, the reproduction unit can render 3D photographs at high resolution and display them in the metaverse space. Furthermore, the reproduction unit can reproduce 3D photographs by adjusting the display method. For example, the reproduction unit can display 3D photographs according to the user's viewpoint, providing a more realistic experience. In this way, the reproduction unit allows users to experience the space by reproducing 3D photographs in the metaverse space.

[0034] The immersive unit allows users to experience the metaverse space via a VR device. For example, the immersive unit allows users to experience the metaverse space by wearing a VR device. The immersive unit can also adjust the method of experience depending on the type of VR device. For example, the immersive unit displays the metaverse space using a head-mounted display (HMD), allowing the user to experience that space. The immersive unit can also adjust the details of the experience. For example, the immersive unit adjusts the visual and auditory information of the metaverse space to provide a more realistic experience. In this way, the immersive unit allows users to realistically experience past memories by experiencing the metaverse space via a VR device.

[0035] The reception desk can analyze a user's past upload history and select the appropriate upload method. For example, the reception desk can prioritize suggesting upload methods that the user has frequently used in the past (such as drag and drop or file selection). Furthermore, the reception desk can analyze the user's past upload history to identify upload trends at specific times and prompt uploads during those times. In addition, the reception desk can analyze the types and themes of photos the user has uploaded in the past and suggest the best method for uploading similar photos. Thus, by analyzing a user's past upload history, the reception desk can suggest the optimal upload method.

[0036] The reception system can filter photos uploaded based on the user's current projects and areas of interest. For example, it can filter photos to only include those related to a project the user is currently working on. It can also filter photos based on the user's areas of interest (travel, sports, family, etc.) to prioritize relevant photos. Furthermore, when a user uploads photos related to a specific event (birthday, wedding, etc.), the reception system can filter to include only those related to that event. This allows the reception system to upload more relevant photos by filtering them based on the user's current projects and areas of interest.

[0037] The reception system can prioritize uploading photos that are highly relevant to the user's geographical location when they upload photos. For example, the reception system can prioritize uploading photos related to the user's current location. It can also prioritize uploading photos related to places the user has visited in the past. Furthermore, it can prioritize uploading photos related to places the user plans to visit in the future. In this way, the reception system can prioritize uploading highly relevant photos by taking the user's geographical location into consideration.

[0038] The reception desk can analyze a user's social media activity when they upload photos and upload relevant photos. For example, the reception desk can prioritize uploading photos related to photos the user has recently shared on social media. It can also prioritize uploading photos related to accounts the user follows on social media. Furthermore, it can prioritize uploading photos related to events the user has participated in on social media. In this way, the reception desk can prioritize uploading relevant photos by analyzing the user's social media activity.

[0039] The conversion unit can adjust the accuracy of the conversion based on the importance of the photos during the conversion process. For example, the conversion unit can perform detailed 3D conversions to reproduce even the smallest details for photos that the user considers particularly important. It can also perform simplified 3D conversions for photos that the user considers less important. Furthermore, the conversion unit can perform detailed 3D conversions to reproduce the atmosphere of events related to specific events (such as weddings or graduations). This allows the conversion unit to perform more detailed 3D conversions on more important photos by adjusting the accuracy of the conversion based on their importance.

[0040] The conversion unit can apply different conversion algorithms depending on the category of the photograph during conversion. For example, the conversion unit can apply an algorithm that emphasizes the natural sense of depth to landscape photographs. It can also apply an algorithm that reproduces facial expressions and body movements to portraits. Furthermore, it can apply an algorithm that reproduces the structure and details of buildings to building photographs. By applying different conversion algorithms depending on the category of the photograph, the conversion unit can achieve more appropriate 3D conversion.

[0041] The conversion unit can determine the conversion priority based on when the photos were taken. For example, the conversion unit prioritizes 3D conversion of recently taken photos. It can also prioritize 3D conversion of photos related to specific events (such as weddings or graduations). Furthermore, the conversion unit can prioritize 3D conversion of past photos that the user considers particularly important. In this way, the conversion unit can prioritize 3D conversion of more important photos by determining the conversion priority based on when the photos were taken.

[0042] The conversion unit can adjust the order of conversions based on the relevance of the photos during the conversion process. For example, the conversion unit can 3D convert photos related to the same event consecutively. It can also prioritize 3D conversions of photos related to a specific theme (such as travel or family) used by the user. Furthermore, it can prioritize 3D conversions of photos related to a specific location used by the user. This allows the conversion unit to perform more consistent 3D conversions by adjusting the order of conversions based on the relevance of the photos.

[0043] The reproduction unit can improve the accuracy of the reproduction based on the relationships between the photographs during the reproduction process. For example, the reproduction unit can reproduce a sequence of photographs related to the same event to recreate the flow of the event. The reproduction unit can also reproduce a sequence of photographs related to a specific theme (travel, family, etc.) to maintain thematic consistency. Furthermore, the reproduction unit can reproduce a sequence of photographs related to a specific location to recreate the atmosphere of that location. In this way, the reproduction unit can reproduce a more consistent metaverse space by improving the accuracy of the reproduction based on the relationships between the photographs.

[0044] The reproduction unit can perform reproductions based on the attribute information of the photographer. For example, if the photographer is a family member, the reproduction unit can reproduce the family atmosphere. If the photographer is a professional photographer, the reproduction unit can also reproduce a professional atmosphere. Furthermore, if the photographer is a friend, the reproduction unit can reproduce a fun atmosphere with a friend. In this way, by considering the attribute information of the photographer, the reproduction unit can reproduce a more appropriate metaverse space.

[0045] The reproduction unit can perform reproductions based on the geographical distribution of the photographs. For example, the reproduction unit can reproduce a sequence of photographs taken in the same location to recreate the atmosphere of that location. It can also reproduce photographs in the order in which the user visited places to recreate the flow of a trip. Furthermore, the reproduction unit can reproduce a sequence of photographs related to a specific region to recreate the atmosphere of that region. By considering the geographical distribution of the photographs, the reproduction unit can reproduce a more consistent metaverse space.

[0046] The reproduction unit can improve the accuracy of its reproduction based on relevant literature related to the photograph. For example, the reproduction unit can refer to the historical background related to the photograph and reproduce that background. It can also refer to cultural information related to the photograph and reproduce that culture. Furthermore, the reproduction unit can refer to geographical information related to the photograph and reproduce its geographical features. As a result, the reproduction unit can reproduce a more accurate metaverse space by referring to relevant literature related to the photograph.

[0047] The interactive unit can analyze the user's past experience history during an interactive session to select the optimal interactive method. For example, the interactive unit can prioritize providing methods that the user has enjoyed in the past. Furthermore, based on the user's past experience history, the interactive unit can suggest interactive methods related to specific themes or events. In addition, the interactive unit can prioritize providing methods that the user has experienced in the past that have provided particularly high satisfaction. In this way, the interactive unit can provide the optimal interactive method by analyzing the user's past experience history.

[0048] The sensory unit can customize the sensory experience based on the user's current lifestyle. For example, if the user is currently relaxed, the sensory unit can provide a gentle sensory experience. If the user is currently busy, the sensory unit can also provide a shorter sensory experience. Furthermore, if the user is currently stressed, the sensory unit can provide a sensory experience that reduces stress. In this way, the sensory unit can provide a more appropriate sensory experience by customizing the sensory experience based on the user's current lifestyle.

[0049] The sensory unit can select an appropriate sensory experience based on the user's geographical location information during the user's experience. For example, the sensory unit can provide a sensory experience related to the user's current location. It can also provide a sensory experience related to places the user has visited in the past. Furthermore, it can provide a sensory experience related to places the user plans to visit in the future. In this way, the sensory unit can provide the optimal sensory experience by taking the user's geographical location information into consideration.

[0050] The experience unit can analyze the user's social media activity during the experience and propose ways to enhance the experience. For example, the experience unit can provide experiences related to experiences the user has recently shared on social media. It can also provide experiences related to accounts the user follows on social media. Furthermore, it can provide experiences related to events the user is participating in on social media. In this way, the experience unit can provide the optimal experience by analyzing the user's social media activity.

[0051] The sharing function can analyze a user's past sharing history to select the optimal sharing method. For example, it can prioritize suggesting sharing methods that the user has frequently used in the past (email, social media, etc.). It can also analyze a user's past sharing history to identify sharing tendencies at specific times and encourage sharing during those times. Furthermore, it can suggest methods related to content the user has shared in the past. In this way, the sharing function can provide the optimal sharing method by analyzing a user's past sharing history.

[0052] The sharing function can select an appropriate sharing method based on the user's geographical location information when sharing. For example, the sharing function can share experiences related to the user's current location. It can also share experiences related to places the user has visited in the past. Furthermore, it can share experiences related to places the user plans to visit in the future. In this way, the sharing function can provide the optimal sharing method by taking the user's geographical location information into consideration.

[0053] The Quality Improvement Department can analyze a user's past photo history to select the optimal quality improvement method during the improvement process. For example, the Quality Improvement Department can prioritize suggesting filters and effects that the user has previously preferred. Furthermore, the Quality Improvement Department can suggest quality improvement methods related to specific themes or events based on the user's past photo history. In addition, the Quality Improvement Department can prioritize suggesting methods that the user has used in the past and found particularly satisfying. This allows the Quality Improvement Department to provide the optimal quality improvement method by analyzing the user's past photo history.

[0054] The Quality Improvement Department can select the appropriate quality improvement method based on the user's geographical location information when improving quality. For example, the Quality Improvement Department can improve the experience related to the user's current location. It can also improve the experience related to places the user has visited in the past. Furthermore, it can improve the experience related to places the user plans to visit in the future. In this way, the Quality Improvement Department can provide the optimal quality improvement method by considering the user's geographical location information.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] An experiential album-providing metaverse system can also include a history analysis unit that analyzes the user's past experience history and proposes the most suitable way to experience the content. For example, the history analysis unit can prioritize suggesting methods that the user has enjoyed in the past. Furthermore, the history analysis unit can also suggest methods related to specific themes or events based on the user's past experience history. In addition, the history analysis unit can prioritize suggesting methods that the user found particularly satisfying among their past experiences. This allows the history analysis unit to provide the most optimal experience by analyzing the user's past experience history.

[0057] An experiential album-providing metaverse system can also include a lifestyle-adapting unit that customizes the means of experience based on the user's current lifestyle. For example, if the user is currently relaxed, the lifestyle-adapting unit can provide a gentle experience. If the user is currently busy, it can also provide a short-duration experience. Furthermore, if the user is currently stressed, it can provide an experience that reduces stress. In this way, the lifestyle-adapting unit can customize the means of experience based on the user's current lifestyle, enabling a more appropriate experience.

[0058] An experiential photo album metaverse system can also include a geographic information adaptation unit that selects an appropriate experience method based on the user's geographic location. For example, the geographic information adaptation unit can provide an experience method related to the user's current location. It can also provide an experience method related to places the user has visited in the past. Furthermore, it can provide an experience method related to places the user plans to visit in the future. In this way, the geographic information adaptation unit can provide the optimal experience method by taking the user's geographic location into consideration.

[0059] An experiential album-providing metaverse system can also include a social media analysis unit that analyzes users' social media activity and suggests ways to experience the content. For example, the social media analysis unit can provide experiences related to experiences the user has recently shared on social media. It can also provide experiences related to accounts the user follows on social media. Furthermore, it can provide experiences related to events the user is participating in on social media. In this way, the social media analysis unit can provide the most optimal experience by analyzing the user's social media activity.

[0060] An experiential album-providing metaverse system can also include a sharing history analysis unit that analyzes a user's past sharing history to select the optimal sharing method. For example, the sharing history analysis unit can prioritize suggesting sharing methods that the user has frequently used in the past (email, social media, etc.). Furthermore, the sharing history analysis unit can analyze the user's past sharing history to identify sharing tendencies at specific times and encourage sharing during those times. In addition, the sharing history analysis unit can suggest methods related to content the user has shared in the past. In this way, the sharing history analysis unit can provide the optimal sharing method by analyzing the user's past sharing history.

[0061] An experiential album-providing metaverse system can also include a geographic information sharing unit that selects the appropriate sharing method based on the user's geographic location. For example, the geographic information sharing unit can share experiences related to the user's current location. It can also share experiences related to places the user has visited in the past. Furthermore, it can share experiences related to places the user plans to visit in the future. In this way, the geographic information sharing unit can provide the optimal sharing method by taking the user's geographic location into consideration.

[0062] An experiential photo album metaverse system can also include a photo history analysis unit that analyzes the user's past photo history to select the optimal quality improvement method. For example, the photo history analysis unit can prioritize suggesting filters and effects that the user has previously preferred to use. It can also suggest quality improvement methods related to specific themes or events based on the user's past photo history. Furthermore, it can prioritize suggesting methods that the user has used in the past that have provided particularly high satisfaction. In this way, the photo history analysis unit can provide the optimal quality improvement method by analyzing the user's past photo history.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception area is where users upload 2D photos from their albums. For example, users can upload various memorable photos, such as family trip photos or graduation photos. The reception area inputs the uploaded 2D photos into the AI. Step 2: The conversion unit uses AI to analyze the uploaded 2D photo and convert it into a 3D photo. For example, the conversion unit analyzes each element of the photo and reproduces depth and three-dimensionality. The conversion unit generates a 3D photo by considering factors such as the position of the person and the perspective of the background. Step 3: The reproduction unit is the part that reproduces the 3D image converted by the transformation unit in the metaverse space. For example, the reproduction unit places the generated 3D image in the metaverse space, allowing the user to experience that space. Step 4: The tactile section is the part that allows users to experience the metaverse space reproduced by the reproduction section via a VR device. For example, the tactile section allows users to experience the metaverse space by wearing a VR device.

[0065] (Example of form 2) The experiential album-providing metaverse system according to an embodiment of the present invention is a system that allows users to realistically experience past memories via a VR device by converting 2D photos from an album into 3D photos using AI and then reproducing them in a metaverse space. In this system, users upload 2D photos from an album, the AI ​​analyzes the 2D photos and converts them into 3D photos, and the converted 3D photos are reproduced in a metaverse space, allowing users to experience that space via a VR device. For example, users can upload various memorable photos, such as family trip photos or graduation photos. These photos are input to the AI, which analyzes each element of the photo and reproduces depth and three-dimensionality. For example, it generates 3D photos by considering the position of people and the perspective of the background. The generated 3D photos are reproduced in a metaverse space, and users can experience that space by wearing a VR device. For example, family trip photos can be made 3D, allowing users to experience them as if they were actually there. Furthermore, this system can also be used to share experiences with others. For example, graduation photos can be made 3D with friends and experienced together in the metaverse space. This allows users to share not only memories but also experiences. In this way, the experiential photo album metaverse system not only allows users to experience past memories more vividly, but also enables them to share experiences with others through the photos they share. As a result, the experiential photo album metaverse system allows users to realistically experience past memories and share them with others.

[0066] The experiential album-providing metaverse system according to this embodiment comprises a reception unit, a conversion unit, a reproduction unit, and an experience unit. The reception unit is the part for users to upload 2D photos for their albums. For example, users can upload various memorable photos, such as family trip photos or graduation photos. The reception unit inputs the uploaded 2D photos into the AI. The conversion unit is the part that uses the AI ​​to analyze the uploaded 2D photos and convert them into 3D photos. For example, the conversion unit analyzes each element of the photo and reproduces depth and three-dimensionality. The conversion unit generates a 3D photo, taking into account, for example, the position of people and the perspective of the background. The reproduction unit is the part that reproduces the 3D photo converted by the conversion unit in the metaverse space. For example, the reproduction unit places the generated 3D photo in the metaverse space so that the user can experience that space. The experience unit is the part for experiencing the metaverse space reproduced by the reproduction unit via a VR device. For example, the experience unit allows the user to experience the metaverse space by wearing a VR device. As a result, the experiential album-providing metaverse system according to this embodiment allows users to realistically experience past memories and share them with others.

[0067] The experiential photo album metaverse system includes a shared section for sharing experiences with others. The shared section is a part for sharing experiences with others. For example, the shared section allows users to create 3D versions of graduation photos with friends and experience them together in the metaverse space. The shared section has the functionality to share experiences in real time. For example, the shared section can share the metaverse space a user is experiencing with others in real time. The shared section also has the functionality to share recorded experiences. For example, the shared section can record the metaverse space a user has experienced and share that recording with others. In this way, the experiential photo album metaverse system allows users to share experiences with others.

[0068] The experiential album-providing metaverse system includes a quality enhancement unit to improve the quality of 3D photographs. This unit is responsible for improving the quality of 3D photographs. For example, it can improve the quality of 3D photographs using image correction technology. This includes, for instance, increasing resolution and adjusting colors. The quality enhancement unit can also improve the quality of 3D photographs using noise reduction technology. For example, it can remove noise from 3D photographs to produce sharper images. Furthermore, the quality enhancement unit can improve the quality of 3D photographs using detail enhancement technology. For example, it can enhance the details of 3D photographs to produce more realistic images. Thus, the experiential album-providing metaverse system can improve the quality of 3D photographs.

[0069] The conversion unit can analyze each element of a photograph and reproduce depth or three-dimensionality. For example, the conversion unit analyzes each element of a photograph and reproduces depth and three-dimensionality. For example, the conversion unit generates a 3D photograph by considering the position of people and the perspective of the background. The conversion unit can also reproduce depth using depth map generation technology. For example, the conversion unit calculates the depth of each element of the photograph and generates a depth map. Furthermore, the conversion unit can reproduce three-dimensionality using 3D model construction technology. For example, the conversion unit constructs each element of the photograph as a 3D model and reproduces three-dimensionality. In this way, the conversion unit can generate more realistic 3D photographs by reproducing the depth and three-dimensionality of the photograph.

[0070] The reproduction unit can reproduce 3D photographs in the metaverse space. For example, the reproduction unit can reproduce 3D photographs in the metaverse space. For example, the reproduction unit can place the generated 3D photographs in the metaverse space, allowing the user to experience that space. The reproduction unit can also reproduce 3D photographs using rendering technology. For example, the reproduction unit can render 3D photographs at high resolution and display them in the metaverse space. Furthermore, the reproduction unit can reproduce 3D photographs by adjusting the display method. For example, the reproduction unit can display 3D photographs according to the user's viewpoint, providing a more realistic experience. In this way, the reproduction unit allows users to experience the space by reproducing 3D photographs in the metaverse space.

[0071] The immersive unit allows users to experience the metaverse space via a VR device. For example, the immersive unit allows users to experience the metaverse space by wearing a VR device. The immersive unit can also adjust the method of experience depending on the type of VR device. For example, the immersive unit displays the metaverse space using a head-mounted display (HMD), allowing the user to experience that space. The immersive unit can also adjust the details of the experience. For example, the immersive unit adjusts the visual and auditory information of the metaverse space to provide a more realistic experience. In this way, the immersive unit allows users to realistically experience past memories by experiencing the metaverse space via a VR device.

[0072] The reception desk can estimate the user's emotions and adjust the timing of photo uploads based on those emotions. For example, if the emotion engine indicates that the user is stressed, the reception desk can delay the upload to allow the user to upload in a relaxed state. Alternatively, if the emotion engine indicates that the user is excited, the reception desk can prompt the user to upload immediately, allowing them to upload while their emotions are heightened. Furthermore, if the emotion engine indicates that the user is sad, the reception desk can adjust the upload timing to allow the user to upload after their emotions have calmed down. In this way, the reception desk can upload photos at a more appropriate time by adjusting the timing of photo uploads according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The reception desk can analyze a user's past upload history and select the appropriate upload method. For example, the reception desk can prioritize suggesting upload methods that the user has frequently used in the past (such as drag and drop or file selection). Furthermore, the reception desk can analyze the user's past upload history to identify upload trends at specific times and prompt uploads during those times. In addition, the reception desk can analyze the types and themes of photos the user has uploaded in the past and suggest the best method for uploading similar photos. Thus, by analyzing a user's past upload history, the reception desk can suggest the optimal upload method.

[0074] The reception system can filter photos uploaded based on the user's current projects and areas of interest. For example, it can filter photos to only include those related to a project the user is currently working on. It can also filter photos based on the user's areas of interest (travel, sports, family, etc.) to prioritize relevant photos. Furthermore, when a user uploads photos related to a specific event (birthday, wedding, etc.), the reception system can filter to include only those related to that event. This allows the reception system to upload more relevant photos by filtering them based on the user's current projects and areas of interest.

[0075] The reception desk can estimate the user's emotions and determine the priority of photos to upload based on the estimated emotions. For example, if the emotion engine indicates that the user is happy, the reception desk will prioritize uploading photos of happy memories. Similarly, if the emotion engine indicates that the user is sad, the reception desk can prioritize uploading photos that soothe those emotions. Furthermore, if the emotion engine indicates that the user is excited, the reception desk can prioritize uploading exciting photos that match that emotion. This allows the reception desk to upload more appropriate photos by prioritizing photos according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The reception system can prioritize uploading photos that are highly relevant to the user's geographical location when they upload photos. For example, the reception system can prioritize uploading photos related to the user's current location. It can also prioritize uploading photos related to places the user has visited in the past. Furthermore, it can prioritize uploading photos related to places the user plans to visit in the future. In this way, the reception system can prioritize uploading highly relevant photos by taking the user's geographical location into consideration.

[0077] The reception desk can analyze a user's social media activity when they upload photos and upload relevant photos. For example, the reception desk can prioritize uploading photos related to photos the user has recently shared on social media. It can also prioritize uploading photos related to accounts the user follows on social media. Furthermore, it can prioritize uploading photos related to events the user has participated in on social media. In this way, the reception desk can prioritize uploading relevant photos by analyzing the user's social media activity.

[0078] The transformation unit can estimate the user's emotions and adjust the 3D transformation's presentation based on the estimated emotions. For example, if the emotion engine indicates that the user is relaxed, the transformation unit can use soft colors and smooth animation for the 3D transformation. Alternatively, if the emotion engine indicates that the user is excited, the transformation unit can use vibrant colors and dynamic animation for the 3D transformation. Furthermore, if the emotion engine indicates that the user is sad, the transformation unit can use calm colors and gentle animation for the 3D transformation. This allows the transformation unit to generate more appropriate 3D images by adjusting the 3D transformation's presentation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The conversion unit can adjust the accuracy of the conversion based on the importance of the photos during the conversion process. For example, the conversion unit can perform detailed 3D conversions to reproduce even the smallest details for photos that the user considers particularly important. It can also perform simplified 3D conversions for photos that the user considers less important. Furthermore, the conversion unit can perform detailed 3D conversions to reproduce the atmosphere of events related to specific events (such as weddings or graduations). This allows the conversion unit to perform more detailed 3D conversions on more important photos by adjusting the accuracy of the conversion based on their importance.

[0080] The conversion unit can apply different conversion algorithms depending on the category of the photograph during conversion. For example, the conversion unit can apply an algorithm that emphasizes the natural sense of depth to landscape photographs. It can also apply an algorithm that reproduces facial expressions and body movements to portraits. Furthermore, it can apply an algorithm that reproduces the structure and details of buildings to building photographs. By applying different conversion algorithms depending on the category of the photograph, the conversion unit can achieve more appropriate 3D conversion.

[0081] The transformation unit can estimate the user's emotions and adjust the length of the 3D transformation based on the estimated emotions. For example, if the user is in a hurry according to the emotion engine, the transformation unit will complete the 3D transformation in a short time. Conversely, if the user is relaxed according to the emotion engine, the transformation unit can perform the 3D transformation at a slower pace. Furthermore, if the user is excited according to the emotion engine, the transformation unit can perform the 3D transformation with dynamic effects. This allows the transformation unit to perform a more appropriate 3D transformation by adjusting the length of the 3D transformation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The conversion unit can determine the conversion priority based on when the photos were taken. For example, the conversion unit prioritizes 3D conversion of recently taken photos. It can also prioritize 3D conversion of photos related to specific events (such as weddings or graduations). Furthermore, the conversion unit can prioritize 3D conversion of past photos that the user considers particularly important. In this way, the conversion unit can prioritize 3D conversion of more important photos by determining the conversion priority based on when the photos were taken.

[0083] The conversion unit can adjust the order of conversions based on the relevance of the photos during the conversion process. For example, the conversion unit can 3D convert photos related to the same event consecutively. It can also prioritize 3D conversions of photos related to a specific theme (such as travel or family) used by the user. Furthermore, it can prioritize 3D conversions of photos related to a specific location used by the user. This allows the conversion unit to perform more consistent 3D conversions by adjusting the order of conversions based on the relevance of the photos.

[0084] The reproduction unit can estimate the user's emotions and adjust the method of reproducing the metaverse space based on the estimated user emotions. For example, if the user is relaxed according to the emotion engine, the reproduction unit will reproduce a metaverse space with calm music and soft colors. If the user is excited according to the emotion engine, the reproduction unit can also reproduce a metaverse space with vivid colors and dynamic effects. Furthermore, if the user is sad according to the emotion engine, the reproduction unit can also reproduce a metaverse space with calm colors and gentle music. In this way, the reproduction unit can adjust the method of reproducing the metaverse space according to the user's emotions, enabling a more appropriate experience. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0085] The reproduction unit can improve the accuracy of the reproduction based on the relationships between the photographs during the reproduction process. For example, the reproduction unit can reproduce a sequence of photographs related to the same event to recreate the flow of the event. The reproduction unit can also reproduce a sequence of photographs related to a specific theme (travel, family, etc.) to maintain thematic consistency. Furthermore, the reproduction unit can reproduce a sequence of photographs related to a specific location to recreate the atmosphere of that location. In this way, the reproduction unit can reproduce a more consistent metaverse space by improving the accuracy of the reproduction based on the relationships between the photographs.

[0086] The reproduction unit can perform reproductions based on the attribute information of the photographer. For example, if the photographer is a family member, the reproduction unit can reproduce the family atmosphere. If the photographer is a professional photographer, the reproduction unit can also reproduce a professional atmosphere. Furthermore, if the photographer is a friend, the reproduction unit can reproduce a fun atmosphere with a friend. In this way, by considering the attribute information of the photographer, the reproduction unit can reproduce a more appropriate metaverse space.

[0087] The display unit can estimate the user's emotions and adjust the display order of the reproduced metaverse space based on the estimated user emotions. For example, if the user is relaxed according to the emotion engine, the display unit will display scenes in order from calm to serene. If the user is excited according to the emotion engine, the display unit can also display scenes in order from dynamic to serene. Furthermore, if the user is sad according to the emotion engine, the display unit can also display scenes in order from calm to serene. In this way, the display unit can adjust the display order of the metaverse space according to the user's emotions, enabling a more appropriate experience. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The reproduction unit can perform reproductions based on the geographical distribution of the photographs. For example, the reproduction unit can reproduce a sequence of photographs taken in the same location to recreate the atmosphere of that location. It can also reproduce photographs in the order in which the user visited places to recreate the flow of a trip. Furthermore, the reproduction unit can reproduce a sequence of photographs related to a specific region to recreate the atmosphere of that region. By considering the geographical distribution of the photographs, the reproduction unit can reproduce a more consistent metaverse space.

[0089] The reproduction unit can improve the accuracy of its reproduction based on relevant literature related to the photograph. For example, the reproduction unit can refer to the historical background related to the photograph and reproduce that background. It can also refer to cultural information related to the photograph and reproduce that culture. Furthermore, the reproduction unit can refer to geographical information related to the photograph and reproduce its geographical features. As a result, the reproduction unit can reproduce a more accurate metaverse space by referring to relevant literature related to the photograph.

[0090] The sensory unit can estimate the user's emotions and adjust the sensory experience based on those emotions. For example, if the user is relaxed according to the emotion engine, the sensory unit can provide a sensory space with calming music and soft colors. If the user is excited according to the emotion engine, the sensory unit can also provide a sensory space with vibrant colors and dynamic effects. Furthermore, if the user is sad according to the emotion engine, the sensory unit can provide a sensory space with calming colors and gentle music. In this way, the sensory unit can provide a more appropriate sensory experience by adjusting the sensory experience according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The interactive unit can analyze the user's past experience history during an interactive session to select the optimal interactive method. For example, the interactive unit can prioritize providing methods that the user has enjoyed in the past. Furthermore, based on the user's past experience history, the interactive unit can suggest interactive methods related to specific themes or events. In addition, the interactive unit can prioritize providing methods that the user has experienced in the past that have provided particularly high satisfaction. In this way, the interactive unit can provide the optimal interactive method by analyzing the user's past experience history.

[0092] The sensory unit can customize the sensory experience based on the user's current lifestyle. For example, if the user is currently relaxed, the sensory unit can provide a gentle sensory experience. If the user is currently busy, the sensory unit can also provide a shorter sensory experience. Furthermore, if the user is currently stressed, the sensory unit can provide a sensory experience that reduces stress. In this way, the sensory unit can provide a more appropriate sensory experience by customizing the sensory experience based on the user's current lifestyle.

[0093] The sensory unit can estimate the user's emotions and determine the priority of sensory experiences based on those estimated emotions. For example, if the emotion engine indicates that the user is happy, the sensory unit will prioritize providing enjoyable sensory experiences. Similarly, if the emotion engine indicates that the user is sad, the sensory unit can prioritize providing sensory experiences that alleviate those emotions. Furthermore, if the emotion engine indicates that the user is excited, the sensory unit can prioritize providing exciting sensory experiences that match that emotion. This allows the sensory unit to provide more appropriate sensory experiences by prioritizing experiences according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The sensory unit can select an appropriate sensory experience based on the user's geographical location information during the user's experience. For example, the sensory unit can provide a sensory experience related to the user's current location. It can also provide a sensory experience related to places the user has visited in the past. Furthermore, it can provide a sensory experience related to places the user plans to visit in the future. In this way, the sensory unit can provide the optimal sensory experience by taking the user's geographical location information into consideration.

[0095] The experience unit can analyze the user's social media activity during the experience and propose ways to enhance the experience. For example, the experience unit can provide experiences related to experiences the user has recently shared on social media. It can also provide experiences related to accounts the user follows on social media. Furthermore, it can provide experiences related to events the user is participating in on social media. In this way, the experience unit can provide the optimal experience by analyzing the user's social media activity.

[0096] The sharing function can estimate the user's emotions and adjust the sharing method based on the estimated emotions. For example, if the emotion engine indicates that the user is relaxed, the sharing function will encourage sharing in a calm tone. If the emotion engine indicates that the user is excited, the sharing function can encourage sharing in an energetic tone. Furthermore, if the emotion engine indicates that the user is sad, the sharing function can encourage sharing in a calm tone. In this way, the sharing function can enable more appropriate sharing by adjusting the sharing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The sharing function can analyze a user's past sharing history to select the optimal sharing method. For example, it can prioritize suggesting sharing methods that the user has frequently used in the past (email, social media, etc.). It can also analyze a user's past sharing history to identify sharing tendencies at specific times and encourage sharing during those times. Furthermore, it can suggest methods related to content the user has shared in the past. In this way, the sharing function can provide the optimal sharing method by analyzing a user's past sharing history.

[0098] The sharing function can estimate the user's emotions and determine sharing priorities based on those estimated emotions. For example, if the emotion engine indicates that the user is happy, the sharing function will prioritize sharing enjoyable experiences. Similarly, if the emotion engine indicates that the user is sad, the sharing function can prioritize sharing experiences that alleviate those emotions. Furthermore, if the emotion engine indicates that the user is excited, the sharing function can prioritize sharing exciting experiences that match that emotion. This allows the sharing function to provide more appropriate sharing by prioritizing sharing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The sharing function can select an appropriate sharing method based on the user's geographical location information when sharing. For example, the sharing function can share experiences related to the user's current location. It can also share experiences related to places the user has visited in the past. Furthermore, it can share experiences related to places the user plans to visit in the future. In this way, the sharing function can provide the optimal sharing method by taking the user's geographical location information into consideration.

[0100] The Quality Improvement Unit can estimate the user's emotions and adjust its quality improvement methods based on those emotions. For example, if the emotion engine indicates that the user is relaxed, the Quality Improvement Unit can offer quality improvement suggestions in a calm tone. If the emotion engine indicates that the user is excited, the Quality Improvement Unit can offer quality improvement suggestions in an energetic tone. Furthermore, if the emotion engine indicates that the user is sad, the Quality Improvement Unit can offer quality improvement suggestions in a calm tone. This allows the Quality Improvement Unit to adjust its quality improvement methods according to the user's emotions, enabling more appropriate quality improvement. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The Quality Improvement Department can analyze a user's past photo history to select the optimal quality improvement method during the improvement process. For example, the Quality Improvement Department can prioritize suggesting filters and effects that the user has previously preferred. Furthermore, the Quality Improvement Department can suggest quality improvement methods related to specific themes or events based on the user's past photo history. In addition, the Quality Improvement Department can prioritize suggesting methods that the user has used in the past and found particularly satisfying. This allows the Quality Improvement Department to provide the optimal quality improvement method by analyzing the user's past photo history.

[0102] The Quality Improvement Department can estimate the user's emotions and determine the priority of quality improvements based on those estimated emotions. For example, if the emotion engine indicates that the user is happy, the Quality Improvement Department can prioritize improving enjoyable experiences. Similarly, if the emotion engine indicates that the user is sad, the Quality Improvement Department can prioritize improving experiences that alleviate those emotions. Furthermore, if the emotion engine indicates that the user is excited, the Quality Improvement Department can prioritize improving exciting experiences that match that emotion. This allows the Quality Improvement Department to determine the priority of quality improvements according to the user's emotions, enabling more appropriate quality improvements. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The Quality Improvement Department can select the appropriate quality improvement method based on the user's geographical location information when improving quality. For example, the Quality Improvement Department can improve the experience related to the user's current location. It can also improve the experience related to places the user has visited in the past. Furthermore, it can improve the experience related to places the user plans to visit in the future. In this way, the Quality Improvement Department can provide the optimal quality improvement method by considering the user's geographical location information. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, conversion unit, reproduction unit, experience unit, and sharing unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, allowing the user to upload 2D photos from their album. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12, converting 2D photos into 3D photos using AI. The reproduction unit is implemented by the specific processing unit 290 of the data processing unit 12, reproducing the converted 3D photos in the metaverse space. The experience unit is implemented by the control unit 46A of the smart device 14, allowing the user to experience the metaverse space via a VR device. The sharing unit is implemented by the specific processing unit 290 of the data processing unit 12, allowing the user to share the experience with others. The quality improvement unit is implemented by the specific processing unit 290 of the data processing unit 12, improving the quality of the 3D photos. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, conversion unit, reproduction unit, experience unit, and sharing unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, allowing the user to upload 2D photos from their album. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12, converting 2D photos into 3D photos using AI. The reproduction unit is implemented by the specific processing unit 290 of the data processing unit 12, reproducing the converted 3D photos in the metaverse space. The experience unit is implemented by the control unit 46A of the smart glasses 214, allowing the user to experience the metaverse space via a VR device. The sharing unit is implemented by the specific processing unit 290 of the data processing unit 12, allowing the user to share the experience with others. The quality improvement unit is implemented by the specific processing unit 290 of the data processing unit 12, improving the quality of the 3D photos. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, conversion unit, reproduction unit, experience unit, and sharing unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, allowing the user to upload 2D photos from their album. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12, which uses AI to convert 2D photos into 3D photos. The reproduction unit is implemented by the specific processing unit 290 of the data processing unit 12, which reproduces the converted 3D photo in the metaverse space. The experience unit is implemented by the control unit 46A of the headset terminal 314, allowing the user to experience the metaverse space via a VR device. The sharing unit is implemented by the specific processing unit 290 of the data processing unit 12, allowing the user to share the experience with others. The quality improvement unit is implemented by the specific processing unit 290 of the data processing unit 12, which improves the quality of the 3D photo. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, conversion unit, reproduction unit, experience unit, and sharing unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, allowing the user to upload 2D photos from their album. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12, converting 2D photos into 3D photos using AI. The reproduction unit is implemented by the specific processing unit 290 of the data processing unit 12, reproducing the converted 3D photos in the metaverse space. The experience unit is implemented by the control unit 46A of the robot 414, allowing the user to experience the metaverse space via a VR device. The sharing unit is implemented by the specific processing unit 290 of the data processing unit 12, allowing the user to share the experience with others. The quality improvement unit is implemented by the specific processing unit 290 of the data processing unit 12, improving the quality of the 3D photos.

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

[0105] An experiential album-providing metaverse system can also include a history analysis unit that analyzes the user's past experience history and proposes the most suitable way to experience the content. For example, the history analysis unit can prioritize suggesting methods that the user has enjoyed in the past. Furthermore, the history analysis unit can also suggest methods related to specific themes or events based on the user's past experience history. In addition, the history analysis unit can prioritize suggesting methods that the user found particularly satisfying among their past experiences. This allows the history analysis unit to provide the most optimal experience by analyzing the user's past experience history.

[0106] An experiential album-providing metaverse system can also include a lifestyle-adapting unit that customizes the means of experience based on the user's current lifestyle. For example, if the user is currently relaxed, the lifestyle-adapting unit can provide a gentle experience. If the user is currently busy, it can also provide a short-duration experience. Furthermore, if the user is currently stressed, it can provide an experience that reduces stress. In this way, the lifestyle-adapting unit can customize the means of experience based on the user's current lifestyle, enabling a more appropriate experience.

[0107] An experiential photo album metaverse system can also include a geographic information adaptation unit that selects an appropriate experience method based on the user's geographic location. For example, the geographic information adaptation unit can provide an experience method related to the user's current location. It can also provide an experience method related to places the user has visited in the past. Furthermore, it can provide an experience method related to places the user plans to visit in the future. In this way, the geographic information adaptation unit can provide the optimal experience method by taking the user's geographic location into consideration.

[0108] An experiential album-providing metaverse system can also include a social media analysis unit that analyzes users' social media activity and suggests ways to experience the content. For example, the social media analysis unit can provide experiences related to experiences the user has recently shared on social media. It can also provide experiences related to accounts the user follows on social media. Furthermore, it can provide experiences related to events the user is participating in on social media. In this way, the social media analysis unit can provide the most optimal experience by analyzing the user's social media activity.

[0109] An experiential album-providing metaverse system can also include an emotion-prioritizing unit that estimates the user's emotions and determines the priority of experiences based on those emotions. For example, if the emotion engine indicates that the user is happy, the emotion-prioritizing unit can prioritize providing enjoyable experiences. It can also prioritize providing experiences that alleviate sadness if the emotion engine indicates that the user is sad. Furthermore, if the emotion-prioritizing unit indicates excitement, it can prioritize providing exciting experiences that match that emotion. This allows the emotion-prioritizing unit to determine the priority of experiences according to the user's emotions, enabling a more appropriate experience.

[0110] An experiential album-providing metaverse system can also include an emotion-sharing unit that estimates the user's emotions and adjusts the sharing method based on those emotions. For example, if the emotion engine indicates that the user is relaxed, the emotion-sharing unit can encourage sharing in a calm tone. If the emotion engine indicates that the user is excited, the emotion-sharing unit can encourage sharing in an energetic tone. Furthermore, if the emotion engine indicates that the user is sad, the emotion-sharing unit can encourage sharing in a calm tone. In this way, the emotion-sharing unit can adjust the sharing method according to the user's emotions, enabling more appropriate sharing.

[0111] An experiential album-providing metaverse system can also include a sharing history analysis unit that analyzes a user's past sharing history to select the optimal sharing method. For example, the sharing history analysis unit can prioritize suggesting sharing methods that the user has frequently used in the past (email, social media, etc.). Furthermore, the sharing history analysis unit can analyze the user's past sharing history to identify sharing tendencies at specific times and encourage sharing during those times. In addition, the sharing history analysis unit can suggest methods related to content the user has shared in the past. In this way, the sharing history analysis unit can provide the optimal sharing method by analyzing the user's past sharing history.

[0112] An experiential album-providing metaverse system can also include a geographic information sharing unit that selects the appropriate sharing method based on the user's geographic location. For example, the geographic information sharing unit can share experiences related to the user's current location. It can also share experiences related to places the user has visited in the past. Furthermore, it can share experiences related to places the user plans to visit in the future. In this way, the geographic information sharing unit can provide the optimal sharing method by taking the user's geographic location into consideration.

[0113] An experiential album-providing metaverse system may also include an emotional quality enhancement unit that estimates the user's emotions and adjusts the quality enhancement method based on the estimated emotions. For example, if the emotional quality enhancement unit is relaxed according to the emotional engine, it may make quality enhancement suggestions in a calm tone. If the emotional quality enhancement unit is excited according to the emotional engine, it may make quality enhancement suggestions in an energetic tone. Furthermore, if the emotional quality enhancement unit is sad according to the emotional engine, it may make quality enhancement suggestions in a calm tone. In this way, the emotional quality enhancement unit can adjust the quality enhancement method according to the user's emotions, enabling more appropriate quality enhancement.

[0114] An experiential photo album metaverse system can also include a photo history analysis unit that analyzes the user's past photo history to select the optimal quality improvement method. For example, the photo history analysis unit can prioritize suggesting filters and effects that the user has previously preferred to use. It can also suggest quality improvement methods related to specific themes or events based on the user's past photo history. Furthermore, it can prioritize suggesting methods that the user has used in the past that have provided particularly high satisfaction. In this way, the photo history analysis unit can provide the optimal quality improvement method by analyzing the user's past photo history.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The reception area is where users upload 2D photos from their albums. For example, users can upload various memorable photos, such as family trip photos or graduation photos. The reception area inputs the uploaded 2D photos into the AI. Step 2: The conversion unit uses AI to analyze the uploaded 2D photo and convert it into a 3D photo. For example, the conversion unit analyzes each element of the photo and reproduces depth and three-dimensionality. The conversion unit generates a 3D photo by considering factors such as the position of the person and the perspective of the background. Step 3: The reproduction unit is the part that reproduces the 3D image converted by the transformation unit in the metaverse space. For example, the reproduction unit places the generated 3D image in the metaverse space, allowing the user to experience that space. Step 4: The tactile section is the part that allows users to experience the metaverse space reproduced by the reproduction section via a VR device. For example, the tactile section allows users to experience the metaverse space by wearing a VR device.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0118] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0120] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0122] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0138] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0154] As shown in Figure 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.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0170] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0172] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0173] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0174] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0179] 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.

[0180] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0181] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0182] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0183] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area for uploading 2D photos, A conversion unit analyzes the 2D photo uploaded by the reception unit and converts it into 3D, A reproduction unit reproduces the 3D photograph converted by the aforementioned conversion unit in the metaverse space, The experience unit allows users to experience the metaverse space reproduced by the reproduction unit via a VR device. Equipped with A system characterized by the following features.

2. It features a shared area for sharing experiences with others. The system according to feature 1.

3. It is equipped with a quality improvement unit to enhance the quality of 3D images. The system according to feature 1.

4. The conversion unit is Analyze each element of the photograph to reproduce depth or three-dimensionality. The system according to feature 1.

5. The reproduction unit is, Recreating 3D photographs in metaverse space The system according to feature 1.

6. The aforementioned sensory unit is Experience the metaverse space through VR devices. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of photo uploads based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past upload history and select the appropriate upload method. The system according to feature 1.

Images