system

Abstract

The system according to this embodiment aims to analyze photographs and videos in three dimensions, enabling users to realistically relive past memories. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, and an experience unit. The reception unit uploads a photo or video. The analysis unit analyzes the photo or video uploaded by the reception unit and generates a three-dimensional model. The experience unit allows the user to experience the three-dimensional model generated by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes 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 difficult to experience photos and videos stereoscopically and difficult to reproduce past memories more realistically.

[0005] The system according to the embodiment aims to analyze photos and videos stereoscopically so that users can realistically experience past memories.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, and an experience unit. The reception unit uploads a photograph or video. The analysis unit analyzes the photograph or video uploaded by the reception unit and generates a three-dimensional model. The experience unit allows the user to experience the three-dimensional model generated by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze photos and videos in three dimensions, allowing users to realistically relive past memories. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. 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) An XR album generation system according to an embodiment of the present invention is a system in which AI analyzes photos and videos and automatically generates a three-dimensional XR album. This XR album generation system allows users to immerse themselves in an XR space and relive past memories as a three-dimensional, realistic experience. This system provides a new level of emotion and immersion, enabling users to deepen their bonds with family and friends. First, the user uploads photos and videos. Next, the AI ​​analyzes these photos and videos and automatically generates a three-dimensional XR album. The AI ​​analyzes the content of the photos and videos and generates a three-dimensional model. For example, it analyzes family group photos or travel videos and recreates three-dimensional scenes. The generated XR album can be experienced by the user using an XR device. The user can immerse themselves in an XR space and relive past memories as a three-dimensional, realistic experience. For example, a family group photo can be recreated three-dimensionally, giving the user the feeling of being there. This system enables users to deepen their bonds with family and friends. By relive past memories as a realistic experience, the bonds between family and friends are strengthened. For example, a three-dimensional reproduction of a family photo can deepen family bonds by creating the feeling of being present in the moment. This system also offers a new level of emotional impact and immersion. Users can relive past memories as a three-dimensional, realistic experience, leading to new emotional experiences. For instance, a three-dimensional reproduction of a travel video can create the feeling of being present in the moment, providing a new level of emotional impact. In this way, by using AI to analyze photos and videos and automatically generate three-dimensional XR albums, users can relive past memories as a three-dimensional, realistic experience, deepening bonds with family and friends. Thus, the XR album generation system allows users to relive past memories as a three-dimensional, realistic experience.

[0029] The XR album generation system according to this embodiment comprises a reception unit, an analysis unit, and an experience unit. The reception unit allows users to upload photos and videos. The photos and videos uploaded by the user include, but are not limited to, formats such as JPEG, PNG, MP4, and AVI. The reception unit allows users to upload photos and videos by drag and drop, for example. The reception unit also allows users to select and upload photos and videos from a specific folder. Furthermore, the reception unit allows users to directly upload photos and videos from cloud storage. The analysis unit uses deep learning to analyze the photos and videos uploaded by the reception unit and generate a three-dimensional model. Deep learning can utilize technologies such as CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network). The analysis unit can, for example, use CNN to extract features from a photo and generate a three-dimensional model. The analysis unit can also use RNN to analyze the temporal changes in a video and reconstruct a three-dimensional scene. Furthermore, the analysis unit can use a GAN (Generative Opposite Network) to generate new three-dimensional scenes based on the content of photos and videos. The experience unit enables the user to experience the three-dimensional model generated by the analysis unit. The experience unit enables the user to enjoy a three-dimensional experience using XR devices such as VR headsets or AR glasses. The experience unit enables the user to immerse themselves in a three-dimensional scene using a VR headset, for example. The experience unit also allows the user to experience the three-dimensional scene overlaid with the real world using AR glasses. Furthermore, the experience unit allows the user to enjoy a seamlessly integrated experience of the real and virtual worlds using MR devices. Thus, the XR album generation system according to this embodiment allows the user to upload photos and videos, have them analyzed, and experience a three-dimensional model.

[0030] The reception desk allows users to upload photos and videos. These uploads may include, but are not limited to, formats such as JPEG, PNG, MP4, and AVI. The reception desk allows users to upload photos and videos via drag-and-drop, for example. It also allows users to select and upload photos and videos from specific folders. Furthermore, it enables users to directly upload photos and videos from cloud storage. Specifically, the reception desk provides intuitive operation through its user interface. For example, when a user performs a drag-and-drop operation in a browser, the file is uploaded instantly, and the progress is displayed. The folder selection function allows users to select multiple files at once and upload them efficiently. Regarding uploads from cloud storage, users can integrate with external services such as Google Drive and Dropbox to directly select and upload files. This allows users to easily access files not only stored on their devices but also in the cloud. Additionally, the reception desk automatically detects the format and size of uploaded files and performs conversion or compression as needed. For example, if an uploaded video file is too large, the reception system automatically compresses the video to reduce the system load. Also, if an unsupported file format is uploaded, the reception system suggests an appropriate conversion method to the user, supporting a smooth upload. This allows the reception system to provide an environment where users can easily upload photos and videos in various formats, improving the overall usability of the system.

[0031] The analysis unit uses deep learning to analyze photos and videos uploaded by the reception unit and generate three-dimensional models. Deep learning can utilize technologies such as CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network). For example, the analysis unit can use CNN to extract features from photos and generate a three-dimensional model. It can also use RNN to analyze the temporal changes in videos and reconstruct three-dimensional scenes. Furthermore, the analysis unit can use GAN (Generative Opposite Network) to generate new three-dimensional scenes based on the content of photos and videos. Specifically, by using CNN, features such as edges, textures, and colors from photos can be extracted with high accuracy, and a 3D model can be constructed based on these. For example, information from different angles in multiple photos can be integrated to reconstruct a three-dimensional object. When using RNN, the temporal changes between video frames are analyzed to generate a three-dimensional scene that captures movement and change. This makes it possible to realistically reproduce not only still images but also dynamic scenes. Furthermore, by using GAN, new viewpoints and scenes can be generated based on information from existing photos and videos. For example, GANs can fill in the gaps in a photograph, generating a more complete 3D scene. This allows the analysis unit to generate realistic and detailed 3D models based on user-uploaded content, providing users with a new experience. Furthermore, the analysis unit has the ability to evaluate the quality of the generated model and make corrections or optimizations as needed. This ensures that high-quality 3D models are always provided, improving user satisfaction.

[0032] The Experience Unit enables users to experience the three-dimensional models generated by the Analysis Unit. The Experience Unit allows users to enjoy a three-dimensional experience using XR devices such as VR headsets and AR glasses. For example, using a VR headset, the Experience Unit allows users to immerse themselves in a three-dimensional scene. Furthermore, using AR glasses, the Experience Unit allows users to experience the real world overlaid with a three-dimensional scene. Additionally, using MR devices, the Experience Unit allows users to enjoy a seamlessly integrated experience of the real and virtual worlds. Specifically, a user wearing a VR headset can fully immerse themselves in a virtual three-dimensional scene and experience it with a 360-degree view. For example, they can experience three-dimensional landscapes or event scenes generated based on photos or videos uploaded by the user, as if they were actually there. When using AR glasses, users can overlay three-dimensional models onto their real-world view. For example, while a user is in their living room at home, they can overlay a three-dimensional landscape generated based on photos from a past trip onto their real space, creating an experience as if they were revisiting that place. When using MR devices, users can enjoy an experience that seamlessly integrates the real and virtual worlds. For example, a user can walk around a real room while interacting with virtual objects and scenes. This allows the experience unit to provide users with diverse XR experiences and propose new ways to enjoy photos and videos. Furthermore, the experience unit can provide a more immersive experience by tracking the user's movements and gaze and adding interactive elements. In this way, the experience unit can provide users with advanced XR experiences and enhance the overall value of the system.

[0033] The analysis unit can analyze photos and videos using deep learning. For example, the analysis unit can analyze photos and videos using deep learning technology. For example, the analysis unit can extract features from photos using a CNN (Convolutional Neural Network). The analysis unit can also analyze the temporal changes in videos using an RNN (Recurrent Neural Network). Furthermore, the analysis unit can generate new three-dimensional scenes based on the content of photos and videos using a GAN (Generative Opposite Network). As a result, the accuracy of photo and video analysis is improved by using deep learning. Deep learning technologies include, but are not limited to, CNNs, RNNs, and GANs. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input photos and videos into a generative AI, which can extract features from the photos and videos and generate a three-dimensional model.

[0034] The analysis unit can reproduce a three-dimensional scene using 3D modeling techniques. For example, the analysis unit can reproduce a three-dimensional scene using 3D modeling techniques. For example, the analysis unit can generate a three-dimensional scene using polygon modeling. The analysis unit can also create a detailed three-dimensional model using sculpting techniques. Furthermore, the analysis unit can reproduce a three-dimensional scene using voxel modeling. Thus, by using 3D modeling techniques, it becomes possible to reproduce a three-dimensional scene. 3D modeling techniques include, but are not limited to, polygon modeling, sculpting, and voxel modeling. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input a photograph or video into a generative AI, which can then perform 3D modeling and reproduce a three-dimensional scene.

[0035] The experience section allows users to experience things using XR devices. For example, the experience section enables users to immerse themselves in three-dimensional scenes using a VR headset. For instance, the experience section allows users to experience three-dimensional scenes in a 360-degree field of view using a VR headset. The experience section also allows users to experience the real world overlaid with three-dimensional scenes using AR glasses. For example, the experience section allows users to display three-dimensional models overlaid on real-world landscapes using AR glasses. Furthermore, the experience section allows users to enjoy a seamlessly integrated experience of the real and virtual worlds using MR devices. For example, the experience section allows users to simultaneously manipulate real-world objects and virtual-world objects using MR devices. This allows users to enjoy a three-dimensional experience by using XR devices. XR devices include, but are not limited to, VR headsets, AR glasses, and MR devices. Some or all of the above-described processes in the experience section may be performed using, for example, generative AI, or without generative AI. For example, the experience section can use generative AI to analyze user movements and gaze to provide an optimal experience.

[0036] The Experience Unit can provide specific scenarios to deepen bonds with family and friends. For example, it can provide a family reunion scenario. For instance, it can recreate a family group photo in 3D, allowing users to experience the feeling of being there. The Experience Unit can also provide adventure scenarios with friends. For example, it can recreate a travel video in 3D, allowing users to enjoy the adventure with their friends. Furthermore, the Experience Unit can provide specific event scenarios. For example, it can recreate a wedding video in 3D, allowing users to relive the emotions. In this way, by providing concrete scenarios, bonds with family and friends can be deepened. Specific scenarios include, but are not limited to, family reunion scenarios, adventure scenarios with friends, and specific event scenarios. Some or all of the processing described above in the Experience Unit may be performed using, for example, generative AI, or without generative AI. For example, the Experience Unit can use generative AI to analyze the user's emotions and reactions and provide the optimal scenario.

[0037] The experience unit can provide user interfaces and operating methods. For example, the experience unit can provide a GUI (Graphical User Interface). For example, the experience unit can provide an interface with icons and buttons that users can operate intuitively. The experience unit can also provide a voice interface. For example, the experience unit can provide an interface that allows users to operate the system using voice commands. Furthermore, the experience unit can also provide a gesture interface. For example, the experience unit can provide an interface that allows users to operate the system using hand movements and gestures. By providing user interfaces and operating methods, the system becomes easier for users to operate. User interfaces include, but are not limited to, GUIs, voice interfaces, and gesture interfaces. Some or all of the above processing in the experience unit may be performed using, for example, generative AI, or not using generative AI. For example, the experience unit can use generative AI to analyze the user's operation history and provide an optimal interface.

[0038] The reception desk can analyze a user's past upload history and select the optimal upload method. For example, the reception desk can prioritize suggesting upload methods that the user has frequently used in the past (e.g., drag and drop). For example, the reception desk can analyze a user's past upload history and select the optimal upload method. The reception desk can also send notifications during specific time periods if the user tends to upload during those times. For example, the reception desk can suggest the optimal upload time based on the user's past upload history. Furthermore, if the reception desk frequently uploads from a particular device, it can provide an interface optimized for that device. For example, the reception desk can suggest the optimal device based on the user's past upload history. This allows the reception desk to provide the user with the optimal upload method by analyzing past upload history. Optimal upload methods include, but are not limited to, file compression, batch uploads, and real-time uploads. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's past upload history into AI, which can then select the optimal upload method.

[0039] The reception desk can filter photos and videos uploaded based on the user's current projects and areas of interest. For example, if a user is working on a travel project, the reception desk will prioritize uploading travel-related photos and videos. For example, the reception desk can analyze the user's current projects and areas of interest and select the most suitable photos and videos. The reception desk can also prioritize uploading media related to a specific event if the user is interested in that event. For example, the reception desk can select the most suitable media based on the user's areas of interest. Furthermore, if a user is working on academic research, the reception desk can prioritize uploading data related to that research. For example, the reception desk can select the most suitable data based on the user's project. This allows for the priority uploading of highly relevant media by filtering based on the user's projects and areas of interest. Filtering includes, but is not limited to, keyword filtering and content-based filtering. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's project and areas of interest into the AI, which can then select the most suitable media.

[0040] The reception system can prioritize uploading highly relevant media when users upload photos and videos, taking into account their geographical location. For example, if a user is traveling, the reception system can prioritize uploading photos and videos related to their current location. For example, the reception system can analyze the user's geographical location and select the most suitable photos and videos. Furthermore, if a user is attending a specific event, the reception system can prioritize uploading media related to that event. For example, the reception system can select the most suitable media based on the user's geographical location. Additionally, if a user is at home, the reception system can prioritize uploading media related to past memories at home. For example, the reception system can select the most suitable data based on the user's geographical location. This allows for the prioritization of highly relevant media by considering geographical location. Geographical location information includes, but is not limited to, GPS data, IP addresses, and Wi-Fi location information. Some or all of the processing described above in the reception system may be performed using, for example, AI, or not. For example, the reception desk can input the user's geographical location information into the AI, which can then select the most suitable media.

[0041] The reception desk can analyze a user's social media activity when they upload photos or videos and upload relevant media. For example, the reception desk can prioritize uploading photos and videos related to what the user has recently shared on social media. For example, the reception desk can analyze a user's social media activity and select the most suitable photos and videos. The reception desk can also prioritize uploading media related to specific hashtags if the user is using them. For example, the reception desk can select the most suitable media based on the user's social media activity. Furthermore, if the reception desk is a member of a specific group or community, it can prioritize uploading media related to that group or community. For example, the reception desk can select the most suitable data based on the user's social media activity. This allows for the priority uploading of relevant media by analyzing social media activity. Social media activity includes, but is not limited to, posts, likes, and follower counts. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity into an AI, which can then select the most suitable media.

[0042] The analysis unit can adjust the level of detail in its analysis based on the importance of the photos and videos. For example, it can analyze photos and videos of important events in detail and display them in fine detail. For example, it can analyze the importance of photos and videos and select the optimal level of detail. It can also simplify the analysis of everyday photos and videos and display only the main points. For example, it can select the optimal level of detail based on the importance of the photos and videos. Furthermore, the analysis unit can perform detailed analysis specific to a particular theme for photos and videos related to that theme. For example, it can select the optimal level of detail based on the theme of the photos and videos. This allows for optimal analysis results by adjusting the level of detail based on the importance of the photos and videos. Importance includes, but is not limited to, user interest and content novelty. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of photos and videos into the AI, which can then select the optimal level of detail.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the photos or videos during analysis. For example, the analysis unit can apply a landscape analysis algorithm to travel photos to emphasize the features of the landscape. For example, the analysis unit can analyze the category of photos or videos and select the optimal analysis algorithm. The analysis unit can also apply a face recognition algorithm to family photos to analyze the facial expressions of people. For example, the analysis unit can select the optimal analysis algorithm based on the category of photos or videos. Furthermore, the analysis unit can apply a motion analysis algorithm to event videos to extract important scenes. For example, the analysis unit can select the optimal analysis algorithm based on the category of photos or videos. This allows for the provision of optimal analysis results by applying an analysis algorithm appropriate to the category. Categories include, but are not limited to, landscape photos, portraits, and event videos. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the category of photos or videos into the AI, which can then select the optimal analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on when the photos and videos were taken. For example, the analysis unit may prioritize the analysis of recently taken photos and videos. For example, the analysis unit can analyze when the photos and videos were taken and select the optimal priority. The analysis unit can also prioritize the analysis of photos and videos taken during a specific event period. For example, the analysis unit can select the optimal priority based on when the photos and videos were taken. Furthermore, if the user is interested in a particular period, the analysis unit can prioritize the analysis of photos and videos taken during that period. For example, the analysis unit can select the optimal priority based on when the photos and videos were taken. This allows for the provision of optimal analysis results by determining the priority of analysis based on the shooting date. The shooting date includes, but is not limited to, timestamps, metadata, and calendar information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the shooting dates of photos and videos into the AI, which can then select the optimal priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of photos and videos during the analysis process. For example, the analysis unit can analyze photos and videos related to the same event in sequence. For example, the analysis unit can analyze the relevance of photos and videos and select the optimal order. The analysis unit can also analyze photos and videos taken in the same location in sequence. For example, the analysis unit can select the optimal order based on the relevance of photos and videos. Furthermore, the analysis unit can analyze photos and videos featuring the same person in sequence. For example, the analysis unit can select the optimal order based on the relevance of photos and videos. By adjusting the order of analysis based on relevance, the optimal analysis results can be provided. Relevance includes, but is not limited to, content similarity and user interest. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of photos and videos into AI, which can then select the optimal order.

[0046] The experience unit can optimize the current experience by referring to past experience data during the experience. For example, the experience unit can customize the current experience based on experiences the user has enjoyed in the past. For example, the experience unit can analyze the user's past experience data and provide the optimal experience. The experience unit can also adjust the current experience based on experiences the user has avoided in the past. For example, the experience unit can provide the optimal experience based on the user's past experience data. Furthermore, the experience unit can optimize the current experience based on experiences the user has given high ratings to in the past. For example, the experience unit can provide the optimal experience based on the user's past experience data. In this way, the current experience can be optimized by referring to past experience data. Past experience data includes, but is not limited to, log data, user feedback, and session history. Some or all of the above processing in the experience unit may be performed using, for example, AI, or not using AI. For example, the experience unit can input the user's past experience data into AI, and the AI ​​can provide the optimal experience.

[0047] The experience unit can apply different experience scenarios to each category of photos and videos during an experience. For example, the experience unit can apply a travel scenario to travel photos to recreate the atmosphere of a trip. For example, the experience unit can analyze the categories of photos and videos and select the optimal experience scenario. The experience unit can also apply a family scenario to family photos to emphasize family bonds. For example, the experience unit can select the optimal experience scenario based on the categories of photos and videos. Furthermore, the experience unit can apply an event scenario to event videos to recreate the atmosphere of an event. For example, the experience unit can select the optimal experience scenario based on the categories of photos and videos. In this way, by applying different scenarios to each category, the optimal experience can be provided. Experience scenarios include, but are not limited to, educational scenarios, entertainment scenarios, and training scenarios. Some or all of the above processing in the experience unit may be performed using, for example, AI, or not using AI. For example, the experience unit can input the categories of photos and videos into AI, and the AI ​​can select the optimal experience scenario.

[0048] The experience department can analyze changes in the experience based on when photos and videos were taken. For example, the experience department can provide the latest experience based on recently taken photos and videos. For example, the experience department can analyze when photos and videos were taken and provide the optimal experience. The experience department can also provide an event experience based on photos and videos taken during a specific event period. For example, the experience department can provide the optimal experience based on when photos and videos were taken. Furthermore, if the user is interested in a particular period, the experience department can provide an experience based on photos and videos taken during that period. For example, the experience department can provide the optimal experience based on when photos and videos were taken. This allows for the provision of the optimal experience by analyzing changes in the experience based on when they were taken. Changes in the experience include, but are not limited to, temporal changes and user feedback. Some or all of the above processing in the experience department may be performed using, for example, AI, or not using AI. For example, the experience department can input the timing of when photos and videos were taken into AI, and the AI ​​can provide the optimal experience.

[0049] The Experience Department can analyze the experience by referring to relevant market data for photos and videos during the experience. For example, the Experience Department can provide experiences that users are likely to be interested in based on market data. For example, the Experience Department can analyze relevant market data for photos and videos and provide the optimal experience. The Experience Department can also adjust experiences that users are likely to avoid based on market data. For example, the Experience Department can provide the optimal experience based on relevant market data for photos and videos. Furthermore, the Experience Department can optimize experiences that users are likely to rate highly based on market data. For example, the Experience Department can provide the optimal experience based on relevant market data for photos and videos. This allows for the provision of the optimal experience by referring to relevant market data. Relevant market data includes, but is not limited to, market research data and competitive analysis data. Some or all of the above processing in the Experience Department may be performed using, for example, AI, or not using AI. For example, the Experience Department can input relevant market data for photos and videos into AI, which can then provide the optimal experience.

[0050] The analysis unit can optimize the learning algorithm by referring to past training data during deep learning training. For example, the analysis unit can select the optimal hyperparameters based on past training data. For example, the analysis unit can analyze past training data and select the optimal learning algorithm. The analysis unit can also adjust the learning speed based on past training data. For example, the analysis unit can select the optimal learning speed based on past training data. Furthermore, the analysis unit can set the convergence conditions for learning based on past training data. For example, the analysis unit can select the optimal convergence conditions based on past training data. In this way, the learning algorithm can be optimized by referring to past training data. The learning algorithm includes, but is not limited to, hyperparameter tuning and model selection. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input past training data into AI, and the AI ​​can select the optimal learning algorithm.

[0051] The analysis unit can weight training data based on when photos and videos were taken during deep learning training. For example, the analysis unit can assign higher weights to recently taken photos and videos. For example, the analysis unit can analyze when photos and videos were taken and assign the optimal weights. The analysis unit can also assign higher weights to photos and videos taken during a specific event period. For example, the analysis unit can assign the optimal weights based on when photos and videos were taken. Furthermore, if the user is interested in a particular period, the analysis unit can assign higher weights to photos and videos taken during that period. For example, the analysis unit can assign the optimal weights based on when photos and videos were taken. This allows for optimal learning results by weighting training data based on when it was taken. Weighting includes, but is not limited to, data importance and data reliability. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of when photos and videos were taken into the AI, which can then perform optimal weighting.

[0052] The analysis unit can analyze the content of photos and videos during 3D modeling to select the optimal modeling method. For example, the analysis unit can apply a 3D modeling method specialized for landscapes to landscape photographs. For example, the analysis unit can analyze the content of photos and videos and select the optimal modeling method. The analysis unit can also apply a 3D modeling method specialized for people to family photographs. For example, the analysis unit can select the optimal modeling method based on the content of photos and videos. Furthermore, the analysis unit can apply a 3D modeling method specialized for motion analysis to event videos. For example, the analysis unit can select the optimal modeling method based on the content of photos and videos. In this way, the optimal 3D modeling method can be selected by analyzing the content of photos and videos. The optimal modeling method includes, but is not limited to, the type of content and user requirements. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the content of photos and videos into AI, and the AI ​​can select the optimal modeling method.

[0053] The analysis unit can select the optimal modeling method when 3D modeling, taking into account the geographical location information of photos and videos. For example, the analysis unit can apply a 3D modeling method that takes into account the geographical information of the travel destination to travel photos. For example, the analysis unit can analyze the geographical location information of photos and videos and select the optimal modeling method. The analysis unit can also apply a 3D modeling method that takes into account the geographical information of the event venue to event videos. For example, the analysis unit can select the optimal modeling method based on the geographical location information of photos and videos. Furthermore, the analysis unit can apply a 3D modeling method that takes into account the geographical information of the home or relatives' homes to family photos. For example, the analysis unit can select the optimal modeling method based on the geographical location information of photos and videos. In this way, the optimal 3D modeling method can be selected by taking into account geographical location information. Geographical location information includes, but is not limited to, GPS data, IP addresses, and Wi-Fi location information. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without using AI. For example, the analysis unit can input geographical location information from photos and videos into the AI, which can then select the optimal modeling method.

[0054] The Experience Unit can select the optimal usage method when using an XR device by referring to the user's past experience history. For example, the Experience Unit can customize the current experience based on the XR experiences the user has enjoyed in the past. For example, the Experience Unit can analyze the user's past experience history and provide the optimal usage method. The Experience Unit can also adjust the current experience based on XR experiences the user has avoided in the past. For example, the Experience Unit can provide the optimal usage method based on the user's past experience history. Furthermore, the Experience Unit can optimize the current experience based on XR experiences the user has given high ratings to in the past. For example, the Experience Unit can provide the optimal usage method based on the user's past experience history. In this way, the optimal XR experience can be provided by referring to past experience history. Past experience history includes, but is not limited to, log data, user feedback, and session history. Some or all of the above processing in the Experience Unit may be performed using, for example, AI, or not using AI. For example, the Experience Unit can input the user's past experience history into AI, and the AI ​​can provide the optimal usage method.

[0055] The experience unit can select the optimal usage method when using an XR device, taking into account the user's device information. For example, if the user is using a specific XR device, the experience unit can provide a usage method optimized for that device. For example, the experience unit can analyze the user's device information and provide the optimal usage method. Furthermore, if the user is using multiple XR devices, the experience unit can provide a usage method that takes into account the interoperability between devices. For example, the experience unit can provide the optimal usage method based on the user's device information. In addition, if the user is using a new XR device, the experience unit can provide a usage method that takes into account the characteristics of that device. For example, the experience unit can provide the optimal usage method based on the user's device information. In this way, by considering device information, the optimal XR experience can be provided. Device information includes, but is not limited to, the type, performance, and settings of the device. Some or all of the above processing in the experience unit may be performed using, for example, AI, or not using AI. For example, the experience unit can input the user's device information into AI, and the AI ​​can provide the optimal usage method.

[0056] The experience department can select the optimal scenario by referring to the user's past experience data when providing scenarios. For example, the experience department can customize the current scenario based on scenarios the user has preferred in the past. For example, the experience department can analyze the user's past experience data and provide the optimal scenario. The experience department can also adjust the current scenario based on scenarios the user has avoided in the past. For example, the experience department can provide the optimal scenario based on the user's past experience data. Furthermore, the experience department can optimize the current scenario based on scenarios the user has given high ratings to in the past. For example, the experience department can provide the optimal scenario based on the user's past experience data. In this way, the optimal scenario can be provided by referring to past experience data. Past experience data includes, but is not limited to, log data, user feedback, and session history. Some or all of the above processing in the experience department may be performed using, for example, AI, or not using AI. For example, the experience department can input the user's past experience data into AI, and the AI ​​can provide the optimal scenario.

[0057] The Experience Unit can provide the most suitable scenario by considering the user's geographical location information when providing scenarios. For example, if the user is traveling, the Experience Unit can provide a scenario related to their travel destination. For example, the Experience Unit can analyze the user's geographical location information and provide the most suitable scenario. The Experience Unit can also provide a scenario related to an event if the user is participating in a specific event. For example, the Experience Unit can provide the most suitable scenario based on the user's geographical location information. Furthermore, if the user is at home, the Experience Unit can provide a scenario related to memories at home. For example, the Experience Unit can provide the most suitable scenario based on the user's geographical location information. In this way, the best scenario can be provided by considering geographical location information. Geographical location information includes, but is not limited to, GPS data, IP addresses, and Wi-Fi location information. Some or all of the above processing in the Experience Unit may be performed using, for example, AI, or not using AI. For example, the Experience Unit can input the user's geographical location information into AI, and the AI ​​can provide the most suitable scenario.

[0058] The user experience unit can select the optimal display method by referring to the user's past operation history when displaying an interface. For example, the user experience unit can customize the current interface based on interfaces the user has preferred in the past. For example, the user experience unit can analyze the user's past operation history and provide the optimal display method. The user experience unit can also adjust the current interface based on interfaces the user has avoided in the past. For example, the user experience unit can provide the optimal display method based on the user's past operation history. Furthermore, the user experience unit can optimize the current interface based on interfaces the user has given high ratings to in the past. For example, the user experience unit can provide the optimal display method based on the user's past operation history. In this way, the optimal interface can be provided by referring to past operation history. Past operation history includes, but is not limited to, log data, user feedback, and session history. Some or all of the above processing in the user experience unit may be performed using, for example, AI, or not using AI. For example, the user experience unit can input the user's past operation history into AI, and the AI ​​can provide the optimal display method.

[0059] The user experience unit can select the optimal display method when displaying an interface, taking into account the user's device information. For example, if the user is using a smartphone, the user experience unit can provide a display method that matches the screen size. For example, the user experience unit can analyze the user's device information and provide the optimal display method. Also, if the user is using a tablet, the user experience unit can provide a display method optimized for a larger screen. For example, the user experience unit can provide the optimal display method based on the user's device information. Furthermore, if the user is using a smartwatch, the user experience unit can provide a concise and highly visible display method. For example, the user experience unit can provide the optimal display method based on the user's device information. In this way, by considering device information, the optimal interface can be provided. Device information includes, but is not limited to, the type, performance, and settings of the device. Some or all of the above processing in the user experience unit may be performed using, for example, AI, or not using AI. For example, the user experience unit can input the user's device information into AI, and the AI ​​can provide the optimal display method.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The analysis unit can analyze the content of photos and videos and select the optimal analysis algorithm. For example, a landscape-specific analysis algorithm can be applied to landscape photos. Similarly, a people-specific analysis algorithm can be applied to family photos. Furthermore, an action-analysis-specific analysis algorithm can be applied to event videos. In this way, the optimal analysis algorithm can be selected by analyzing the content of photos and videos. The optimal analysis algorithm may include, but is not limited to, the type of content and user requirements.

[0062] The experience section provides user interfaces and operating methods, and can select the optimal display method by referring to the user's past operation history. For example, the current interface can be customized based on interfaces the user has preferred in the past. It can also be adjusted based on interfaces the user has avoided in the past. Furthermore, the current interface can be optimized based on interfaces the user has given high ratings to in the past. In this way, the optimal interface can be provided by referring to past operation history. Past operation history includes, but is not limited to, log data, user feedback, and session history.

[0063] The reception desk can analyze a user's past upload history and select the optimal upload method. For example, it can prioritize suggesting upload methods the user has frequently used in the past (e.g., drag and drop). It can also send notifications if the user tends to upload during specific time periods. Furthermore, if a user frequently uploads from a particular device, it can provide an interface optimized for that device. This allows the system to provide the user with the best possible upload method by analyzing their past upload history. Optimal upload methods include, but are not limited to, file compression, batch uploads, and real-time uploads.

[0064] The analysis unit can prioritize analysis based on when the photos and videos were taken. For example, it can prioritize the analysis of recently taken photos and videos. It can also prioritize the analysis of photos and videos taken during a specific event period. Furthermore, if the user is interested in a particular time period, it can prioritize the analysis of photos and videos taken during that period. By prioritizing analysis based on the time of capture, the system can provide optimal analysis results. The time of capture includes, but is not limited to, timestamps, metadata, and calendar information.

[0065] The experience section can provide optimal scenarios by considering the user's geographical location. For example, if the user is traveling, it can provide scenarios related to their travel destination. Similarly, if the user is participating in a specific event, it can provide scenarios related to that event. Furthermore, if the user is at home, it can provide scenarios related to memories at home. This allows for the provision of optimal scenarios by considering geographical location. Geographical location information includes, but is not limited to, GPS data, IP addresses, and Wi-Fi location information.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk allows users to upload photos and videos. These uploads may include, but are not limited to, formats such as JPEG, PNG, MP4, and AVI. The reception desk can, for example, allow users to upload photos and videos via drag-and-drop. It can also allow users to select and upload photos and videos from specific folders. Furthermore, the reception desk can allow users to directly upload photos and videos from cloud storage. Step 2: The analysis unit uses deep learning to analyze the photos and videos uploaded by the reception unit and generate a 3D model. Deep learning can utilize technologies such as CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network). For example, the analysis unit can use CNN to extract features from photos and generate a 3D model. The analysis unit can also use RNN to analyze the temporal changes in videos and reconstruct 3D scenes. Furthermore, the analysis unit can use GAN (Generative Opposite Network) to generate new 3D scenes based on the content of photos and videos. Step 3: The experience unit enables the user to experience the three-dimensional model generated by the analysis unit. The experience unit enables the user to enjoy a three-dimensional experience using XR devices such as VR headsets or AR glasses. For example, the experience unit enables the user to immerse themselves in a three-dimensional scene using a VR headset. The experience unit also allows the user to experience the real world overlaid with the three-dimensional scene using AR glasses. Furthermore, the experience unit allows the user to enjoy a seamlessly integrated experience of the real and virtual worlds using MR devices.

[0068] (Example of form 2) An XR album generation system according to an embodiment of the present invention is a system in which AI analyzes photos and videos and automatically generates a three-dimensional XR album. This XR album generation system allows users to immerse themselves in an XR space and relive past memories as a three-dimensional, realistic experience. This system provides a new level of emotion and immersion, enabling users to deepen their bonds with family and friends. First, the user uploads photos and videos. Next, the AI ​​analyzes these photos and videos and automatically generates a three-dimensional XR album. The AI ​​analyzes the content of the photos and videos and generates a three-dimensional model. For example, it analyzes family group photos or travel videos and recreates three-dimensional scenes. The generated XR album can be experienced by the user using an XR device. The user can immerse themselves in an XR space and relive past memories as a three-dimensional, realistic experience. For example, a family group photo can be recreated three-dimensionally, giving the user the feeling of being there. This system enables users to deepen their bonds with family and friends. By relive past memories as a realistic experience, the bonds between family and friends are strengthened. For example, a three-dimensional reproduction of a family photo can deepen family bonds by creating the feeling of being present in the moment. This system also offers a new level of emotional impact and immersion. Users can relive past memories as a three-dimensional, realistic experience, leading to new emotional experiences. For instance, a three-dimensional reproduction of a travel video can create the feeling of being present in the moment, providing a new level of emotional impact. In this way, by using AI to analyze photos and videos and automatically generate three-dimensional XR albums, users can relive past memories as a three-dimensional, realistic experience, deepening bonds with family and friends. Thus, the XR album generation system allows users to relive past memories as a three-dimensional, realistic experience.

[0069] The XR album generation system according to this embodiment comprises a reception unit, an analysis unit, and an experience unit. The reception unit allows users to upload photos and videos. The photos and videos uploaded by the user include, but are not limited to, formats such as JPEG, PNG, MP4, and AVI. The reception unit allows users to upload photos and videos by drag and drop, for example. The reception unit also allows users to select and upload photos and videos from a specific folder. Furthermore, the reception unit allows users to directly upload photos and videos from cloud storage. The analysis unit uses deep learning to analyze the photos and videos uploaded by the reception unit and generate a three-dimensional model. Deep learning can utilize technologies such as CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network). The analysis unit can, for example, use CNN to extract features from a photo and generate a three-dimensional model. The analysis unit can also use RNN to analyze the temporal changes in a video and reconstruct a three-dimensional scene. Furthermore, the analysis unit can use a GAN (Generative Opposite Network) to generate new three-dimensional scenes based on the content of photos and videos. The experience unit enables the user to experience the three-dimensional model generated by the analysis unit. The experience unit enables the user to enjoy a three-dimensional experience using XR devices such as VR headsets or AR glasses. The experience unit enables the user to immerse themselves in a three-dimensional scene using a VR headset, for example. The experience unit also allows the user to experience the three-dimensional scene overlaid with the real world using AR glasses. Furthermore, the experience unit allows the user to enjoy a seamlessly integrated experience of the real and virtual worlds using MR devices. Thus, the XR album generation system according to this embodiment allows the user to upload photos and videos, have them analyzed, and experience a three-dimensional model.

[0070] The reception desk allows users to upload photos and videos. These uploads may include, but are not limited to, formats such as JPEG, PNG, MP4, and AVI. The reception desk allows users to upload photos and videos via drag-and-drop, for example. It also allows users to select and upload photos and videos from specific folders. Furthermore, it enables users to directly upload photos and videos from cloud storage. Specifically, the reception desk provides intuitive operation through its user interface. For example, when a user performs a drag-and-drop operation in their browser, the file is uploaded instantly, and the progress is displayed. The folder selection function allows users to select multiple files at once and upload them efficiently. Regarding uploads from cloud storage, users can integrate with external services such as Google Drive and Dropbox, directly selecting and uploading files. This allows users to easily access files not only stored on their devices but also in the cloud. Additionally, the reception desk automatically detects the format and size of uploaded files and performs conversion or compression as needed. For example, if an uploaded video file is too large, the reception system automatically compresses the video to reduce the system load. Also, if an unsupported file format is uploaded, the reception system suggests an appropriate conversion method to the user, supporting a smooth upload. This allows the reception system to provide an environment where users can easily upload photos and videos in various formats, improving the overall usability of the system.

[0071] The analysis unit uses deep learning to analyze photos and videos uploaded by the reception unit and generate three-dimensional models. Deep learning can utilize technologies such as CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network). For example, the analysis unit can use CNN to extract features from photos and generate a three-dimensional model. It can also use RNN to analyze the temporal changes in videos and reconstruct three-dimensional scenes. Furthermore, the analysis unit can use GAN (Generative Opposite Network) to generate new three-dimensional scenes based on the content of photos and videos. Specifically, by using CNN, features such as edges, textures, and colors from photos can be extracted with high accuracy, and a 3D model can be constructed based on these. For example, information from different angles in multiple photos can be integrated to reconstruct a three-dimensional object. When using RNN, the temporal changes between video frames are analyzed to generate a three-dimensional scene that captures movement and change. This makes it possible to realistically reproduce not only still images but also dynamic scenes. Furthermore, by using GAN, new viewpoints and scenes can be generated based on information from existing photos and videos. For example, GANs can fill in the gaps in a photograph, generating a more complete 3D scene. This allows the analysis unit to generate realistic and detailed 3D models based on user-uploaded content, providing users with a new experience. Furthermore, the analysis unit has the ability to evaluate the quality of the generated model and make corrections or optimizations as needed. This ensures that high-quality 3D models are always provided, improving user satisfaction.

[0072] The Experience Unit enables users to experience the three-dimensional models generated by the Analysis Unit. The Experience Unit allows users to enjoy a three-dimensional experience using XR devices such as VR headsets and AR glasses. For example, using a VR headset, the Experience Unit allows users to immerse themselves in a three-dimensional scene. Furthermore, using AR glasses, the Experience Unit allows users to experience the real world overlaid with a three-dimensional scene. Additionally, using MR devices, the Experience Unit allows users to enjoy a seamlessly integrated experience of the real and virtual worlds. Specifically, a user wearing a VR headset can fully immerse themselves in a virtual three-dimensional scene and experience it with a 360-degree view. For example, they can experience three-dimensional landscapes or event scenes generated based on photos or videos uploaded by the user, as if they were actually there. When using AR glasses, users can overlay three-dimensional models onto their real-world view. For example, while a user is in their living room at home, they can overlay a three-dimensional landscape generated based on photos from a past trip onto their real space, creating an experience as if they were revisiting that place. When using MR devices, users can enjoy an experience that seamlessly integrates the real and virtual worlds. For example, a user can walk around a real room while interacting with virtual objects and scenes. This allows the experience unit to provide users with diverse XR experiences and propose new ways to enjoy photos and videos. Furthermore, the experience unit can provide a more immersive experience by tracking the user's movements and gaze and adding interactive elements. In this way, the experience unit can provide users with advanced XR experiences and enhance the overall value of the system.

[0073] The analysis unit can analyze photos and videos using deep learning. For example, the analysis unit can analyze photos and videos using deep learning technology. For example, the analysis unit can extract features from photos using a CNN (Convolutional Neural Network). The analysis unit can also analyze the temporal changes in videos using an RNN (Recurrent Neural Network). Furthermore, the analysis unit can generate new three-dimensional scenes based on the content of photos and videos using a GAN (Generative Opposite Network). As a result, the accuracy of photo and video analysis is improved by using deep learning. Deep learning technologies include, but are not limited to, CNNs, RNNs, and GANs. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input photos and videos into a generative AI, which can extract features from the photos and videos and generate a three-dimensional model.

[0074] The analysis unit can reproduce a three-dimensional scene using 3D modeling techniques. For example, the analysis unit can reproduce a three-dimensional scene using 3D modeling techniques. For example, the analysis unit can generate a three-dimensional scene using polygon modeling. The analysis unit can also create a detailed three-dimensional model using sculpting techniques. Furthermore, the analysis unit can reproduce a three-dimensional scene using voxel modeling. Thus, by using 3D modeling techniques, it becomes possible to reproduce a three-dimensional scene. 3D modeling techniques include, but are not limited to, polygon modeling, sculpting, and voxel modeling. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input a photograph or video into a generative AI, which can then perform 3D modeling and reproduce a three-dimensional scene.

[0075] The experience section allows users to experience things using XR devices. For example, the experience section enables users to immerse themselves in three-dimensional scenes using a VR headset. For instance, the experience section allows users to experience three-dimensional scenes in a 360-degree field of view using a VR headset. The experience section also allows users to experience the real world overlaid with three-dimensional scenes using AR glasses. For example, the experience section allows users to display three-dimensional models overlaid on real-world landscapes using AR glasses. Furthermore, the experience section allows users to enjoy a seamlessly integrated experience of the real and virtual worlds using MR devices. For example, the experience section allows users to simultaneously manipulate real-world objects and virtual-world objects using MR devices. This allows users to enjoy a three-dimensional experience by using XR devices. XR devices include, but are not limited to, VR headsets, AR glasses, and MR devices. Some or all of the above-described processes in the experience section may be performed using, for example, generative AI, or without generative AI. For example, the experience section can use generative AI to analyze user movements and gaze to provide an optimal experience.

[0076] The Experience Unit can provide specific scenarios to deepen bonds with family and friends. For example, it can provide a family reunion scenario. For instance, it can recreate a family group photo in 3D, allowing users to experience the feeling of being there. The Experience Unit can also provide adventure scenarios with friends. For example, it can recreate a travel video in 3D, allowing users to enjoy the adventure with their friends. Furthermore, the Experience Unit can provide specific event scenarios. For example, it can recreate a wedding video in 3D, allowing users to relive the emotions. In this way, by providing concrete scenarios, bonds with family and friends can be deepened. Specific scenarios include, but are not limited to, family reunion scenarios, adventure scenarios with friends, and specific event scenarios. Some or all of the processing described above in the Experience Unit may be performed using, for example, generative AI, or without generative AI. For example, the Experience Unit can use generative AI to analyze the user's emotions and reactions and provide the optimal scenario.

[0077] The experience unit can provide user interfaces and operating methods. For example, the experience unit can provide a GUI (Graphical User Interface). For example, the experience unit can provide an interface with icons and buttons that users can operate intuitively. The experience unit can also provide a voice interface. For example, the experience unit can provide an interface that allows users to operate the system using voice commands. Furthermore, the experience unit can also provide a gesture interface. For example, the experience unit can provide an interface that allows users to operate the system using hand movements and gestures. By providing user interfaces and operating methods, the system becomes easier for users to operate. User interfaces include, but are not limited to, GUIs, voice interfaces, and gesture interfaces. Some or all of the above processing in the experience unit may be performed using, for example, generative AI, or not using generative AI. For example, the experience unit can use generative AI to analyze the user's operation history and provide an optimal interface.

[0078] The reception desk can estimate the user's emotions and adjust the timing of photo and video uploads based on the estimated emotions. For example, if the user is emotional, the reception desk can prompt them to upload at a time when their emotions are heightened. For example, the reception desk can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The reception desk can also delay uploads until the user calms down if they are feeling down. For example, the reception desk can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is excited, the reception desk can prompt them to upload immediately to capture the peak of their emotions. For example, the reception desk can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for optimal timing of photo and video uploads by adjusting the upload timing based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, 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. Some or all of the above-described processes at the reception desk may be performed using, for example, a generative AI, or without using a generative AI. For example, the reception desk can input image data of the user captured by a camera into a generative AI and have the generative AI perform an estimation of the user's emotions.

[0079] The reception desk can analyze a user's past upload history and select the optimal upload method. For example, the reception desk can prioritize suggesting upload methods that the user has frequently used in the past (e.g., drag and drop). For example, the reception desk can analyze a user's past upload history and select the optimal upload method. The reception desk can also send notifications during specific time periods if the user tends to upload during those times. For example, the reception desk can suggest the optimal upload time based on the user's past upload history. Furthermore, if the reception desk frequently uploads from a particular device, it can provide an interface optimized for that device. For example, the reception desk can suggest the optimal device based on the user's past upload history. This allows the reception desk to provide the user with the optimal upload method by analyzing past upload history. Optimal upload methods include, but are not limited to, file compression, batch uploads, and real-time uploads. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's past upload history into AI, which can then select the optimal upload method.

[0080] The reception desk can filter photos and videos uploaded based on the user's current projects and areas of interest. For example, if a user is working on a travel project, the reception desk will prioritize uploading travel-related photos and videos. For example, the reception desk can analyze the user's current projects and areas of interest and select the most suitable photos and videos. The reception desk can also prioritize uploading media related to a specific event if the user is interested in that event. For example, the reception desk can select the most suitable media based on the user's areas of interest. Furthermore, if a user is working on academic research, the reception desk can prioritize uploading data related to that research. For example, the reception desk can select the most suitable data based on the user's project. This allows for the priority uploading of highly relevant media by filtering based on the user's projects and areas of interest. Filtering includes, but is not limited to, keyword filtering and content-based filtering. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's project and areas of interest into the AI, which can then select the most suitable media.

[0081] The reception desk can estimate the user's emotions and prioritize photos and videos to upload based on those estimated emotions. For example, if the user is emotional, the reception desk will prioritize uploading photos and videos that evoke heightened emotions. For instance, the reception desk can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Similarly, if the user is depressed, the reception desk can prioritize uploading photos and videos that promote a calming mood. For example, the reception desk can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is excited, the reception desk can prioritize selecting photos and videos to upload immediately. For example, the reception desk can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the reception desk to prioritize based on the user's emotions, enabling the uploading of the most suitable photos and videos. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the reception area may be performed using a generative AI, or not. For example, the reception area can input image data of the user captured by a camera into a generative AI and have the generative AI perform an estimation of the user's emotions.

[0082] The reception system can prioritize uploading highly relevant media when users upload photos and videos, taking into account their geographical location. For example, if a user is traveling, the reception system can prioritize uploading photos and videos related to their current location. For example, the reception system can analyze the user's geographical location and select the most suitable photos and videos. Furthermore, if a user is attending a specific event, the reception system can prioritize uploading media related to that event. For example, the reception system can select the most suitable media based on the user's geographical location. Additionally, if a user is at home, the reception system can prioritize uploading media related to past memories at home. For example, the reception system can select the most suitable data based on the user's geographical location. This allows for the prioritization of highly relevant media by considering geographical location. Geographical location information includes, but is not limited to, GPS data, IP addresses, and Wi-Fi location information. Some or all of the processing described above in the reception system may be performed using, for example, AI, or not. For example, the reception desk can input the user's geographical location information into the AI, which can then select the most suitable media.

[0083] The reception desk can analyze a user's social media activity when they upload photos or videos and upload relevant media. For example, the reception desk can prioritize uploading photos and videos related to what the user has recently shared on social media. For example, the reception desk can analyze a user's social media activity and select the most suitable photos and videos. The reception desk can also prioritize uploading media related to specific hashtags if the user is using them. For example, the reception desk can select the most suitable media based on the user's social media activity. Furthermore, if the reception desk is a member of a specific group or community, it can prioritize uploading media related to that group or community. For example, the reception desk can select the most suitable data based on the user's social media activity. This allows for the priority uploading of relevant media by analyzing social media activity. Social media activity includes, but is not limited to, posts, likes, and follower counts. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity into an AI, which can then select the most suitable media.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is moved, the analysis unit can display analysis results that emphasize those emotions. For example, the analysis unit can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also display analysis results that soothe the user's mood if they are depressed. For example, the analysis unit can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is excited, the analysis unit can display visually stimulating analysis results. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis based on 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. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0085] The analysis unit can adjust the level of detail in its analysis based on the importance of the photos and videos. For example, it can analyze photos and videos of important events in detail and display them in fine detail. For example, it can analyze the importance of photos and videos and select the optimal level of detail. It can also simplify the analysis of everyday photos and videos and display only the main points. For example, it can select the optimal level of detail based on the importance of the photos and videos. Furthermore, the analysis unit can perform detailed analysis specific to a particular theme for photos and videos related to that theme. For example, it can select the optimal level of detail based on the theme of the photos and videos. This allows for optimal analysis results by adjusting the level of detail based on the importance of the photos and videos. Importance includes, but is not limited to, user interest and content novelty. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of photos and videos into the AI, which can then select the optimal level of detail.

[0086] The analysis unit can apply different analysis algorithms depending on the category of the photos or videos during analysis. For example, the analysis unit can apply a landscape analysis algorithm to travel photos to emphasize the features of the landscape. For example, the analysis unit can analyze the category of photos or videos and select the optimal analysis algorithm. The analysis unit can also apply a face recognition algorithm to family photos to analyze the facial expressions of people. For example, the analysis unit can select the optimal analysis algorithm based on the category of photos or videos. Furthermore, the analysis unit can apply a motion analysis algorithm to event videos to extract important scenes. For example, the analysis unit can select the optimal analysis algorithm based on the category of photos or videos. This allows for the provision of optimal analysis results by applying an analysis algorithm appropriate to the category. Categories include, but are not limited to, landscape photos, portraits, and event videos. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the category of photos or videos into the AI, which can then select the optimal analysis algorithm.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can display a short, concise analysis result. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also display detailed analysis results if the user is relaxed. For example, the analysis unit can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is excited, the analysis unit can display visually stimulating analysis results. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the analysis unit to provide optimal analysis results by adjusting the length of the analysis based on 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. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0088] The analysis unit can determine the priority of analysis based on when the photos and videos were taken. For example, the analysis unit may prioritize the analysis of recently taken photos and videos. For example, the analysis unit can analyze when the photos and videos were taken and select the optimal priority. The analysis unit can also prioritize the analysis of photos and videos taken during a specific event period. For example, the analysis unit can select the optimal priority based on when the photos and videos were taken. Furthermore, if the user is interested in a particular period, the analysis unit can prioritize the analysis of photos and videos taken during that period. For example, the analysis unit can select the optimal priority based on when the photos and videos were taken. This allows for the provision of optimal analysis results by determining the priority of analysis based on the shooting date. The shooting date includes, but is not limited to, timestamps, metadata, and calendar information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the shooting dates of photos and videos into the AI, which can then select the optimal priority.

[0089] The analysis unit can adjust the order of analysis based on the relevance of photos and videos during the analysis process. For example, the analysis unit can analyze photos and videos related to the same event in sequence. For example, the analysis unit can analyze the relevance of photos and videos and select the optimal order. The analysis unit can also analyze photos and videos taken in the same location in sequence. For example, the analysis unit can select the optimal order based on the relevance of photos and videos. Furthermore, the analysis unit can analyze photos and videos featuring the same person in sequence. For example, the analysis unit can select the optimal order based on the relevance of photos and videos. By adjusting the order of analysis based on relevance, the optimal analysis results can be provided. Relevance includes, but is not limited to, content similarity and user interest. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of photos and videos into AI, which can then select the optimal order.

[0090] The experience unit can estimate the user's emotions and adjust the display method of the experience based on the estimated user emotions. For example, if the user is moved, the experience unit can provide a display method that emphasizes those emotions. For example, the experience unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The experience unit can also provide a display method that soothes the user's mood if they are depressed. For example, the experience unit can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is excited, the experience unit can provide a visually stimulating display method. For example, the experience unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to provide an optimal experience by adjusting the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the experience section may be performed using, for example, a generative AI, or without using a generative AI. For example, the experience section can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0091] The experience unit can optimize the current experience by referring to past experience data during the experience. For example, the experience unit can customize the current experience based on experiences the user has enjoyed in the past. For example, the experience unit can analyze the user's past experience data and provide the optimal experience. The experience unit can also adjust the current experience based on experiences the user has avoided in the past. For example, the experience unit can provide the optimal experience based on the user's past experience data. Furthermore, the experience unit can optimize the current experience based on experiences the user has given high ratings to in the past. For example, the experience unit can provide the optimal experience based on the user's past experience data. In this way, the current experience can be optimized by referring to past experience data. Past experience data includes, but is not limited to, log data, user feedback, and session history. Some or all of the above processing in the experience unit may be performed using, for example, AI, or not using AI. For example, the experience unit can input the user's past experience data into AI, and the AI ​​can provide the optimal experience.

[0092] The experience unit can apply different experience scenarios to each category of photos and videos during an experience. For example, the experience unit can apply a travel scenario to travel photos to recreate the atmosphere of a trip. For example, the experience unit can analyze the categories of photos and videos and select the optimal experience scenario. The experience unit can also apply a family scenario to family photos to emphasize family bonds. For example, the experience unit can select the optimal experience scenario based on the categories of photos and videos. Furthermore, the experience unit can apply an event scenario to event videos to recreate the atmosphere of an event. For example, the experience unit can select the optimal experience scenario based on the categories of photos and videos. In this way, by applying different scenarios to each category, the optimal experience can be provided. Experience scenarios include, but are not limited to, educational scenarios, entertainment scenarios, and training scenarios. Some or all of the above processing in the experience unit may be performed using, for example, AI, or not using AI. For example, the experience unit can input the categories of photos and videos into AI, and the AI ​​can select the optimal experience scenario.

[0093] The experience unit can estimate the user's emotions and adjust the importance of the experience based on those emotions. For example, if the user is moved, the experience unit can provide an experience that emphasizes those emotions. For example, the experience unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The experience unit can also provide an experience that soothes the user's mood if they are feeling down. For example, the experience unit can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is excited, the experience unit can provide a visually stimulating experience. For example, the experience unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to provide an optimal experience by adjusting the importance of the experience based on 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. Some or all of the above-described processes in the experience section may be performed using, for example, a generative AI, or without using a generative AI. For example, the experience section can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0094] The experience department can analyze changes in the experience based on when photos and videos were taken. For example, the experience department can provide the latest experience based on recently taken photos and videos. For example, the experience department can analyze when photos and videos were taken and provide the optimal experience. The experience department can also provide an event experience based on photos and videos taken during a specific event period. For example, the experience department can provide the optimal experience based on when photos and videos were taken. Furthermore, if the user is interested in a particular period, the experience department can provide an experience based on photos and videos taken during that period. For example, the experience department can provide the optimal experience based on when photos and videos were taken. This allows for the provision of the optimal experience by analyzing changes in the experience based on when they were taken. Changes in the experience include, but are not limited to, temporal changes and user feedback. Some or all of the above processing in the experience department may be performed using, for example, AI, or not using AI. For example, the experience department can input the timing of when photos and videos were taken into AI, and the AI ​​can provide the optimal experience.

[0095] The Experience Department can analyze the experience by referring to relevant market data for photos and videos during the experience. For example, the Experience Department can provide experiences that users are likely to be interested in based on market data. For example, the Experience Department can analyze relevant market data for photos and videos and provide the optimal experience. The Experience Department can also adjust experiences that users are likely to avoid based on market data. For example, the Experience Department can provide the optimal experience based on relevant market data for photos and videos. Furthermore, the Experience Department can optimize experiences that users are likely to rate highly based on market data. For example, the Experience Department can provide the optimal experience based on relevant market data for photos and videos. This allows for the provision of the optimal experience by referring to relevant market data. Relevant market data includes, but is not limited to, market research data and competitive analysis data. Some or all of the above processing in the Experience Department may be performed using, for example, AI, or not using AI. For example, the Experience Department can input relevant market data for photos and videos into AI, which can then provide the optimal experience.

[0096] The analysis unit can estimate the user's emotions using deep learning and select training data for the deep learning model based on the estimated user emotions. For example, if the user is emotional, the analysis unit can select data that emphasizes those emotions as training data. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Furthermore, if the user is depressed, the analysis unit can select data that soothes their mood as training data. For example, the analysis unit can record the user's voice and estimate their emotions using voice analysis technology. Additionally, if the user is excited, the analysis unit can select visually stimulating data as training data. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for optimal learning results by selecting training data 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. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0097] The analysis unit can optimize the learning algorithm by referring to past training data during deep learning training. For example, the analysis unit can select the optimal hyperparameters based on past training data. For example, the analysis unit can analyze past training data and select the optimal learning algorithm. The analysis unit can also adjust the learning speed based on past training data. For example, the analysis unit can select the optimal learning speed based on past training data. Furthermore, the analysis unit can set the convergence conditions for learning based on past training data. For example, the analysis unit can select the optimal convergence conditions based on past training data. In this way, the learning algorithm can be optimized by referring to past training data. The learning algorithm includes, but is not limited to, hyperparameter tuning and model selection. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input past training data into AI, and the AI ​​can select the optimal learning algorithm.

[0098] The analysis unit can estimate the user's emotions using deep learning and adjust the learning frequency of the deep learning model based on the estimated user emotions. For example, if the user is moved, the analysis unit can learn more frequently and generate a model that emphasizes the emotion. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also reduce the learning frequency and generate a model that soothes the mood if the user is depressed. For example, the analysis unit can record the user's voice and estimate the emotions using voice analysis technology. Furthermore, if the user is excited, the analysis unit can increase the learning frequency and generate a visually stimulating model. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This allows for optimal learning results by adjusting the learning frequency based on 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. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0099] The analysis unit can weight training data based on when photos and videos were taken during deep learning training. For example, the analysis unit can assign higher weights to recently taken photos and videos. For example, the analysis unit can analyze when photos and videos were taken and assign the optimal weights. The analysis unit can also assign higher weights to photos and videos taken during a specific event period. For example, the analysis unit can assign the optimal weights based on when photos and videos were taken. Furthermore, if the user is interested in a particular period, the analysis unit can assign higher weights to photos and videos taken during that period. For example, the analysis unit can assign the optimal weights based on when photos and videos were taken. This allows for optimal learning results by weighting training data based on when it was taken. Weighting includes, but is not limited to, data importance and data reliability. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of when photos and videos were taken into the AI, which can then perform optimal weighting.

[0100] The analysis unit can estimate the user's emotions using 3D modeling technology and adjust the 3D modeling method based on the estimated user emotions. For example, if the user is moved, the analysis unit can create a 3D model that emphasizes those emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also create a 3D model that soothes the user's mood if they are depressed. For example, the analysis unit can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is excited, the analysis unit can create a visually stimulating 3D model. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to generate an optimal 3D model by adjusting the 3D modeling method 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. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0101] The analysis unit can analyze the content of photos and videos during 3D modeling to select the optimal modeling method. For example, the analysis unit can apply a 3D modeling method specialized for landscapes to landscape photographs. For example, the analysis unit can analyze the content of photos and videos and select the optimal modeling method. The analysis unit can also apply a 3D modeling method specialized for people to family photographs. For example, the analysis unit can select the optimal modeling method based on the content of photos and videos. Furthermore, the analysis unit can apply a 3D modeling method specialized for motion analysis to event videos. For example, the analysis unit can select the optimal modeling method based on the content of photos and videos. In this way, the optimal 3D modeling method can be selected by analyzing the content of photos and videos. The optimal modeling method includes, but is not limited to, the type of content and user requirements. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the content of photos and videos into AI, and the AI ​​can select the optimal modeling method.

[0102] The analysis unit can estimate the user's emotions using 3D modeling technology and determine the priority of 3D modeling based on the estimated user emotions. For example, if the user is moved, the analysis unit will prioritize 3D modeling that emphasizes those emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also prioritize 3D modeling that soothes the user's mood if they are depressed. For example, the analysis unit can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is excited, the analysis unit can prioritize visually stimulating 3D modeling. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to generate the optimal 3D model by determining the priority of 3D modeling based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be a text-generating AI (e.g., LLM) or a multimodal generative AI, but is not limited to such examples. Some or all of the processing described above in the analysis unit may be performed using the generative AI, or not using the generative AI. For example, the analysis unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0103] The analysis unit can select the optimal modeling method when 3D modeling, taking into account the geographical location information of photos and videos. For example, the analysis unit can apply a 3D modeling method that takes into account the geographical information of the travel destination to travel photos. For example, the analysis unit can analyze the geographical location information of photos and videos and select the optimal modeling method. The analysis unit can also apply a 3D modeling method that takes into account the geographical information of the event venue to event videos. For example, the analysis unit can select the optimal modeling method based on the geographical location information of photos and videos. Furthermore, the analysis unit can apply a 3D modeling method that takes into account the geographical information of the home or relatives' homes to family photos. For example, the analysis unit can select the optimal modeling method based on the geographical location information of photos and videos. In this way, the optimal 3D modeling method can be selected by taking into account geographical location information. Geographical location information includes, but is not limited to, GPS data, IP addresses, and Wi-Fi location information. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without using AI. For example, the analysis unit can input geographical location information from photos and videos into the AI, which can then select the optimal modeling method.

[0104] The Experience Division can use XR devices to estimate the user's emotions and adjust how the XR devices are used based on the estimated emotions. For example, if the user is moved, the Experience Division can provide an XR experience that emphasizes those emotions. For example, the Experience Division can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The Experience Division can also provide an XR experience that soothes the user's mood if they are depressed. For example, the Experience Division can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is excited, the Experience Division can provide a visually stimulating XR experience. For example, the Experience Division can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to provide an optimal XR experience by adjusting how the XR devices are used based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, 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. Some or all of the above-described processes in the experience section may be performed using, for example, a generative AI, or without using a generative AI. For example, the experience section can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0105] The Experience Unit can select the optimal usage method when using an XR device by referring to the user's past experience history. For example, the Experience Unit can customize the current experience based on the XR experiences the user has enjoyed in the past. For example, the Experience Unit can analyze the user's past experience history and provide the optimal usage method. The Experience Unit can also adjust the current experience based on XR experiences the user has avoided in the past. For example, the Experience Unit can provide the optimal usage method based on the user's past experience history. Furthermore, the Experience Unit can optimize the current experience based on XR experiences the user has given high ratings to in the past. For example, the Experience Unit can provide the optimal usage method based on the user's past experience history. In this way, the optimal XR experience can be provided by referring to past experience history. Past experience history includes, but is not limited to, log data, user feedback, and session history. Some or all of the above processing in the Experience Unit may be performed using, for example, AI, or not using AI. For example, the Experience Unit can input the user's past experience history into AI, and the AI ​​can provide the optimal usage method.

[0106] The Experience Unit can use XR devices to estimate the user's emotions and adjust the operation procedures of the XR devices based on the estimated user emotions. For example, if the user is moved, the Experience Unit can provide operation procedures that emphasize those emotions. For example, the Experience Unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. The Experience Unit can also provide operation procedures that soothe the user's mood if they are depressed. For example, the Experience Unit can record the user's voice and estimate emotions using voice analysis technology. Furthermore, if the user is excited, the Experience Unit can provide visually stimulating operation procedures. For example, the Experience Unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. This allows for the provision of an optimal XR experience by adjusting operation procedures based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, 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. Some or all of the above-described processes in the experience section may be performed using, for example, a generative AI, or without using a generative AI. For example, the experience section can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0107] The experience unit can select the optimal usage method when using an XR device, taking into account the user's device information. For example, if the user is using a specific XR device, the experience unit can provide a usage method optimized for that device. For example, the experience unit can analyze the user's device information and provide the optimal usage method. Furthermore, if the user is using multiple XR devices, the experience unit can provide a usage method that takes into account the interoperability between devices. For example, the experience unit can provide the optimal usage method based on the user's device information. In addition, if the user is using a new XR device, the experience unit can provide a usage method that takes into account the characteristics of that device. For example, the experience unit can provide the optimal usage method based on the user's device information. In this way, by considering device information, the optimal XR experience can be provided. Device information includes, but is not limited to, the type, performance, and settings of the device. Some or all of the above processing in the experience unit may be performed using, for example, AI, or not using AI. For example, the experience unit can input the user's device information into AI, and the AI ​​can provide the optimal usage method.

[0108] The experience unit can provide specific scenarios, estimate the user's emotions, and adjust the scenario content based on the estimated emotions. For example, if the user is moved, the experience unit can provide a scenario that emphasizes those emotions. For example, the experience unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The experience unit can also provide a scenario to soothe a depressed user. For example, the experience unit can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is excited, the experience unit can provide a visually stimulating scenario. For example, the experience unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to provide an optimal experience by adjusting the scenario content 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. Some or all of the above-described processes in the experience section may be performed using, for example, a generative AI, or without using a generative AI. For example, the experience section can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0109] The experience department can select the optimal scenario by referring to the user's past experience data when providing scenarios. For example, the experience department can customize the current scenario based on scenarios the user has preferred in the past. For example, the experience department can analyze the user's past experience data and provide the optimal scenario. The experience department can also adjust the current scenario based on scenarios the user has avoided in the past. For example, the experience department can provide the optimal scenario based on the user's past experience data. Furthermore, the experience department can optimize the current scenario based on scenarios the user has given high ratings to in the past. For example, the experience department can provide the optimal scenario based on the user's past experience data. In this way, the optimal scenario can be provided by referring to past experience data. Past experience data includes, but is not limited to, log data, user feedback, and session history. Some or all of the above processing in the experience department may be performed using, for example, AI, or not using AI. For example, the experience department can input the user's past experience data into AI, and the AI ​​can provide the optimal scenario.

[0110] The experience unit can provide specific scenarios, estimate the user's emotions, and prioritize scenarios based on the estimated emotions. For example, if the user is emotional, the experience unit will prioritize scenarios that emphasize those emotions. For instance, the experience unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Similarly, if the user is depressed, the experience unit can prioritize scenarios that soothe their mood. For example, the experience unit can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is excited, the experience unit can prioritize visually stimulating scenarios. For example, the experience unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to provide an optimal experience by prioritizing scenarios based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, 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. Some or all of the above-described processes in the experience section may be performed using, for example, a generative AI, or without using a generative AI. For example, the experience section can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0111] The Experience Unit can provide the most suitable scenario by considering the user's geographical location information when providing scenarios. For example, if the user is traveling, the Experience Unit can provide a scenario related to their travel destination. For example, the Experience Unit can analyze the user's geographical location information and provide the most suitable scenario. The Experience Unit can also provide a scenario related to an event if the user is participating in a specific event. For example, the Experience Unit can provide the most suitable scenario based on the user's geographical location information. Furthermore, if the user is at home, the Experience Unit can provide a scenario related to memories at home. For example, the Experience Unit can provide the most suitable scenario based on the user's geographical location information. In this way, the best scenario can be provided by considering geographical location information. Geographical location information includes, but is not limited to, GPS data, IP addresses, and Wi-Fi location information. Some or all of the above processing in the Experience Unit may be performed using, for example, AI, or not using AI. For example, the Experience Unit can input the user's geographical location information into AI, and the AI ​​can provide the most suitable scenario.

[0112] The experience unit provides a user interface and operating methods, estimates the user's emotions, and adjusts the interface display based on the estimated user emotions. For example, if the user is moved, the experience unit provides an interface that emphasizes those emotions. For example, the experience unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The experience unit can also provide an interface that soothes the user's mood if they are depressed. For example, the experience unit can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is excited, the experience unit can provide a visually stimulating interface. For example, the experience unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to provide an optimal interface by adjusting the interface display based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the experience section may be performed using, for example, a generative AI, or without using a generative AI. For example, the experience section can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0113] The user experience unit can select the optimal display method by referring to the user's past operation history when displaying an interface. For example, the user experience unit can customize the current interface based on interfaces the user has preferred in the past. For example, the user experience unit can analyze the user's past operation history and provide the optimal display method. The user experience unit can also adjust the current interface based on interfaces the user has avoided in the past. For example, the user experience unit can provide the optimal display method based on the user's past operation history. Furthermore, the user experience unit can optimize the current interface based on interfaces the user has given high ratings to in the past. For example, the user experience unit can provide the optimal display method based on the user's past operation history. In this way, the optimal interface can be provided by referring to past operation history. Past operation history includes, but is not limited to, log data, user feedback, and session history. Some or all of the above processing in the user experience unit may be performed using, for example, AI, or not using AI. For example, the user experience unit can input the user's past operation history into AI, and the AI ​​can provide the optimal display method.

[0114] The user experience unit can select the optimal display method when displaying an interface, taking into account the user's device information. For example, if the user is using a smartphone, the user experience unit can provide a display method that matches the screen size. For example, the user experience unit can analyze the user's device information and provide the optimal display method. Also, if the user is using a tablet, the user experience unit can provide a display method optimized for a larger screen. For example, the user experience unit can provide the optimal display method based on the user's device information. Furthermore, if the user is using a smartwatch, the user experience unit can provide a concise and highly visible display method. For example, the user experience unit can provide the optimal display method based on the user's device information. In this way, by considering device information, the optimal interface can be provided. Device information includes, but is not limited to, the type, performance, and settings of the device. Some or all of the above processing in the user experience unit may be performed using, for example, AI, or not using AI. For example, the user experience unit can input the user's device information into AI, and the AI ​​can provide the optimal display method.

[0115] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0116] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is emotional, it can prioritize analyzing photos and videos that emphasize those emotions. If the user is depressed, it can prioritize analyzing photos and videos that soothe their mood. Furthermore, if the user is excited, it can prioritize analyzing visually stimulating photos and videos. By adjusting the analysis priority based on the user's emotions, the system can provide optimal analysis results. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0117] The experience component can estimate the user's emotions and adjust the display of the experience based on those estimated emotions. For example, if the user is moved, it can provide a display that emphasizes those emotions. If the user is depressed, it can provide a display that soothes their mood. Furthermore, if the user is excited, it can provide a visually stimulating display. In this way, by adjusting the display based on the user's emotions, the optimal experience can be provided. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] The reception desk can estimate the user's emotions and adjust the timing of photo and video uploads based on those estimates. For example, if a user is emotional, it can prompt them to upload when their emotions are heightened. If a user is depressed, it can delay the upload until they calm down. Furthermore, if a user is excited, it can prompt them to upload immediately to capture the peak of their emotions. This allows for optimal photo and video uploads by adjusting the upload timing based on the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is moved, the analysis results can be displayed in a way that emphasizes those emotions. If the user is depressed, the analysis results can be displayed in a way that soothes their mood. Furthermore, if the user is excited, the analysis results can be displayed in a way that is visually stimulating. In this way, by adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0120] The experience component can estimate the user's emotions and adjust the importance of the experience based on those emotions. For example, if the user is moved, it can provide an experience that emphasizes that emotion. If the user is depressed, it can provide an experience that soothes their mood. Furthermore, if the user is excited, it can provide a visually stimulating experience. In this way, by adjusting the importance of the experience based on the user's emotions, the optimal experience can be provided. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0121] The analysis unit can analyze the content of photos and videos and select the optimal analysis algorithm. For example, a landscape-specific analysis algorithm can be applied to landscape photos. Similarly, a people-specific analysis algorithm can be applied to family photos. Furthermore, an action-analysis-specific analysis algorithm can be applied to event videos. In this way, the optimal analysis algorithm can be selected by analyzing the content of photos and videos. The optimal analysis algorithm may include, but is not limited to, the type of content and user requirements.

[0122] The experience section provides user interfaces and operating methods, and can select the optimal display method by referring to the user's past operation history. For example, the current interface can be customized based on interfaces the user has preferred in the past. It can also be adjusted based on interfaces the user has avoided in the past. Furthermore, the current interface can be optimized based on interfaces the user has given high ratings to in the past. In this way, the optimal interface can be provided by referring to past operation history. Past operation history includes, but is not limited to, log data, user feedback, and session history.

[0123] The reception desk can analyze a user's past upload history and select the optimal upload method. For example, it can prioritize suggesting upload methods the user has frequently used in the past (e.g., drag and drop). It can also send notifications if the user tends to upload during specific time periods. Furthermore, if a user frequently uploads from a particular device, it can provide an interface optimized for that device. This allows the system to provide the user with the best possible upload method by analyzing their past upload history. Optimal upload methods include, but are not limited to, file compression, batch uploads, and real-time uploads.

[0124] The analysis unit can prioritize analysis based on when the photos and videos were taken. For example, it can prioritize the analysis of recently taken photos and videos. It can also prioritize the analysis of photos and videos taken during a specific event period. Furthermore, if the user is interested in a particular time period, it can prioritize the analysis of photos and videos taken during that period. By prioritizing analysis based on the time of capture, the system can provide optimal analysis results. The time of capture includes, but is not limited to, timestamps, metadata, and calendar information.

[0125] The experience section can provide optimal scenarios by considering the user's geographical location. For example, if the user is traveling, it can provide scenarios related to their travel destination. Similarly, if the user is participating in a specific event, it can provide scenarios related to that event. Furthermore, if the user is at home, it can provide scenarios related to memories at home. This allows for the provision of optimal scenarios by considering geographical location. Geographical location information includes, but is not limited to, GPS data, IP addresses, and Wi-Fi location information.

[0126] The following briefly describes the processing flow for example form 2.

[0127] Step 1: The reception desk allows users to upload photos and videos. These uploads may include, but are not limited to, formats such as JPEG, PNG, MP4, and AVI. The reception desk can, for example, allow users to upload photos and videos via drag-and-drop. It can also allow users to select and upload photos and videos from specific folders. Furthermore, the reception desk can allow users to directly upload photos and videos from cloud storage. Step 2: The analysis unit uses deep learning to analyze the photos and videos uploaded by the reception unit and generate a 3D model. Deep learning can utilize technologies such as CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network). For example, the analysis unit can use CNN to extract features from photos and generate a 3D model. The analysis unit can also use RNN to analyze the temporal changes in videos and reconstruct 3D scenes. Furthermore, the analysis unit can use GAN (Generative Opposite Network) to generate new 3D scenes based on the content of photos and videos. Step 3: The experience unit enables the user to experience the three-dimensional model generated by the analysis unit. The experience unit enables the user to enjoy a three-dimensional experience using XR devices such as VR headsets or AR glasses. For example, the experience unit enables the user to immerse themselves in a three-dimensional scene using a VR headset. The experience unit also allows the user to experience the real world overlaid with the three-dimensional scene using AR glasses. Furthermore, the experience unit allows the user to enjoy a seamlessly integrated experience of the real and virtual worlds using MR devices.

[0128] 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.

[0129] 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 text generation AI, image generation AI, and multimodal generation AI. 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 with 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 from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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 various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts 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.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0130] 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.

[0131] For example, the reception unit is implemented by the reception device 38 of the smart device 14, allowing users to upload photos and videos. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes photos and videos using deep learning and generates a three-dimensional model. The experience unit is implemented by the output device 40 of the smart device 14, allowing users to enjoy a three-dimensional experience using an XR device. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0132] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0146] 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.

[0147] For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing users to upload photos and videos. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes photos and videos using deep learning and generates a three-dimensional model. The experience unit is implemented by the speaker 240 of the smart glasses 214, allowing users to enjoy a three-dimensional experience using an XR device. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0148] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0162] 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.

[0163] For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, allowing users to upload photos and videos. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12, which uses deep learning to analyze photos and videos and generate a three-dimensional model. The experience unit is implemented by the display 343 of the headset terminal 314, allowing users to enjoy a three-dimensional experience using an XR device. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0164] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.).

[0177] 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.

[0178] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0179] 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.

[0180] For example, the reception section is implemented by the microphone 238 of robot 414, allowing users to upload photos and videos. The analysis section is implemented by the specific processing unit 290 of the data processing device 12, which uses deep learning to analyze photos and videos and generate a three-dimensional model. The experience section is implemented by the speaker 240 of robot 414, allowing users to enjoy a three-dimensional experience using an XR device. The correspondence between each section and the devices and control units is not limited to the example described above, and various modifications are possible.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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."

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] (Note 1) A reception area for uploading photos or videos, An analysis unit analyzes photos or videos uploaded by the reception unit and generates a three-dimensional model, The system includes an experience unit in which the user experiences the three-dimensional model generated by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyzing photos and videos using deep learning. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Recreate a three-dimensional scene using 3D modeling technology. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned experience section is, What users experience using XR devices The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned experience section is, Provides specific scenarios to deepen bonds with family and friends. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned experience section is, Provides user interfaces and operating methods. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of photo and video uploads based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past upload history and select the optimal upload method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When uploading photos or videos, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes the photos and videos to upload based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When uploading photos and videos, the system prioritizes uploading media that is more relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When uploading photos or videos, the system analyzes the user's social media activity and uploads relevant media. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the photos and videos. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the photos or videos. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the photos and videos were taken. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of photos and videos. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned experience section is, It estimates the user's emotions and adjusts how the experience is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned experience section is, During the user experience, past user data is referenced to optimize the current experience. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned experience section is, During the experience, different experience scenarios are applied for each category of photos and videos. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned experience section is, It estimates the user's emotions and adjusts the importance of the experience based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned experience section is, During the experience, we analyze how the experience changes based on when photos and videos were taken. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned experience section is, During the user experience, we analyze the experience by referring to relevant market data for photos and videos. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, We use deep learning to estimate user emotions and select training data for the deep learning model based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit, During deep learning training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit, We use deep learning to estimate user emotions and adjust the training frequency of the deep learning model based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit, During deep learning training, the training data is weighted based on when the photos and videos were taken. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit, We use 3D modeling technology to estimate user emotions and adjust the 3D modeling method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit, During 3D modeling, the content of photos and videos is analyzed to select the optimal modeling method. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned analysis unit, 3D modeling technology is used to estimate user emotions, and the priority of 3D modeling is determined based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned analysis unit, When 3D modeling, the optimal modeling method is selected by considering the geographical location information of photos and videos. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned experience section is, The system uses XR devices to estimate the user's emotions and adjusts how the XR devices are used based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned experience section is, When using an XR device, the system selects the optimal usage method by referring to the user's past experience history. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned experience section is, The system uses XR devices to estimate the user's emotions and adjusts the operation of the XR devices based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned experience section is, When using an XR device, the optimal usage method is selected by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned experience section is, It provides specific scenarios, estimates user emotions, and adjusts the scenario content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned experience section is, When providing scenarios, the system selects the most suitable scenario by referring to the user's past experience data. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned experience section is, It provides specific scenarios, estimates user emotions, and determines the priority of scenarios based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned experience section is, When providing scenarios, we will provide the optimal scenario considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned experience section is, It provides a user interface and operating methods, estimates the user's emotions, and adjusts the interface display based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned experience section is, When displaying the interface, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned experience section is, When displaying the interface, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0200] 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 photos or videos, An analysis unit analyzes photos or videos uploaded by the reception unit and generates a three-dimensional model, The system includes an experience unit in which the user experiences the three-dimensional model generated by the analysis unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyzing photos and videos using deep learning. The system according to feature 1.

3. The aforementioned analysis unit, Recreate a three-dimensional scene using 3D modeling technology. The system according to feature 1.

4. The aforementioned experience section is, What users experience using XR devices The system according to feature 1.

5. The aforementioned experience section is, Provides specific scenarios to deepen bonds with family and friends. The system according to feature 1.

6. The aforementioned experience section is, Provides user interfaces and operating methods. The system according to feature 1.

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

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

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