system

Abstract

The system according to this embodiment aims to efficiently update image data in a virtual space and create a space that closely resembles reality. [Solution] The system according to the embodiment comprises a lending unit, a shooting unit, a supplementation unit, an update unit, and a reward unit. The lending unit lends a glasses-type device equipped with communication functions to the user. The shooting unit uses the glasses-type device lent by the lending unit to shoot video and upload it to a virtual space. The supplementation unit analyzes the video uploaded by the shooting unit and supplements any gaps with a generation AI. The update unit updates the image with a new one based on the coordinate data. The reward unit provides a reward according to the number of uploads that are adopted.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 the method for updating the image data in the virtual space is limited, and it is particularly difficult to collect data for the walking part.

[0005] The system according to the embodiment aims to efficiently update the image data in the virtual space and create a space closer to reality.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a lending unit, a shooting unit, a supplementation unit, an update unit, and a reward unit. The lending unit lends a glasses-type device equipped with communication functions to the user. The shooting unit uses the glasses-type device lent by the lending unit to shoot video and upload it to a virtual space. The supplementation unit analyzes the video uploaded by the shooting unit and supplements any gaps with images generated by AI. The update unit updates the images with new ones based on coordinate data. The reward unit provides a reward based on the number of uploads that are adopted. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently update image data in a virtual space and create a space that closely resembles reality. [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, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The virtual space generation system according to an embodiment of the present invention is a system for providing a virtual space nearly identical to reality to people who cannot travel far for economic or physical reasons. This virtual space generation system rents a glasses-type device equipped with communication functions to the user for 100 yen per month. When the user puts on the glasses and turns on communication, video recording begins and images are automatically updated. These images are uploaded to the virtual space, creating a space that closely resembles reality. While it uses a method similar to a map service, it is human-operated, allowing for walking sections, and gaps in the images are filled in by a generation AI. When new images are uploaded based on coordinate data, they are replaced, and the user receives a reward based on the number of uploads adopted. Items requiring permission, such as shops, are not uploaded, and once a certain amount has accumulated, the operator negotiates for permission. This system enables a service that provides a more detailed metaverse space as a platform than virtual globe services. Thus, the virtual space generation system can provide a virtual space nearly identical to reality to people who cannot travel far for economic or physical reasons. For example, the user rents a glasses-type device equipped with communication functions for 100 yen per month. This glasses-type device features video recording and communication capabilities. When a user puts on the glasses and turns on communication, video recording automatically begins. For example, if a user walks around town, their movements are recorded as a video and uploaded to the virtual space in real time. Next, the uploaded video is analyzed by a generative AI, and any missing images are filled in. For instance, if some images are missing from a video shot by a user, the generative AI fills in those parts, creating a space that closely resembles reality. Also, if new images are uploaded based on the coordinate data, the old images are replaced. Furthermore, users are rewarded based on the number of uploads that are adopted. For example, if a video shot by a user is used by many people, that user will receive a reward. This encourages users to actively shoot videos and contribute to enriching the virtual space. In addition, users cannot upload videos to places that require permission, such as shops, without permission. Once a certain amount of data has been collected, the operators will negotiate.This allows for the provision of detailed virtual spaces while avoiding privacy and copyright issues. This system enables people who cannot travel far for economic or physical reasons to enjoy virtual spaces that are nearly identical to reality. For example, elderly people and people with disabilities who find travel difficult can visit tourist destinations around the world from the comfort of their homes. Furthermore, by providing a more detailed metaverse space than virtual globe services, it is expected to have applications in various fields, including tourism and education.

[0029] The virtual space generation system according to this embodiment comprises a lending unit, a shooting unit, a completion unit, an update unit, and a reward unit. The lending unit lends a glasses-type device equipped with communication functions to the user. The lending unit lends the glasses-type device for, for example, 100 yen per month. The glasses-type device has a video recording function and a communication function, and when the user puts on the glasses and turns on communication, video recording starts automatically. The shooting unit has the glasses-type device lent by the lending unit record video and upload it to the virtual space. For example, if the user is walking in a city, the shooting unit records the scene as a video and uploads it to the virtual space in real time. The completion unit analyzes the video uploaded by the shooting unit and completes the gaps with a generation AI. For example, if some images are missing in the video recorded by the user, the generation AI completes those parts and creates a space that is close to reality. The generation AI generates the gaps using technologies such as GAN (Generative Opposite Network) or VAE (Variational Autoencoder). The update unit updates new images based on coordinate data. For example, if a new image is uploaded based on coordinate data, the update unit replaces the old image. Coordinate data is acquired in the form of GPS data, 3D coordinates, pixel coordinates, etc. The reward unit provides rewards according to the number of uploads that are adopted. For example, if a video shot by a user is used by many people, the reward unit pays the user a reward. The reward is paid in the form of cash, points, gift cards, etc. As a result, the virtual space generation system according to the embodiment can efficiently summarize, cluster, analyze trends, and extract elements from the user's ideas.

[0030] The lending department provides users with glasses-type devices equipped with communication functions. For example, the lending department may rent out the glasses-type devices for 100 yen per month. The glasses-type devices have video recording and communication functions, and when the user puts on the glasses and turns on communication, video recording starts automatically. Specifically, the glasses-type devices have a built-in high-resolution camera that captures the user's field of view in real time. The communication function allows the recorded videos to be instantly uploaded to a cloud server using Wi-Fi or mobile data communication. The glasses-type devices are lightweight and designed to withstand long periods of use, and have a long battery life, making them suitable for daily use by users. Furthermore, the glasses-type devices are equipped with a voice command function, allowing users to operate the device hands-free. For example, they recognize voice commands such as "start recording" and "stop recording" and operate according to the user's instructions. This allows users to record videos naturally and contribute to the virtual space generation system. The lending department also provides maintenance and support for the glasses-type devices, and has a system in place to respond quickly if the user encounters a malfunction or problem with the device. This allows users to use the device with peace of mind and contributes to improving the quality of the virtual space generation system.

[0031] The recording unit uses glasses-type devices provided by the lending unit to record videos and upload them to a virtual space. For example, if a user is walking in a city, the recording unit records their movements as a video and uploads it to the virtual space in real time. Specifically, the camera on the glasses-type device captures the user's field of view, compresses the video data, and sends it to a cloud server. The cloud server analyzes the received video data and places it in a way that corresponds to specific coordinates in the virtual space. This recreates the user's walking route and the scenery they saw in the virtual space. The recording unit uses appropriate compression algorithms to maintain the quality of the video data and minimize data loss. In addition, the recording unit has a function that automatically blurs the faces of people in the video using facial recognition technology to protect user privacy. This allows users to record videos with peace of mind without infringing on the privacy of others. Furthermore, the recording unit also provides a function to tag videos recorded by users, making it easier to search and filter them. For example, it automatically assigns tags related to specific locations or events, making it easy for other users to find the videos. This allows the photography department to enrich the content of the virtual space and promote interaction among users.

[0032] The interpolation unit analyzes the video uploaded by the shooting unit and uses a generation AI to fill in the gaps with images. For example, if some images are missing from a video shot by a user, the interpolation unit uses the generation AI to fill in those parts and create a space that closely resembles reality. The generation AI generates the gap images using technologies such as GAN (Generative Opposite Network) or VAE (Variational Autoencoder). Specifically, GAN is a technology that generates realistic images by having two neural networks, a generator and a discriminator, compete. The generator generates an image of the missing part, and the discriminator determines whether that image is real or fake. By repeating this process, the generator improves its ability to generate more realistic images. On the other hand, VAE is a technology that fills in the missing parts by compressing the input data into a low-dimensional latent space and reconstructing it from that latent space. As a result, the interpolation unit can improve the quality of the video shot by the user and enhance the reality of the virtual space. Furthermore, the interpolation unit also has a filtering function to verify the images generated by the generation AI and guarantee their quality. For example, if a generated image is unnatural or contains incorrect information, it can be detected and corrected. Furthermore, the complementary unit can collect user feedback and continuously improve the performance of the generation AI. This allows the complementary unit to consistently provide a high-quality virtual space and enhance user satisfaction.

[0033] The update unit updates images based on coordinate data. For example, if a new image is uploaded based on coordinate data, the update unit replaces the old image. Coordinate data can be obtained in various formats, such as GPS data, 3D coordinates, or pixel coordinates. Specifically, it analyzes the location information of videos taken by users and identifies the coordinates in the virtual space corresponding to those locations. When a new image is uploaded, the update unit automatically replaces the old image corresponding to those coordinates. This ensures that the virtual space is always updated with the latest information, reflecting the real world that changes in real time. Furthermore, the update unit can integrate data from multiple users to build a more detailed and accurate virtual space. For example, if different users film the same location, it integrates their video data to generate a higher resolution and more detailed image. The update unit also has a function to archive past data and restore it as needed. This allows for the recreation of the virtual space at a specific point in time, and can be used for the preservation and analysis of historical data. In addition, the update unit has an algorithm that evaluates the quality of data provided by users and prioritizes the adoption of high-quality data. This maintains the quality of the virtual space and ensures that users always receive the best possible experience.

[0034] The rewards department provides rewards based on the number of uploads that are adopted. For example, if a user's video is used by many people, the rewards department will pay that user a reward. Rewards can be paid in the form of cash, points, gift cards, etc. Specifically, the rewards department calculates rewards based on metrics such as the number of views, ratings, and comments on videos provided by users. This allows users to receive rewards commensurate with their contributions, increasing their motivation. Furthermore, the rewards department can also hold events that regularly announce rankings and offer special rewards to top-ranking users. This promotes competition among users and provides more high-quality content to the virtual space. The rewards department also provides a dashboard to visualize user contributions, allowing users to check their achievements. This allows users to realize how much their activities contribute to the virtual space and encourages further contributions. In addition, the rewards department can collect feedback from users and use it to improve the rewards system. For example, it can reflect opinions on how rewards are calculated and paid out to create a fairer and more attractive rewards system. This allows the rewards department to increase user satisfaction and contribute to the revitalization of the virtual space generation system.

[0035] The interpolation unit uses generative AI to fill in gaps in the image. For example, if some images are missing from a video shot by a user, the interpolation unit uses generative AI to fill in those missing parts, creating a space that closely resembles reality. The generative AI generates images of the gaps using technologies such as GAN (Generative Opposite Network) or VAE (Variational Autoencoder). This allows for the automatic interpolation of images of gaps using generative AI.

[0036] The update unit updates images based on coordinate data. For example, if a new image is uploaded based on coordinate data, the update unit replaces the old image. Coordinate data is acquired in various formats, such as GPS data, 3D coordinates, or pixel coordinates. This allows the system to provide the latest information by updating images based on coordinate data.

[0037] The rewards department provides rewards based on the number of uploads that are accepted. For example, if a user's video is used by many people, the rewards department will pay that user a reward. The rewards may be paid in the form of cash, points, gift cards, etc. This system encourages active user participation by rewarding users based on the number of accepted uploads.

[0038] The filming department uploads videos to the virtual space in real time. For example, if a user is walking around town, the filming department will capture their movements as a video and upload it to the virtual space in real time. This real-time uploading of videos to the virtual space enables the provision of information in an immediate manner.

[0039] The lending department will lend out glasses-type devices for 100 yen per month. For example, the lending department will lend out glasses-type devices for 100 yen per month. This will reduce the financial burden by lending out glasses-type devices for 100 yen per month.

[0040] The lending department analyzes the user's past usage history and selects the optimal lending method. For example, the lending department may lend equipment according to the time slots the user frequently used in the past. For example, the lending department may provide a customized lending method by considering the functions the user preferred to use in the past. For example, the lending department may propose the optimal lending period based on the user's past usage history. In this way, by analyzing past usage history, the lending department can provide the user with the most suitable lending method.

[0041] The lending department filters the glasses-type devices provided based on the user's current lifestyle and areas of interest. For example, if a user is traveling, the lending department prioritizes lending devices with features useful for travel. If a user is interested in health, the lending department lends devices with enhanced health management features. If a user is studying, the lending department lends devices with learning support features. By lending devices tailored to the user's lifestyle and areas of interest, the lending department improves user satisfaction.

[0042] The lending department prioritizes lending relevant devices to users based on their geographical location when providing glasses-type devices. For example, if a user is in an urban area, the lending department will lend a device suitable for urban use. If a user is in a natural environment, the lending department will lend an outdoor-oriented device. If a user is overseas, the lending department will lend a multilingual device. By lending devices based on the user's geographical location, the lending department aims to improve user satisfaction.

[0043] The lending department analyzes the user's social media activity when lending out glasses-type devices and lends out relevant devices. For example, if a user frequently posts photos, the lending department will lend them a device with a high-resolution camera. If a user streams videos, the lending department will lend them a device specifically designed for video recording. If a user shares health information, the lending department will lend them a device with enhanced health management functions. By lending out devices based on the user's social media activity, the lending department aims to improve user satisfaction.

[0044] The camera crew adjusts the level of detail during shooting based on the importance of the video. For example, when shooting important events, the crew will shoot in high resolution for detailed footage. When shooting everyday scenes, for example, the crew will shoot in standard resolution. When shooting specific locations or objects, for example, the crew will use the zoom function to capture detailed footage. This allows for high-quality shooting of important scenes by adjusting the level of detail based on the importance of the video.

[0045] The camera applies different shooting algorithms depending on the video category during shooting. For example, when shooting landscapes, it uses a wide-angle lens to capture a wide area. When shooting people, for example, it uses portrait mode to blur the background. When shooting animals, for example, it uses a fast shutter that responds to movement. By applying shooting algorithms appropriate to the video category, it can provide optimal footage.

[0046] The filming department prioritizes filming based on the timing of the video. For example, they prioritize filming important events. For example, they postpone filming everyday scenes. For example, they prioritize filming seasonal scenes. By prioritizing filming based on the timing of the video, they can ensure that important scenes are filmed first.

[0047] The camera crew adjusts the shooting order based on the relevance of the video footage. For example, they might prioritize shooting important scenes and edit them later. Or, they might shoot highly relevant scenes consecutively. Or, they might postpone shooting less relevant scenes. By adjusting the shooting order based on the relevance of the video footage, efficient shooting becomes possible.

[0048] The interpolation unit improves the accuracy of interpolation by considering the relationships between video clips during the interpolation process. For example, the interpolation unit considers the continuity of scenes within the video when performing interpolation. For example, the interpolation unit considers the relationships between different scenes within the video when performing interpolation. For example, the interpolation unit prioritizes the interpolation of important scenes within the video when performing interpolation. This improves the accuracy of interpolation by considering the relationships between video clips.

[0049] The interpolation unit considers the attributes of the videographer during interpolation. For example, the interpolation unit adjusts the interpolation style based on the videographer's age and gender. For example, the interpolation unit adjusts the content of the interpolation based on the videographer's interests and concerns. For example, the interpolation unit improves the accuracy of the interpolation based on the videographer's past shooting history. This makes it possible to perform more appropriate interpolation by considering the videographer's attributes.

[0050] The interpolation unit considers the geographical distribution of the video during interpolation. For example, it prioritizes interpolating images of geographically important locations. For example, it interpolates images of geographically relevant locations consecutively. For example, it postpones interpolating images of geographically irrelevant locations. This allows for more accurate interpolation by considering geographical distribution.

[0051] The interpolation unit improves the accuracy of interpolation by referring to related literature for the video during the interpolation process. For example, the interpolation unit performs interpolation based on information obtained from related literature. For example, the interpolation unit performs interpolation by referring to images from related literature. For example, the interpolation unit improves the accuracy of interpolation by using data from related literature. In this way, the accuracy of interpolation is improved by referring to related literature.

[0052] The update unit optimizes the current update by referring to past update data during the update process. For example, the update unit selects the optimal update method based on past update data. For example, the update unit optimizes the current update based on information obtained from past update data. For example, the update unit analyzes past update data to improve the accuracy of the current update. This allows the current update to be optimized by referring to past data.

[0053] The update unit applies different update methods depending on the video category during the update process. For example, when updating landscape videos, the update unit uses a wide-angle lens to capture a wide area. For example, when updating videos of people, the update unit uses portrait mode to blur the background. For example, when updating animal videos, the update unit uses a high-speed shutter that responds to movement. By applying different methods to each category, optimal updates can be achieved.

[0054] The update unit analyzes the changes in the update based on the video's shooting date. For example, the update unit analyzes the changes in the update based on the shooting date of important events. For example, the update unit analyzes the changes in the update based on the shooting date of seasonal scenes. For example, the update unit analyzes the changes in the update based on the shooting date of everyday scenes. This allows for optimal updates by analyzing the changes in the update based on the shooting date.

[0055] The update unit analyzes the update by referring to relevant market data for the video during the update process. For example, the update unit selects the optimal update method based on the relevant market data. For example, the update unit improves the accuracy of the update based on the information obtained from the relevant market data. For example, the update unit analyzes the relevant market data and evaluates the effectiveness of the update. Thus, by referring to relevant market data, the accuracy of the update is improved.

[0056] The rewards department analyzes users' past contributions to select the optimal reward distribution method when allocating rewards. For example, the rewards department determines reward distribution based on users' past contributions. For example, the rewards department analyzes users' past contributions and proposes the optimal reward distribution method. For example, the rewards department adjusts reward distribution considering users' past contributions. This allows for optimal reward distribution by analyzing past contributions.

[0057] The rewards department customizes reward distribution based on the user's current living situation. For example, the rewards department adjusts reward distribution based on the user's current living situation. For example, the rewards department customizes reward distribution methods considering the user's living situation. For example, the rewards department optimizes reward distribution according to the user's living situation. This improves user satisfaction by customizing reward distribution based on the user's current living situation.

[0058] The rewards department selects the optimal reward distribution method when allocating rewards, taking into account the user's geographical location information. For example, the rewards department adjusts reward distribution based on the user's geographical location information. For example, the rewards department customizes reward distribution methods by considering the user's geographical location information. For example, the rewards department optimizes reward distribution according to the user's geographical location information. This makes it possible to distribute rewards optimally by considering geographical location information.

[0059] The rewards department analyzes users' social media activity when distributing rewards and proposes methods for distributing rewards. For example, the rewards department adjusts reward distribution based on users' social media activity. For example, the rewards department analyzes users' social media activity and proposes the optimal method for distributing rewards. For example, the rewards department optimizes reward distribution according to users' social media activity. This allows the department to propose the optimal method for distributing rewards by analyzing social media activity.

[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 virtual space generation system may also include a health management unit that monitors the user's health status. For example, the health management unit could measure the user's heart rate and blood pressure and issue a warning if an abnormality is detected. For example, if the user is using the glasses-type device for an extended period, the health management unit could send a notification prompting them to take a break. For example, the health management unit could analyze the user's health data and provide appropriate exercise and dietary advice. This allows the system to support the user's health maintenance by monitoring their health status and providing appropriate advice.

[0062] The virtual space generation system can also include a suggestion unit that analyzes the user's behavioral history and proposes the most suitable virtual space. For example, the suggestion unit could suggest new destinations based on places the user has visited or been interested in in the past. Alternatively, it could analyze the user's behavioral patterns and suggest the most optimal time to use the virtual space. Finally, it could suggest a virtual space customized according to the user's preferences. This allows for more personalized virtual space suggestions by analyzing the user's behavioral history.

[0063] The virtual space generation system can also include a learning support unit that analyzes the user's learning history and provides optimal learning content. For example, the learning support unit might suggest new learning content based on what the user has learned in the past. It might also adjust the difficulty level of the content to match the user's learning pace. Furthermore, it might provide customized learning content tailored to the user's interests and preferences. This allows for more effective learning support by analyzing the user's learning history.

[0064] The virtual space generation system can also include a purchase suggestion unit that analyzes the user's purchase history and proposes the most suitable products. For example, the purchase suggestion unit could suggest new products based on the user's past purchases. Alternatively, it could analyze the user's purchasing patterns and suggest products at the optimal time. Finally, it could suggest products customized according to the user's preferences. This allows for more personalized product suggestions by analyzing the user's purchase history.

[0065] The virtual space generation system can also include a communication suggestion unit that analyzes the user's social network and proposes optimal communication. For example, the communication suggestion unit might suggest new communication partners based on the user's past interactions. Alternatively, it might analyze the user's social network and propose communication at the optimal time. Finally, it might propose customized communication based on the user's interests and preferences. This allows for more effective communication suggestions by analyzing the user's social network.

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

[0067] Step 1: The lending department lends the user a glasses-type device equipped with communication capabilities. For example, the glasses-type device is lent out for 100 yen per month. The glasses-type device has video recording and communication capabilities, and when the user puts on the glasses and turns on communication, video recording starts automatically. Step 2: The shooting unit uses the glasses-type device provided by the lending unit to record video and upload it to the virtual space. For example, if a user is walking around town, their movements are recorded as video and uploaded to the virtual space in real time. Step 3: The interpolation unit analyzes the video uploaded by the shooting unit and uses a generation AI to fill in the gaps with images. For example, if some images are missing from a video shot by a user, the generation AI will fill in those parts and create a space that closely resembles reality. The generation AI uses technologies such as GAN (Generative Opposite Network) and VAE (Variational Autoencoder) to generate images for the gaps. Step 4: The update unit updates the image with a new one based on the coordinate data. For example, if a new image is uploaded based on the coordinate data, it replaces the old image. The coordinate data is obtained in various formats, such as GPS data, 3D coordinates, or pixel coordinates. Step 5: The rewards department will award rewards based on the number of uploads that are adopted. For example, if a user's video is used by many people, that user will receive a reward. Rewards may be paid in the form of cash, points, gift cards, etc.

[0068] (Example of form 2) The virtual space generation system according to an embodiment of the present invention is a system for providing a virtual space nearly identical to reality to people who cannot travel far for economic or physical reasons. This virtual space generation system rents a glasses-type device equipped with communication functions to the user for 100 yen per month. When the user puts on the glasses and turns on communication, video recording begins and images are automatically updated. These images are uploaded to the virtual space, creating a space that closely resembles reality. While it uses a method similar to a map service, it is human-operated, allowing for walking sections, and gaps in the images are filled in by a generation AI. When new images are uploaded based on coordinate data, they are replaced, and the user receives a reward based on the number of uploads adopted. Items requiring permission, such as shops, are not uploaded, and once a certain amount has accumulated, the operator negotiates for permission. This system enables a service that provides a more detailed metaverse space as a platform than virtual globe services. Thus, the virtual space generation system can provide a virtual space nearly identical to reality to people who cannot travel far for economic or physical reasons. For example, the user rents a glasses-type device equipped with communication functions for 100 yen per month. This glasses-type device features video recording and communication capabilities. When a user puts on the glasses and turns on communication, video recording automatically begins. For example, if a user walks around town, their movements are recorded as a video and uploaded to the virtual space in real time. Next, the uploaded video is analyzed by a generative AI, and any missing images are filled in. For instance, if some images are missing from a video shot by a user, the generative AI fills in those parts, creating a space that closely resembles reality. Also, if new images are uploaded based on the coordinate data, the old images are replaced. Furthermore, users are rewarded based on the number of uploads that are adopted. For example, if a video shot by a user is used by many people, that user will receive a reward. This encourages users to actively shoot videos and contribute to enriching the virtual space. In addition, users cannot upload videos to places that require permission, such as shops, without permission. Once a certain amount of data has been collected, the operators will negotiate.This allows for the provision of detailed virtual spaces while avoiding privacy and copyright issues. This system enables people who cannot travel far for economic or physical reasons to enjoy virtual spaces that are nearly identical to reality. For example, elderly people and people with disabilities who find travel difficult can visit tourist destinations around the world from the comfort of their homes. Furthermore, by providing a more detailed metaverse space than virtual globe services, it is expected to have applications in various fields, including tourism and education.

[0069] The virtual space generation system according to this embodiment comprises a lending unit, a shooting unit, a completion unit, an update unit, and a reward unit. The lending unit lends a glasses-type device equipped with communication functions to the user. The lending unit lends the glasses-type device for, for example, 100 yen per month. The glasses-type device has a video recording function and a communication function, and when the user puts on the glasses and turns on communication, video recording starts automatically. The shooting unit has the glasses-type device lent by the lending unit record video and upload it to the virtual space. For example, if the user is walking in a city, the shooting unit records the scene as a video and uploads it to the virtual space in real time. The completion unit analyzes the video uploaded by the shooting unit and completes the gaps with a generation AI. For example, if some images are missing in the video recorded by the user, the generation AI completes those parts and creates a space that is close to reality. The generation AI generates the gaps using technologies such as GAN (Generative Opposite Network) or VAE (Variational Autoencoder). The update unit updates new images based on coordinate data. For example, if a new image is uploaded based on coordinate data, the update unit replaces the old image. Coordinate data is acquired in the form of GPS data, 3D coordinates, pixel coordinates, etc. The reward unit provides rewards according to the number of uploads that are adopted. For example, if a video shot by a user is used by many people, the reward unit pays the user a reward. The reward is paid in the form of cash, points, gift cards, etc. As a result, the virtual space generation system according to the embodiment can efficiently summarize, cluster, analyze trends, and extract elements from the user's ideas.

[0070] The lending department provides users with glasses-type devices equipped with communication functions. For example, the lending department may rent out the glasses-type devices for 100 yen per month. The glasses-type devices have video recording and communication functions, and when the user puts on the glasses and turns on communication, video recording starts automatically. Specifically, the glasses-type devices have a built-in high-resolution camera that captures the user's field of view in real time. The communication function allows the recorded videos to be instantly uploaded to a cloud server using Wi-Fi or mobile data communication. The glasses-type devices are lightweight and designed to withstand long periods of use, and have a long battery life, making them suitable for daily use by users. Furthermore, the glasses-type devices are equipped with a voice command function, allowing users to operate the device hands-free. For example, they recognize voice commands such as "start recording" and "stop recording" and operate according to the user's instructions. This allows users to record videos naturally and contribute to the virtual space generation system. The lending department also provides maintenance and support for the glasses-type devices, and has a system in place to respond quickly if the user encounters a malfunction or problem with the device. This allows users to use the device with peace of mind and contributes to improving the quality of the virtual space generation system.

[0071] The recording unit uses glasses-type devices provided by the lending unit to record videos and upload them to a virtual space. For example, if a user is walking in a city, the recording unit records their movements as a video and uploads it to the virtual space in real time. Specifically, the camera on the glasses-type device captures the user's field of view, compresses the video data, and sends it to a cloud server. The cloud server analyzes the received video data and places it in a way that corresponds to specific coordinates in the virtual space. This recreates the user's walking route and the scenery they saw in the virtual space. The recording unit uses appropriate compression algorithms to maintain the quality of the video data and minimize data loss. In addition, the recording unit has a function that automatically blurs the faces of people in the video using facial recognition technology to protect user privacy. This allows users to record videos with peace of mind without infringing on the privacy of others. Furthermore, the recording unit also provides a function to tag videos recorded by users, making it easier to search and filter them. For example, it automatically assigns tags related to specific locations or events, making it easy for other users to find the videos. This allows the photography department to enrich the content of the virtual space and promote interaction among users.

[0072] The interpolation unit analyzes the video uploaded by the shooting unit and uses a generation AI to fill in the gaps with images. For example, if some images are missing from a video shot by a user, the interpolation unit uses the generation AI to fill in those parts and create a space that closely resembles reality. The generation AI generates the gap images using technologies such as GAN (Generative Opposite Network) or VAE (Variational Autoencoder). Specifically, GAN is a technology that generates realistic images by having two neural networks, a generator and a discriminator, compete. The generator generates an image of the missing part, and the discriminator determines whether that image is real or fake. By repeating this process, the generator improves its ability to generate more realistic images. On the other hand, VAE is a technology that fills in the missing parts by compressing the input data into a low-dimensional latent space and reconstructing it from that latent space. As a result, the interpolation unit can improve the quality of the video shot by the user and enhance the reality of the virtual space. Furthermore, the interpolation unit also has a filtering function to verify the images generated by the generation AI and guarantee their quality. For example, if a generated image is unnatural or contains incorrect information, it can be detected and corrected. Furthermore, the complementary unit can collect user feedback and continuously improve the performance of the generation AI. This allows the complementary unit to consistently provide a high-quality virtual space and enhance user satisfaction.

[0073] The update unit updates images based on coordinate data. For example, if a new image is uploaded based on coordinate data, the update unit replaces the old image. Coordinate data can be obtained in various formats, such as GPS data, 3D coordinates, or pixel coordinates. Specifically, it analyzes the location information of videos taken by users and identifies the coordinates in the virtual space corresponding to those locations. When a new image is uploaded, the update unit automatically replaces the old image corresponding to those coordinates. This ensures that the virtual space is always updated with the latest information, reflecting the real world that changes in real time. Furthermore, the update unit can integrate data from multiple users to build a more detailed and accurate virtual space. For example, if different users film the same location, it integrates their video data to generate a higher resolution and more detailed image. The update unit also has a function to archive past data and restore it as needed. This allows for the recreation of the virtual space at a specific point in time, and can be used for the preservation and analysis of historical data. In addition, the update unit has an algorithm that evaluates the quality of data provided by users and prioritizes the adoption of high-quality data. This maintains the quality of the virtual space and ensures that users always receive the best possible experience.

[0074] The rewards department provides rewards based on the number of uploads that are adopted. For example, if a user's video is used by many people, the rewards department will pay that user a reward. Rewards can be paid in the form of cash, points, gift cards, etc. Specifically, the rewards department calculates rewards based on metrics such as the number of views, ratings, and comments on videos provided by users. This allows users to receive rewards commensurate with their contributions, increasing their motivation. Furthermore, the rewards department can also hold events that regularly announce rankings and offer special rewards to top-ranking users. This promotes competition among users and provides more high-quality content to the virtual space. The rewards department also provides a dashboard to visualize user contributions, allowing users to check their achievements. This allows users to realize how much their activities contribute to the virtual space and encourages further contributions. In addition, the rewards department can collect feedback from users and use it to improve the rewards system. For example, it can reflect opinions on how rewards are calculated and paid out to create a fairer and more attractive rewards system. This allows the rewards department to increase user satisfaction and contribute to the revitalization of the virtual space generation system.

[0075] The interpolation unit uses generative AI to fill in gaps in the image. For example, if some images are missing from a video shot by a user, the interpolation unit uses generative AI to fill in those missing parts, creating a space that closely resembles reality. The generative AI generates images of the gaps using technologies such as GAN (Generative Opposite Network) or VAE (Variational Autoencoder). This allows for the automatic interpolation of images of gaps using generative AI.

[0076] The update unit updates images based on coordinate data. For example, if a new image is uploaded based on coordinate data, the update unit replaces the old image. Coordinate data is acquired in various formats, such as GPS data, 3D coordinates, or pixel coordinates. This allows the system to provide the latest information by updating images based on coordinate data.

[0077] The rewards department provides rewards based on the number of uploads that are accepted. For example, if a user's video is used by many people, the rewards department will pay that user a reward. The rewards may be paid in the form of cash, points, gift cards, etc. This system encourages active user participation by rewarding users based on the number of accepted uploads.

[0078] The filming department uploads videos to the virtual space in real time. For example, if a user is walking around town, the filming department will capture their movements as a video and upload it to the virtual space in real time. This real-time uploading of videos to the virtual space enables the provision of information in an immediate manner.

[0079] The lending department will lend out glasses-type devices for 100 yen per month. For example, the lending department will lend out glasses-type devices for 100 yen per month. This will reduce the financial burden by lending out glasses-type devices for 100 yen per month.

[0080] The lending unit estimates the user's emotions and adjusts the timing of lending the glasses-type device based on the estimated emotions. For example, if the user is excited, the lending unit immediately lends the glasses-type device to facilitate use. For example, if the user is relaxed, the lending unit flexibly adjusts the lending timing to match the user's pace. For example, if the user is stressed, the lending unit delays the lending timing and waits until the user calms down. This improves user satisfaction by adjusting the lending timing according to 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.

[0081] The lending department analyzes the user's past usage history and selects the optimal lending method. For example, the lending department may lend equipment according to the time slots the user frequently used in the past. For example, the lending department may provide a customized lending method by considering the functions the user preferred to use in the past. For example, the lending department may propose the optimal lending period based on the user's past usage history. In this way, by analyzing past usage history, the lending department can provide the user with the most suitable lending method.

[0082] The lending department filters the glasses-type devices provided based on the user's current lifestyle and areas of interest. For example, if a user is traveling, the lending department prioritizes lending devices with features useful for travel. If a user is interested in health, the lending department lends devices with enhanced health management features. If a user is studying, the lending department lends devices with learning support features. By lending devices tailored to the user's lifestyle and areas of interest, the lending department improves user satisfaction.

[0083] The lending unit estimates the user's emotions and determines the priority of devices to lend based on the estimated emotions. For example, if the user is excited, the lending unit will prioritize lending the latest devices. If the user is relaxed, the lending unit will prioritize lending devices with stable performance. If the user is stressed, the lending unit will prioritize lending devices that are easy to use. This improves user satisfaction by prioritizing devices according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to these examples.

[0084] The lending department prioritizes lending relevant devices to users based on their geographical location when providing glasses-type devices. For example, if a user is in an urban area, the lending department will lend a device suitable for urban use. If a user is in a natural environment, the lending department will lend an outdoor-oriented device. If a user is overseas, the lending department will lend a multilingual device. By lending devices based on the user's geographical location, the lending department aims to improve user satisfaction.

[0085] The lending department analyzes the user's social media activity when lending out glasses-type devices and lends out relevant devices. For example, if a user frequently posts photos, the lending department will lend them a device with a high-resolution camera. If a user streams videos, the lending department will lend them a device specifically designed for video recording. If a user shares health information, the lending department will lend them a device with enhanced health management functions. By lending out devices based on the user's social media activity, the lending department aims to improve user satisfaction.

[0086] The camera unit estimates the user's emotions and adjusts the camera's presentation based on those emotions. For example, if the user is relaxed, the camera unit uses calm visual expressions. If the user is excited, the camera unit uses dynamic visual expressions. If the user is stressed, the camera unit uses simple and visually calming visual expressions. By adjusting the camera's presentation according to the user's emotions, more engaging videos can be provided. 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.

[0087] The camera crew adjusts the level of detail during shooting based on the importance of the video. For example, when shooting important events, the crew will shoot in high resolution for detailed footage. When shooting everyday scenes, for example, the crew will shoot in standard resolution. When shooting specific locations or objects, for example, the crew will use the zoom function to capture detailed footage. This allows for high-quality shooting of important scenes by adjusting the level of detail based on the importance of the video.

[0088] The camera applies different shooting algorithms depending on the video category during shooting. For example, when shooting landscapes, it uses a wide-angle lens to capture a wide area. When shooting people, for example, it uses portrait mode to blur the background. When shooting animals, for example, it uses a fast shutter that responds to movement. By applying shooting algorithms appropriate to the video category, it can provide optimal footage.

[0089] The shooting unit estimates the user's emotions and adjusts the shooting length based on the estimated emotions. For example, if the user is relaxed, the shooting unit will shoot for a longer duration. If the user is in a hurry, the shooting unit will shoot for a shorter duration. If the user is excited, the shooting unit will shoot for a moderate duration. By adjusting the shooting length according to the user's emotions, user satisfaction is improved. 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.

[0090] The filming department prioritizes filming based on the timing of the video. For example, they prioritize filming important events. For example, they postpone filming everyday scenes. For example, they prioritize filming seasonal scenes. By prioritizing filming based on the timing of the video, they can ensure that important scenes are filmed first.

[0091] The camera crew adjusts the shooting order based on the relevance of the video footage. For example, they might prioritize shooting important scenes and edit them later. Or, they might shoot highly relevant scenes consecutively. Or, they might postpone shooting less relevant scenes. By adjusting the shooting order based on the relevance of the video footage, efficient shooting becomes possible.

[0092] The completion unit estimates the user's emotions and determines the priority of images to complete based on the estimated emotions. For example, if the user is relaxed, the completion unit prioritizes images of high importance. If the user is excited, the completion unit prioritizes images that are visually appealing. If the user is stressed, the completion unit prioritizes images that are simple and visually calming. This improves user satisfaction by determining the priority of images to complete according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The interpolation unit improves the accuracy of interpolation by considering the relationships between video clips during the interpolation process. For example, the interpolation unit considers the continuity of scenes within the video when performing interpolation. For example, the interpolation unit considers the relationships between different scenes within the video when performing interpolation. For example, the interpolation unit prioritizes the interpolation of important scenes within the video when performing interpolation. This improves the accuracy of interpolation by considering the relationships between video clips.

[0094] The interpolation unit considers the attributes of the videographer during interpolation. For example, the interpolation unit adjusts the interpolation style based on the videographer's age and gender. For example, the interpolation unit adjusts the content of the interpolation based on the videographer's interests and concerns. For example, the interpolation unit improves the accuracy of the interpolation based on the videographer's past shooting history. This makes it possible to perform more appropriate interpolation by considering the videographer's attributes.

[0095] The complementary unit estimates the user's emotions and adjusts how the complementary images are displayed based on the estimated emotions. For example, if the user is relaxed, the complementary unit uses a calm display method. If the user is excited, the complementary unit uses a dynamic display method. If the user is stressed, the complementary unit uses a simple and visually calming display method. This improves user satisfaction by adjusting the image display method according to 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.

[0096] The interpolation unit considers the geographical distribution of the video during interpolation. For example, it prioritizes interpolating images of geographically important locations. For example, it interpolates images of geographically relevant locations consecutively. For example, it postpones interpolating images of geographically irrelevant locations. This allows for more accurate interpolation by considering geographical distribution.

[0097] The interpolation unit improves the accuracy of interpolation by referring to related literature for the video during the interpolation process. For example, the interpolation unit performs interpolation based on information obtained from related literature. For example, the interpolation unit performs interpolation by referring to images from related literature. For example, the interpolation unit improves the accuracy of interpolation by using data from related literature. In this way, the accuracy of interpolation is improved by referring to related literature.

[0098] The update unit estimates the user's emotions and adjusts the update method based on the estimated emotions. For example, if the user is relaxed, the update unit uses a gentle update method. For example, if the user is excited, the update unit uses a dynamic update method. For example, if the user is stressed, the update unit uses a simple and visually calming update method. This improves user satisfaction by adjusting the update method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The update unit optimizes the current update by referring to past update data during the update process. For example, the update unit selects the optimal update method based on past update data. For example, the update unit optimizes the current update based on information obtained from past update data. For example, the update unit analyzes past update data to improve the accuracy of the current update. This allows the current update to be optimized by referring to past data.

[0100] The update unit applies different update methods depending on the video category during the update process. For example, when updating landscape videos, the update unit uses a wide-angle lens to capture a wide area. For example, when updating videos of people, the update unit uses portrait mode to blur the background. For example, when updating animal videos, the update unit uses a high-speed shutter that responds to movement. By applying different methods to each category, optimal updates can be achieved.

[0101] The update unit estimates the user's emotions and determines update priorities based on those emotions. For example, if the user is relaxed, the update unit prioritizes high-priority updates. If the user is excited, the update unit prioritizes visually appealing updates. If the user is stressed, the update unit prioritizes simple and visually calming updates. This improves user satisfaction by prioritizing updates according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The update unit analyzes the changes in the update based on the video's shooting date. For example, the update unit analyzes the changes in the update based on the shooting date of important events. For example, the update unit analyzes the changes in the update based on the shooting date of seasonal scenes. For example, the update unit analyzes the changes in the update based on the shooting date of everyday scenes. This allows for optimal updates by analyzing the changes in the update based on the shooting date.

[0103] The update unit analyzes the update by referring to relevant market data for the video during the update process. For example, the update unit selects the optimal update method based on the relevant market data. For example, the update unit improves the accuracy of the update based on the information obtained from the relevant market data. For example, the update unit analyzes the relevant market data and evaluates the effectiveness of the update. Thus, by referring to relevant market data, the accuracy of the update is improved.

[0104] The rewards department estimates the user's emotions and adjusts the reward distribution method based on the estimated emotions. For example, if the user is relaxed, the rewards department flexibly adjusts the reward distribution. For example, if the user is excited, the rewards department distributes the reward quickly. For example, if the user is stressed, the rewards department simplifies the reward distribution. This improves user satisfaction by adjusting the reward distribution method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The rewards department analyzes users' past contributions to select the optimal reward distribution method when allocating rewards. For example, the rewards department determines reward distribution based on users' past contributions. For example, the rewards department analyzes users' past contributions and proposes the optimal reward distribution method. For example, the rewards department adjusts reward distribution considering users' past contributions. This allows for optimal reward distribution by analyzing past contributions.

[0106] The rewards department customizes reward distribution based on the user's current living situation. For example, the rewards department adjusts reward distribution based on the user's current living situation. For example, the rewards department customizes reward distribution methods considering the user's living situation. For example, the rewards department optimizes reward distribution according to the user's living situation. This improves user satisfaction by customizing reward distribution based on the user's current living situation.

[0107] The rewards department estimates the user's emotions and determines the priority of rewards based on the estimated emotions. For example, if the user is relaxed, the rewards department flexibly adjusts the reward priority. For example, if the user is excited, the rewards department quickly determines the reward priority. For example, if the user is stressed, the rewards department simplifies the reward priority. This improves user satisfaction by determining the reward priority according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The rewards department selects the optimal reward distribution method when allocating rewards, taking into account the user's geographical location information. For example, the rewards department adjusts reward distribution based on the user's geographical location information. For example, the rewards department customizes reward distribution methods by considering the user's geographical location information. For example, the rewards department optimizes reward distribution according to the user's geographical location information. This makes it possible to distribute rewards optimally by considering geographical location information.

[0109] The rewards department analyzes users' social media activity when distributing rewards and proposes methods for distributing rewards. For example, the rewards department adjusts reward distribution based on users' social media activity. For example, the rewards department analyzes users' social media activity and proposes the optimal method for distributing rewards. For example, the rewards department optimizes reward distribution according to users' social media activity. This allows the department to propose the optimal method for distributing rewards by analyzing social media activity.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The virtual space generation system may also include a health management unit that monitors the user's health status. For example, the health management unit could measure the user's heart rate and blood pressure and issue a warning if an abnormality is detected. For example, if the user is using the glasses-type device for an extended period, the health management unit could send a notification prompting them to take a break. For example, the health management unit could analyze the user's health data and provide appropriate exercise and dietary advice. This allows the system to support the user's health maintenance by monitoring their health status and providing appropriate advice.

[0112] The virtual space generation system may further include an environment adjustment unit that estimates the user's emotions and adjusts the environment within the virtual space based on those emotions. For example, the environment adjustment unit might display calming music and scenery if the user is relaxed. If the user is excited, it might display lively music and dynamic scenery. If the user is stressed, it might display calming music and quiet scenery. This improves user satisfaction by adjusting the environment within the virtual space according to the user's emotions.

[0113] The virtual space generation system can also include a suggestion unit that analyzes the user's behavioral history and proposes the most suitable virtual space. For example, the suggestion unit could suggest new destinations based on places the user has visited or been interested in in the past. Alternatively, it could analyze the user's behavioral patterns and suggest the most optimal time to use the virtual space. Finally, it could suggest a virtual space customized according to the user's preferences. This allows for more personalized virtual space suggestions by analyzing the user's behavioral history.

[0114] The virtual space generation system may further include an interaction adjustment unit that estimates the user's emotions and adjusts the interactions within the virtual space based on the estimated emotions. For example, the interaction adjustment unit provides gentle interactions when the user is relaxed. For example, it provides lively interactions when the user is excited. For example, it provides simple and calm interactions when the user is stressed. This improves user satisfaction by adjusting interactions according to the user's emotions.

[0115] The virtual space generation system can also include a learning support unit that analyzes the user's learning history and provides optimal learning content. For example, the learning support unit might suggest new learning content based on what the user has learned in the past. It might also adjust the difficulty level of the content to match the user's learning pace. Furthermore, it might provide customized learning content tailored to the user's interests and preferences. This allows for more effective learning support by analyzing the user's learning history.

[0116] The virtual space generation system may further include a guide adjustment unit that estimates the user's emotions and adjusts the guides within the virtual space based on the estimated emotions. For example, the guide adjustment unit provides a gentle guide when the user is relaxed. For example, the guide adjustment unit provides an active guide when the user is excited. For example, the guide adjustment unit provides a simple and calm guide when the user is stressed. This improves user satisfaction by adjusting the guides according to the user's emotions.

[0117] The virtual space generation system can also include a purchase suggestion unit that analyzes the user's purchase history and proposes the most suitable products. For example, the purchase suggestion unit could suggest new products based on the user's past purchases. Alternatively, it could analyze the user's purchasing patterns and suggest products at the optimal time. Finally, it could suggest products customized according to the user's preferences. This allows for more personalized product suggestions by analyzing the user's purchase history.

[0118] The virtual space generation system may further include an effect adjustment unit that estimates the user's emotions and adjusts the effects within the virtual space based on the estimated emotions. For example, the effect adjustment unit might use calming effects when the user is relaxed, dynamic effects when the user is excited, or simple, visually calming effects when the user is stressed. This improves user satisfaction by adjusting effects according to the user's emotions.

[0119] The virtual space generation system can also include a communication suggestion unit that analyzes the user's social network and proposes optimal communication. For example, the communication suggestion unit might suggest new communication partners based on the user's past interactions. Alternatively, it might analyze the user's social network and propose communication at the optimal time. Finally, it might propose customized communication based on the user's interests and preferences. This allows for more effective communication suggestions by analyzing the user's social network.

[0120] The virtual space generation system may further include a navigation adjustment unit that estimates the user's emotions and adjusts the navigation within the virtual space based on the estimated emotions. For example, the navigation adjustment unit provides gentle navigation when the user is relaxed. For example, it provides lively navigation when the user is excited. For example, it provides simple and calm navigation when the user is stressed. This improves user satisfaction by adjusting the navigation according to the user's emotions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The lending department lends the user a glasses-type device equipped with communication capabilities. For example, the glasses-type device is lent out for 100 yen per month. The glasses-type device has video recording and communication capabilities, and when the user puts on the glasses and turns on communication, video recording starts automatically. Step 2: The shooting unit uses the glasses-type device provided by the lending unit to record video and upload it to the virtual space. For example, if a user is walking around town, their movements are recorded as video and uploaded to the virtual space in real time. Step 3: The interpolation unit analyzes the video uploaded by the shooting unit and uses a generation AI to fill in the gaps with images. For example, if some images are missing from a video shot by a user, the generation AI will fill in those parts and create a space that closely resembles reality. The generation AI uses technologies such as GAN (Generative Opposite Network) and VAE (Variational Autoencoder) to generate images for the gaps. Step 4: The update unit updates the image with a new one based on the coordinate data. For example, if a new image is uploaded based on the coordinate data, it replaces the old image. The coordinate data is obtained in various formats, such as GPS data, 3D coordinates, or pixel coordinates. Step 5: The rewards department will award rewards based on the number of uploads that are adopted. For example, if a user's video is used by many people, that user will receive a reward. Rewards may be paid in the form of cash, points, gift cards, etc.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the lending unit, shooting unit, supplementation unit, update unit, and reward unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the lending unit is implemented by the control unit 46A of the smart device 14 and lends the glasses-type device to the user. The shooting unit is implemented by the camera 42 of the smart device 14 and shoots a video of the user walking around town and uploads it to a virtual space in real time. The supplementation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the uploaded video and uses generation AI to supplement images in the gaps. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and updates the image with a new image based on coordinate data. The reward unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a reward according to the number of uploads adopted. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the lending unit, shooting unit, supplementation unit, update unit, and reward unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the lending unit is implemented by the control unit 46A of the smart glasses 214, which lends the glasses-type device to the user. The shooting unit is implemented by the camera 42 of the smart glasses 214, which shoots a video of the user walking around town and uploads it to a virtual space in real time. The supplementation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the uploaded video and uses generation AI to supplement images in the gaps. The update unit is implemented by the specific processing unit 290 of the data processing unit 12, which updates the image with a new one based on coordinate data. The reward unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides a reward according to the number of uploads adopted. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the lending unit, shooting unit, supplementation unit, update unit, and reward unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the lending unit is implemented by the control unit 46A of the headset terminal 314 and lends the glasses-type device to the user. The shooting unit is implemented by the camera 42 of the headset terminal 314 and shoots a video of the user walking around town and uploads it to the virtual space in real time. The supplementation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the uploaded video and uses generation AI to supplement images in the gaps. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and updates the image with a new one based on coordinate data. The reward unit is implemented by the specific processing unit 290 of the data processing unit 12 and gives a reward according to the number of uploads adopted. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the lending unit, shooting unit, supplementation unit, update unit, and reward unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the lending unit is implemented by the control unit 46A of the robot 414 and lends the user a glasses-type device. The shooting unit is implemented by the camera 42 of the robot 414 and shoots a video of the user walking around town and uploads it to a virtual space in real time. The supplementation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the uploaded video and uses generation AI to supplement images in the gaps. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and updates the image with a new one based on coordinate data. The reward unit is implemented by the specific processing unit 290 of the data processing unit 12 and gives a reward according to the number of uploads adopted. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A lending unit that provides users with glasses-type devices equipped with communication functions, The glasses-type device provided by the aforementioned lending unit records video and uploads it to a virtual space; The aforementioned shooting unit analyzes the uploaded video and uses AI to generate images to fill in the gaps, The update unit updates the image with a new one based on the coordinate data, It includes a rewards department that pays bonuses based on the number of successful upgrades. A system characterized by the following features. (Note 2) The aforementioned supplementary unit is, Use generative AI to fill in gaps in the image. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned update section is, Update the image with a new one based on the coordinate data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned rewards department, A bonus will be awarded based on the number of successful upgrades. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned imaging unit is Upload videos to a virtual space in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned lending unit is, We will rent out glasses-type devices for 100 yen per month. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned lending unit is, The system estimates the user's emotions and adjusts the timing of lending the glasses-type device based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned lending unit is, Analyze the user's past usage history to select the optimal lending method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned lending unit is, When lending out glasses-type devices, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned lending unit is, It estimates the user's emotions and determines the priority of devices to lend based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned lending unit is, When lending out glasses-type devices, the system prioritizes lending out devices that are more relevant to the user's geographical location, taking this information into account. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned lending unit is, When lending out glasses-type devices, the system analyzes the user's social media activity and lends out devices relevant to that activity. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned imaging unit is The system estimates the user's emotions and adjusts the shooting style based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned imaging unit is During shooting, adjust the level of detail based on the importance of the video. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned imaging unit is During shooting, different shooting algorithms are applied depending on the video category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned imaging unit is It estimates the user's emotions and adjusts the length of the recording based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned imaging unit is When shooting, prioritize the shooting based on when the video was recorded. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned imaging unit is During filming, adjust the shooting order based on the relevance of the videos. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supplementary unit is, It estimates the user's emotions and determines the priority of images to complement them based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supplementary unit is, During interpolation, the accuracy of the interpolation is improved by considering the relationships between the videos. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supplementary unit is, During interpolation, the video's creator's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supplementary unit is, It estimates the user's emotions and adjusts how images are displayed to complement those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supplementary unit is, During interpolation, the geographical distribution of the videos is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supplementary unit is, During the completion process, we refer to related literature for the video to improve the accuracy of the completion. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned update section is, It estimates user sentiment and adjusts the update method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned update section is, During updates, the system optimizes the current update by referencing past update data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned update section is, During updates, different update methods are applied to each video category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned update section is, It estimates user sentiment and determines update priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned update section is, During updates, we analyze the changes based on when the video was filmed. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned update section is, During updates, we analyze the updates by referring to relevant market data for the videos. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned rewards department, The system estimates the user's emotions and adjusts the reward distribution method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned rewards department, When distributing rewards, the system analyzes users' past contributions to select the optimal distribution method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned rewards department, When distributing rewards, customize the distribution based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned rewards department, The system estimates user sentiment and prioritizes rewards based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned rewards department, When distributing rewards, the optimal distribution method will be selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned rewards department, When distributing rewards, we analyze users' social media activity and propose reward distribution methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 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 lending unit that provides users with glasses-type devices equipped with communication functions, The glasses-type device provided by the aforementioned lending unit records video and uploads it to a virtual space; The aforementioned shooting unit analyzes the uploaded video and uses AI to generate and fill in the gaps with images. The update unit updates the image with a new one based on the coordinate data, It includes a rewards department that pays bonuses based on the number of successful upgrades. A system characterized by the following features.

2. The aforementioned supplementary unit is, Use generative AI to fill in gaps in the image. The system according to feature 1.

3. The aforementioned update section is, Update the image with a new one based on the coordinate data. The system according to feature 1.

4. The aforementioned rewards department, A bonus will be awarded based on the number of successful upgrades. The system according to feature 1.

5. The aforementioned imaging unit is Upload videos to a virtual space in real time. The system according to feature 1.

6. The aforementioned lending unit is, We will rent out glasses-type devices for 100 yen per month. The system according to feature 1.

7. The aforementioned lending unit is, The system estimates the user's emotions and adjusts the timing of lending the glasses-type device based on those estimated emotions. The system according to feature 1.

8. The aforementioned lending unit is, Analyze the user's past usage history to select the optimal lending method. The system according to feature 1.

9. The aforementioned lending unit is, When lending out glasses-type devices, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

10. The aforementioned lending unit is, It estimates the user's emotions and determines the priority of devices to lend based on the estimated user emotions. The system according to feature 1.

Images