system

Abstract

The system according to the embodiment aims to recreate memories with a more realistic feel by converting 2D photos into 3D photos and generating a metaverse space. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a metaverse space generation unit, and an experience unit. The reception unit receives input of a 2D photo from a user. The generation unit analyzes the 2D photo received by the reception unit and generates a 3D photo. The metaverse space generation unit generates a metaverse space based on the 3D photo generated by the generation unit. The experience unit allows the user to experience the metaverse space generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, 2D photographs lacked a sense of realism, making it difficult to recreate memories in a more realistic way.

[0005] The system according to the embodiment aims to recreate memories with a more realistic feel by converting 2D photos into 3D photos and generating a metaverse space. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a metaverse space generation unit, and an experience unit. The reception unit receives input of a 2D photo from a user. The generation unit analyzes the 2D photo received by the reception unit and generates a 3D photo. The metaverse space generation unit generates a metaverse space based on the 3D photo generated by the generation unit. The experience unit allows the user to experience the metaverse space generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment converts 2D photos into 3D photos and generates a metaverse space, allowing memories to be recreated with a more realistic feel. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention utilizes a generative AI to convert 2D photos into immersive 3D photos and further recreate past spaces in 3D using the Metaverse. In this system, a user inputs a 2D photo, and the generative AI analyzes the photo to generate a 3D photo. The generative AI then generates a Metaverse space for the location of the photo, allowing the user to experience that space. This mechanism allows users to enjoy memorable photos as immersive 3D photos and share them with family and friends. Furthermore, the Metaverse space allows multiple people to relive special moments and memories from the past. For example, a user inputs a 2D photo. For example, the user selects a memorable photo, such as a family photo or a travel photo. The photo is then input into the generative AI. The generative AI then analyzes the input photo and generates a 3D photo. The generative AI analyzes each element of the photo to recreate a three-dimensional structure. For example, it analyzes the depth of the people and background in the photo and generates a 3D model. This transforms a flat photo into a immersive 3D photo. The generative AI then generates a Metaverse space for the location of the photo. The generative AI recreates past spaces based on photo information. For example, it recreates specific locations, such as travel scenery or family gatherings, in 3D. Users can freely move around and experience this metaverse space. This system allows users to enjoy memorable photos as immersive 3D photos. For example, family photos can be converted into 3D so the whole family can enjoy them together. Special moments and memories from the past can also be recreated and enjoyed by multiple people through the metaverse space. For example, family members who live far away can be reunited in the metaverse space and share memories. The platform generates a metaverse space from users' memorable photos and videos, providing a special experience for interacting with family and friends. For example, it allows users to relive memories with deceased relatives or forever preserve their children's growth. This allows users to preserve memories with their loved ones in a more immersive way. The system converts users' 2D photos into 3D photos and generates a metaverse space for them to experience.

[0029] The system according to the embodiment includes a reception unit, a generation unit, and an experience unit. The reception unit receives input of a 2D photo from a user. The 2D photo from the user may include, but is not limited to, a file format such as JPEG, PNG, or BMP, a resolution, and a color mode. The reception unit, for example, provides an interface for the user to upload the 2D photo. The reception unit may also display guidelines for the user when selecting and uploading a photo. The generation unit uses a generation AI to analyze the 2D photo received by the reception unit and generate a 3D photo. The generation AI, for example, analyzes each element of the photo and recreates a three-dimensional structure. For example, the generation AI analyzes the depth of people and backgrounds in the photo and generates a 3D model. The generation unit also uses the generation AI to recreate a past space based on the information in the photo. For example, the generation unit recreates a specific location, such as a landscape from a travel destination or a place where family members gather, in 3D. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can generate a 3D photo using a generative AI model that receives photo information as input and outputs a 3D model. The experience unit allows a user to experience the metaverse space generated by the generation unit. The experience unit, for example, provides an interface that allows a user to freely move through and experience the metaverse space. For example, the experience unit allows a user to experience the metaverse space using a VR headset. The experience unit can also allow a user to experience the metaverse space using a PC or smartphone. As a result, the system according to the embodiment can convert a user's 2D photo into a 3D photo and generate and experience the metaverse space. Some or all of the above-described processing in the experience unit may be performed, for example, using a generative AI, or may be performed without using a generative AI. For example, the experience unit can provide the metaverse space using a generative AI model that receives the generated metaverse space as input and outputs an interface for the user to experience.

[0030] The reception unit can analyze the user's past photo input history and select the optimal input method. For example, the reception unit prioritizes and suggests input methods (such as voice and text) that the user has frequently used in the past. For example, the reception unit stores the user's past photo input history in a database and analyzes it using AI. Next, the reception unit selects the optimal input method based on the analysis results. The reception unit can also suggest a related input method based on the type or theme of photos the user has previously input. For example, the reception unit analyzes the user's past photo input history and selects an input method related to a specific theme. The reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. For example, the reception unit chronologically analyzes the user's past input history and selects the optimal input method for a specific time period. This makes it possible to suggest the optimal input method based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can select an input method using a generative AI model that takes the user's past photo input history as input and outputs the optimal input method.

[0031] When inputting 2D photos, the reception unit can filter them based on the user's current areas of interest or events. For example, the reception unit prioritizes inputting photos related to events in which the user is currently participating. For example, the reception unit analyzes the user's calendar events and social media activity to select related photos. Then, the reception unit prioritizes inputting the selected photos. The reception unit can also automatically select and input related photos based on the user's areas of interest. For example, the reception unit analyzes the user's hobbies and past search history to select related photos. Furthermore, if the user is interested in a particular theme, the reception unit can prioritize inputting photos related to that theme. For example, the reception unit analyzes the user's social media activity to select photos related to the particular theme. This allows photos related to the user's areas of interest or events to be prioritized. Some or all of the above-described processing in the reception unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the reception unit can perform filtering using a generative AI model that inputs the user's areas of interest and event information and outputs related photos.

[0032] When inputting 2D photos, the reception unit can prioritize inputting highly relevant photos based on the user's geographical location information. For example, the reception unit prioritizes inputting photos related to the user's current location. For example, the reception unit acquires the user's GPS data and analyzes the user's current geographical location information. Then, the reception unit selects highly relevant photos based on the analysis results. The reception unit can also prioritize inputting photos related to places the user has visited in the past. For example, the reception unit stores the user's past location information in a database and analyzes it using AI. Then, the reception unit selects highly relevant photos based on the analysis results. The reception unit can also prioritize inputting photos related to travel destinations the user is planning. For example, the reception unit analyzes the user's travel plans and selects relevant photos. This allows highly relevant photos to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can select photos using a generation AI model that inputs the user's geographical location information and outputs highly relevant photos.

[0033] When inputting a 2D photo, the reception unit can analyze the user's social media activity and input related photos. For example, the reception unit prioritizes inputting photos recently posted by the user on social media. For example, the reception unit analyzes the user's social media account and acquires recent posts. Then, the reception unit selects related photos based on the acquired posts. The reception unit can also prioritize inputting photos that have received many likes and comments on social media. For example, the reception unit analyzes the user's social media engagement data and selects popular photos. The reception unit can also prioritize inputting photos related to accounts the user follows on social media. For example, the reception unit analyzes the user's follow list and selects related photos. This allows related photos to be prioritized based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the reception unit can select photos using a generative AI model that inputs the user's social media activity data and outputs related photos.

[0034] The generation unit can analyze each element of a 2D photo and adjust the level of detail based on the priority of the photo when reproducing a three-dimensional structure. For example, the generation unit may reproduce a person in the center of the photo with a high level of detail and the background with a low level of detail. For example, the generation unit may analyze each element of the photo using object recognition technology to identify important elements. The generation unit may then adjust the level of detail based on the identified important elements. The generation unit may also reproduce particularly important objects in the photo with a high level of detail and other parts with a low level of detail. For example, the generation unit may use edge detection technology to identify important parts of the photo and adjust the level of detail. The generation unit may also reproduce parts of the photo specified by the user with a high level of detail and other parts with a low level of detail. For example, the generation unit may use segmentation technology to divide each part of the photo and adjust the level of detail. This allows a 3D photo to be generated with an optimal level of detail depending on the importance of the photo. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the level of detail using a generative AI model that takes each element of the photo as input and outputs a 3D photo with adjusted level of detail.

[0035] The generation unit can apply different 3D generation algorithms depending on the category of the 2D photo. For example, the generation unit can apply an algorithm that emphasizes the depth of nature to landscape photos. For example, the generation unit can use image classification technology to classify the photo category. Then, the generation unit selects an appropriate 3D generation algorithm based on the classified category. The generation unit can also apply an algorithm that emphasizes the three-dimensionality of people to portrait photos. For example, the generation unit can use a deep learning algorithm to generate 3D images according to the photo category. The generation unit can also apply an algorithm that reproduces the detailed structure of a building to architectural photos. For example, the generation unit can reproduce the building structure using stereo matching technology. This allows the optimal 3D generation algorithm to be applied depending on the photo category. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can apply an algorithm using a generation AI model that inputs the photo category and outputs an appropriate 3D generation algorithm.

[0036] The generation unit can determine the priority for generating 3D photos based on the time the 2D photos were taken. For example, the generation unit prioritizes 3D conversion of recently taken photos. For example, the generation unit analyzes the metadata of the photos to identify the time the photos were taken. Then, the generation unit determines the priority for generating 3D photos based on the identified time the photos were taken. The generation unit can also prioritize 3D conversion of photos taken on specific events or anniversaries. For example, the generation unit analyzes a user's calendar events and selects related photos. Then, the generation unit prioritizes 3D conversion of the selected photos. The generation unit can also prioritize 3D conversion of photos taken within a period specified by the user. For example, the generation unit analyzes a period specified by the user and selects related photos. Then, the generation unit prioritizes 3D conversion of the selected photos. This allows 3D photos to be generated in an optimal order based on the time the photos were taken. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI or without using a generation AI. For example, the generation unit can determine the priority using a generative AI model that takes the time the photo was taken as input and outputs the priority of generating 3D photos.

[0037] The generation unit can adjust the order in which 3D photos are generated based on the relevance of the 2D photos. For example, the generation unit sequentially generates 3D photos taken at the same event. For example, the generation unit analyzes the metadata of the photos to identify highly related photos. Then, the generation unit adjusts the order in which the 3D photos are generated based on the identified relevance. The generation unit can also sequentially generate 3D photos taken at the same location. For example, the generation unit analyzes the location information of the photos to identify highly related photos. Then, the generation unit adjusts the order in which the 3D photos are generated based on the identified relevance. The generation unit can also sequentially generate 3D photos containing the same person. For example, the generation unit uses facial recognition technology to identify photos containing the same person. Then, the generation unit adjusts the order in which the 3D photos are generated based on the identified relevance. This allows 3D photos to be generated in an optimal order based on the relevance. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the order using a generative AI model that takes photo relevance data as input and outputs the order in which 3D photos are generated.

[0038] When experiencing the metaverse space, the experience unit can select an appropriate experience method by referring to the user's past experience history. The experience unit, for example, suggests a similar experience based on experiences the user enjoyed in the past. For example, the experience unit stores the user's past experience history in a database and analyzes it using AI. The experience unit then selects an appropriate experience method based on the analysis results. The experience unit can also suggest a different experience based on experiences the user avoided in the past. For example, the experience unit analyzes the user's past experience history and identifies experiences that the user avoided. The experience unit then suggests a different experience based on the identified experiences. The experience unit can also select an optimal experience method from the user's past experience history. For example, the experience unit analyzes the user's past experience history in chronological order and selects an optimal experience method. This allows the optimal experience method to be provided based on the user's past experience history. Some or all of the above-described processing in the experience unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the experience unit can select an experience method using a generation AI model that inputs the user's past experience history and outputs an appropriate experience method.

[0039] The experience unit can customize the experience content based on the user's current living situation when experiencing the metaverse space. For example, if the user is busy, the experience unit can provide an experience that can be enjoyed in a short amount of time. For example, the experience unit can analyze the user's schedule and identify the user's current living situation. Then, the experience unit customizes the experience content based on the identified living situation. The experience unit can also provide an experience that can be enjoyed for a long time if the user is relaxed. For example, the experience unit can analyze the user's health status and identify the user's current living situation. Then, the experience unit customizes the experience content based on the identified living situation. The experience unit can also provide an experience related to a specific event if the user is participating in that event. For example, the experience unit can analyze the user's calendar events and select a related experience. Then, the experience unit provides the selected experience. This allows the optimal experience content to be provided according to the user's living situation. Some or all of the above-described processing in the experience unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the experience unit can customize the content using a generation AI model that uses the user's living situation data as input and customizes the experience content.

[0040] When experiencing the metaverse space, the experience unit can select an appropriate experience method based on the user's geographic location information. The experience unit, for example, provides an experience related to the user's current location. For example, the experience unit acquires the user's GPS data and analyzes the user's current geographic location information. Then, the experience unit selects an appropriate experience method based on the analysis results. The experience unit can also provide an experience related to places the user has visited in the past. For example, the experience unit stores the user's past location information in a database and analyzes it using AI. Then, the experience unit selects an appropriate experience method based on the analysis results. The experience unit can also provide an experience related to a travel destination the user is planning. For example, the experience unit analyzes the user's travel plans and selects a related experience. Then, the experience unit provides the selected experience. This allows the optimal experience method to be provided based on the user's geographic location information. Some or all of the above-mentioned processing in the experience unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the experience unit can select an experience method using a generation AI model that inputs the user's geographic location information and outputs an appropriate experience method.

[0041] The experience unit can analyze the user's social media activity and suggest experience content when experiencing the metaverse space. The experience unit, for example, provides experiences related to content recently posted by the user on social media. For example, the experience unit analyzes the user's social media account and acquires recent posts. Then, the experience unit selects related experiences based on the acquired posts. The experience unit can also provide experiences related to content that the user has received many likes or comments on social media. For example, the experience unit analyzes the user's social media engagement data and selects popular content. Then, the experience unit provides related experiences based on the selected content. The experience unit can also provide experiences related to accounts the user follows on social media. For example, the experience unit analyzes the user's follow list and selects related experiences. This allows the optimal experience content to be provided based on the user's social media activity. Some or all of the above-described processing in the experience unit may be performed, for example, using a generative AI, or may be performed without using a generative AI. For example, the experience unit can input the user's social media activity data and suggest experience content using a generative AI model that suggests experience content.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The reception unit can analyze the user's past photo input history and select the optimal input method. For example, it can prioritize and suggest input methods (such as voice and text) that the user has frequently used in the past. For example, the reception unit stores the user's past photo input history in a database and analyzes it using AI. Next, the reception unit selects the optimal input method based on the analysis results. The reception unit can also suggest a related input method based on the type or theme of photos the user has previously input. For example, the reception unit can analyze the user's past photo input history and select an input method related to a specific theme. The reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. For example, the reception unit can analyze the user's past input history in chronological order and select the optimal input method for a specific time period. This allows the optimal input method to be suggested based on the user's past input history. Some or all of the above-described processing in the reception unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reception unit can select an input method using a generation AI model that inputs the user's past photo input history and outputs the optimal input method.

[0044] When inputting 2D photos, the reception unit can filter them based on the user's current areas of interest or events. For example, photos related to events in which the user is currently participating can be preferentially input. For example, the reception unit can analyze the user's calendar events and social media activity to select related photos. Then, the reception unit preferentially inputs the selected photos. The reception unit can also automatically select and input related photos based on the user's areas of interest. For example, the reception unit can analyze the user's hobbies and past search history to select related photos. If the user is interested in a particular theme, the reception unit can preferentially input photos related to that theme. For example, the reception unit can analyze the user's social media activity to select photos related to the particular theme. This allows photos related to the user's areas of interest or events to be preferentially input. Some or all of the above-described processing in the reception unit can be performed using, for example, a generative AI, or can be performed without using a generative AI. For example, the reception unit can perform filtering using a generative AI model that inputs the user's areas of interest and event information and outputs related photos.

[0045] The generation unit can analyze each element of a 2D photo and adjust the level of detail based on the priority of the photo when reproducing a three-dimensional structure. For example, the generation unit can reproduce a person in the center of the photo with high detail and the background with low detail. For example, the generation unit can analyze each element of the photo using object recognition technology to identify important elements. The generation unit can then adjust the level of detail based on the identified important elements. The generation unit can also reproduce particularly important objects in the photo with high detail and other parts with low detail. For example, the generation unit can identify important parts of the photo using edge detection technology and adjust the level of detail. The generation unit can also reproduce parts of the photo specified by the user with high detail and other parts with low detail. For example, the generation unit can divide each part of the photo using segmentation technology and adjust the level of detail. This allows a 3D photo to be generated with an optimal level of detail depending on the importance of the photo. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using a generation AI or without a generation AI. For example, the generation unit can adjust the level of detail using a generative AI model that takes each element of the photo as input and outputs a 3D photo with adjusted level of detail.

[0046] The generation unit can apply different 3D generation algorithms depending on the category of the 2D photo. For example, an algorithm that emphasizes the depth of nature can be applied to landscape photos. For example, the generation unit can use image classification technology to classify the photo category. Then, the generation unit selects an appropriate 3D generation algorithm based on the classified category. The generation unit can also apply an algorithm that emphasizes the three-dimensionality of people to portrait photos. For example, the generation unit can use a deep learning algorithm to generate 3D images according to the photo category. The generation unit can also apply an algorithm that reproduces the detailed structure of a building to architectural photos. For example, the generation unit can reproduce the building structure using stereo matching technology. This allows the optimal 3D generation algorithm to be applied depending on the photo category. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can apply an algorithm using a generation AI model that inputs the photo category and outputs an appropriate 3D generation algorithm.

[0047] When experiencing the metaverse space, the experience unit can select an appropriate experience method by referring to the user's past experience history. For example, the experience unit can suggest a similar experience based on experiences the user enjoyed in the past. For example, the experience unit stores the user's past experience history in a database and analyzes it using AI. Then, the experience unit selects an appropriate experience method based on the analysis results. The experience unit can also suggest a different experience based on experiences the user avoided in the past. For example, the experience unit analyzes the user's past experience history and identifies experiences that the user avoided. Then, the experience unit suggests a different experience based on the identified experiences. The experience unit can also select an optimal experience method from the user's past experience history. For example, the experience unit analyzes the user's past experience history in chronological order and selects an optimal experience method. This allows the optimal experience method to be provided based on the user's past experience history. Some or all of the above-mentioned processing in the experience unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the experience unit can select an experience method using a generation AI model that inputs the user's past experience history and outputs an appropriate experience method.

[0048] The experience unit can customize the experience content based on the user's current living situation when experiencing the metaverse space. For example, if the user is busy, the experience unit can provide an experience that can be enjoyed in a short amount of time. For example, the experience unit can analyze the user's schedule and identify the user's current living situation. Then, the experience unit customizes the experience content based on the identified living situation. Alternatively, if the user is relaxed, the experience unit can provide an experience that can be enjoyed for a long period of time. For example, the experience unit can analyze the user's health status and identify the user's current living situation. Then, the experience unit customizes the experience content based on the identified living situation. Alternatively, if the user is participating in a specific event, the experience unit can provide an experience related to the event. For example, the experience unit can analyze the user's calendar events and select a related experience. Then, the experience unit provides the selected experience. This allows the optimal experience content to be provided according to the user's living situation. Some or all of the above-described processing in the experience unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the experience unit can customize the content using a generation AI model that uses the user's living situation data as input and customizes the experience content.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The reception unit receives input of a 2D photo from a user. The 2D photo from the user includes, for example, a file format such as JPEG, PNG, or BMP, a resolution, a color mode, etc. The reception unit provides an interface for the user to upload the 2D photo and can also display guidelines for selecting and uploading a photo. Step 2: The generation unit uses the generation AI to analyze the 2D photo received by the reception unit and generate a 3D photo. The generation AI analyzes each element of the photo and recreates a three-dimensional structure. For example, it analyzes the depth of the people and background in the photo and generates a 3D model. The generation unit can also recreate past spaces based on the information in the photo. For example, it can recreate specific locations in 3D, such as scenery from a travel destination or a place where family members gather. Step 3: The generator generates a metaverse space based on the generated 3D photo. The generator uses a generation AI to build a metaverse space based on the photo information. Step 4: The experience unit allows the user to experience the metaverse space generated by the generation unit. The experience unit provides an interface that allows the user to freely move around and experience the metaverse space. For example, the user can experience the metaverse space using a VR headset. The user can also experience the metaverse space using a PC or smartphone.

[0051] (Example 2) A system according to an embodiment of the present invention utilizes a generative AI to convert 2D photos into immersive 3D photos and further recreate past spaces in 3D using the Metaverse. In this system, a user inputs a 2D photo, and the generative AI analyzes the photo to generate a 3D photo. The generative AI then generates a Metaverse space for the location of the photo, allowing the user to experience that space. This mechanism allows users to enjoy memorable photos as immersive 3D photos and share them with family and friends. Furthermore, the Metaverse space allows multiple people to relive special moments and memories from the past. For example, a user inputs a 2D photo. For example, the user selects a memorable photo, such as a family photo or a travel photo. The photo is then input into the generative AI. The generative AI then analyzes the input photo and generates a 3D photo. The generative AI analyzes each element of the photo to recreate a three-dimensional structure. For example, it analyzes the depth of the people and background in the photo and generates a 3D model. This transforms a flat photo into a immersive 3D photo. The generative AI then generates a Metaverse space for the location of the photo. The generative AI recreates past spaces based on photo information. For example, it recreates specific locations, such as travel scenery or family gatherings, in 3D. Users can freely move around and experience this metaverse space. This system allows users to enjoy memorable photos as immersive 3D photos. For example, family photos can be converted into 3D so the whole family can enjoy them together. Special moments and memories from the past can also be recreated and enjoyed by multiple people through the metaverse space. For example, family members who live far away can be reunited in the metaverse space and share memories. The platform generates a metaverse space from users' memorable photos and videos, providing a special experience for interacting with family and friends. For example, it allows users to relive memories with deceased relatives or forever preserve their children's growth. This allows users to preserve memories with their loved ones in a more immersive way. The system converts users' 2D photos into 3D photos and generates a metaverse space for them to experience.

[0052] The system according to the embodiment includes a reception unit, a generation unit, and an experience unit. The reception unit receives input of a 2D photo from a user. The 2D photo from the user may include, but is not limited to, a file format such as JPEG, PNG, or BMP, a resolution, and a color mode. The reception unit, for example, provides an interface for the user to upload the 2D photo. The reception unit may also display guidelines for the user when selecting and uploading a photo. The generation unit uses a generation AI to analyze the 2D photo received by the reception unit and generate a 3D photo. The generation AI, for example, analyzes each element of the photo and recreates a three-dimensional structure. For example, the generation AI analyzes the depth of people and backgrounds in the photo and generates a 3D model. The generation unit also uses the generation AI to recreate a past space based on the information in the photo. For example, the generation unit recreates a specific location, such as a travel destination or a family gathering, in 3D. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can generate a 3D photo using a generative AI model that receives photo information as input and outputs a 3D model. The experience unit allows a user to experience the metaverse space generated by the generation unit. The experience unit, for example, provides an interface that allows a user to freely move through and experience the metaverse space. For example, the experience unit allows a user to experience the metaverse space using a VR headset. The experience unit can also allow a user to experience the metaverse space using a PC or smartphone. As a result, the system according to the embodiment can convert a user's 2D photo into a 3D photo and generate and experience the metaverse space. Some or all of the above-described processing in the experience unit may be performed, for example, using a generative AI, or may be performed without using a generative AI. For example, the experience unit can provide the metaverse space using a generative AI model that receives the generated metaverse space as input and outputs an interface for the user to experience.

[0053] The reception unit can estimate the user's emotions and adjust the timing of 2D photo input based on the estimated emotions. For example, if the user is excited, the reception unit sends a notification prompting the user to immediately input a 2D photo. For example, the reception unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. Then, the reception unit adjusts the timing of 2D photo input based on the estimated emotions. The reception unit can also provide an interface that encourages input at a slower pace if the user is relaxed. For example, the reception unit records the user's voice and estimates the user's emotions using voice analysis technology. Then, the reception unit adjusts the timing of 2D photo input based on the estimated emotions. The reception unit can also present an option to postpone input if the user is feeling stressed. For example, the reception unit analyzes the user's text input and estimates the user's emotions using text analysis technology. Then, the reception unit adjusts the timing of 2D photo input based on the estimated emotions. This allows the user to be prompted to input a 2D photo at the optimal timing depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit may adjust the input timing using a generation AI model that receives user emotion data as input and outputs the input timing of a 2D photo.

[0054] The reception unit can analyze the user's past photo input history and select the optimal input method. For example, the reception unit prioritizes and suggests input methods (such as voice and text) that the user has frequently used in the past. For example, the reception unit stores the user's past photo input history in a database and analyzes it using AI. Next, the reception unit selects the optimal input method based on the analysis results. The reception unit can also suggest a related input method based on the type or theme of photos the user has previously input. For example, the reception unit analyzes the user's past photo input history and selects an input method related to a specific theme. The reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. For example, the reception unit chronologically analyzes the user's past input history and selects the optimal input method for a specific time period. This makes it possible to suggest the optimal input method based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can select an input method using a generative AI model that takes the user's past photo input history as input and outputs the optimal input method.

[0055] When inputting 2D photos, the reception unit can filter them based on the user's current areas of interest or events. For example, the reception unit prioritizes inputting photos related to events in which the user is currently participating. For example, the reception unit analyzes the user's calendar events and social media activity to select related photos. Then, the reception unit prioritizes inputting the selected photos. The reception unit can also automatically select and input related photos based on the user's areas of interest. For example, the reception unit analyzes the user's hobbies and past search history to select related photos. Furthermore, if the user is interested in a particular theme, the reception unit can prioritize inputting photos related to that theme. For example, the reception unit analyzes the user's social media activity to select photos related to the particular theme. This allows photos related to the user's areas of interest or events to be prioritized. Some or all of the above-described processing in the reception unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the reception unit can perform filtering using a generative AI model that inputs the user's areas of interest and event information and outputs related photos.

[0056] The reception unit can estimate the user's emotions and prioritize the photos to be input based on the estimated emotions. For example, if the user is emotional, the reception unit prioritizes inputting emotional photos. For example, the reception unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Then, the reception unit prioritizes inputting photos based on the estimated emotions. Alternatively, if the user is having fun, the reception unit can prioritize inputting photos capturing happy moments. For example, the reception unit can record the user's voice and estimate the user's emotions using voice analysis technology. Then, the reception unit prioritizes inputting photos based on the estimated emotions. Alternatively, if the user is sad, the reception unit can prioritize inputting photos that are comforting. For example, the reception unit can analyze the user's text input and estimate the user's emotions using text analysis technology. Then, the reception unit prioritizes inputting photos based on the estimated emotions. This allows the most appropriate photos to be input preferentially according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit may determine the priority using a generation AI model that receives user emotion data as input and outputs the priority of the input photos.

[0057] When inputting 2D photos, the reception unit can prioritize inputting highly relevant photos based on the user's geographical location information. For example, the reception unit prioritizes inputting photos related to the user's current location. For example, the reception unit acquires the user's GPS data and analyzes the user's current geographical location information. Then, the reception unit selects highly relevant photos based on the analysis results. The reception unit can also prioritize inputting photos related to places the user has visited in the past. For example, the reception unit stores the user's past location information in a database and analyzes it using AI. Then, the reception unit selects highly relevant photos based on the analysis results. The reception unit can also prioritize inputting photos related to travel destinations the user is planning. For example, the reception unit analyzes the user's travel plans and selects relevant photos. This allows highly relevant photos to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can select photos using a generation AI model that inputs the user's geographical location information and outputs highly relevant photos.

[0058] When inputting a 2D photo, the reception unit can analyze the user's social media activity and input related photos. For example, the reception unit prioritizes inputting photos recently posted by the user on social media. For example, the reception unit analyzes the user's social media account and acquires recent posts. Then, the reception unit selects related photos based on the acquired posts. The reception unit can also prioritize inputting photos that have received many likes and comments on social media. For example, the reception unit analyzes the user's social media engagement data and selects popular photos. The reception unit can also prioritize inputting photos related to accounts the user follows on social media. For example, the reception unit analyzes the user's follow list and selects related photos. This allows related photos to be prioritized based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the reception unit can select photos using a generative AI model that inputs the user's social media activity data and outputs related photos.

[0059] The generation unit can estimate the user's emotions and adjust the 3D photo generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit generates a 3D photo that progresses at a leisurely pace. For example, the generation unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Then, the generation unit adjusts the 3D photo generation method based on the estimated emotions. Furthermore, if the user is in a hurry, the generation unit can generate a 3D photo that emphasizes the shortest route. For example, the generation unit records the user's voice and estimates the user's emotions using voice analysis technology. Then, the generation unit adjusts the 3D photo generation method based on the estimated emotions. Furthermore, if the user is excited, the generation unit can generate a 3D photo with a visually stimulating effect. For example, the generation unit analyzes the user's text input and estimates the user's emotions using text analysis technology. Then, the generation unit adjusts the 3D photo generation method based on the estimated emotions. This allows the generation of an optimal 3D photo 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using a generative AI, or not. For example, the generation unit can adjust the generation method using a generative AI model that takes user emotion data as input and outputs a method for generating 3D photographs.

[0060] The generation unit can analyze each element of a 2D photo and adjust the level of detail based on the priority of the photo when reproducing a three-dimensional structure. For example, the generation unit may reproduce a person in the center of the photo with a high level of detail and the background with a low level of detail. For example, the generation unit may analyze each element of the photo using object recognition technology to identify important elements. The generation unit may then adjust the level of detail based on the identified important elements. The generation unit may also reproduce particularly important objects in the photo with a high level of detail and other parts with a low level of detail. For example, the generation unit may use edge detection technology to identify important parts of the photo and adjust the level of detail. The generation unit may also reproduce parts of the photo specified by the user with a high level of detail and other parts with a low level of detail. For example, the generation unit may use segmentation technology to divide each part of the photo and adjust the level of detail. This allows a 3D photo to be generated with an optimal level of detail depending on the importance of the photo. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the level of detail using a generative AI model that takes each element of the photo as input and outputs a 3D photo with adjusted level of detail.

[0061] The generation unit can apply different 3D generation algorithms depending on the category of the 2D photo. For example, the generation unit can apply an algorithm that emphasizes the depth of nature to landscape photos. For example, the generation unit can use image classification technology to classify the photo category. Then, the generation unit selects an appropriate 3D generation algorithm based on the classified category. The generation unit can also apply an algorithm that emphasizes the three-dimensionality of people to portrait photos. For example, the generation unit can use a deep learning algorithm to generate 3D images according to the photo category. The generation unit can also apply an algorithm that reproduces the detailed structure of a building to architectural photos. For example, the generation unit can reproduce the building structure using stereo matching technology. This allows the optimal 3D generation algorithm to be applied depending on the photo category. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can apply an algorithm using a generation AI model that inputs the photo category and outputs an appropriate 3D generation algorithm.

[0062] The generation unit can estimate the user's emotions and adjust the generation order of 3D photos based on the estimated emotions. For example, if the user is emotional, the generation unit prioritizes creating 3D images of emotional photos. For example, the generation unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Then, the generation unit adjusts the generation order of 3D photos based on the estimated emotions. Furthermore, if the user is having fun, the generation unit can prioritize creating 3D images of photos capturing happy moments. For example, the generation unit can record the user's voice and estimate the user's emotions using voice analysis technology. Then, the generation unit adjusts the generation order of 3D photos based on the estimated emotions. Furthermore, if the user is sad, the generation unit can prioritize creating 3D images of comforting photos. For example, the generation unit can analyze the user's text input and estimate the user's emotions using text analysis technology. Then, the generation unit adjusts the generation order of 3D photos based on the estimated emotions. This allows 3D photos to be generated in an optimal order according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, the generative AI, or may be performed without using the generative AI. For example, the generation unit may adjust the order using a generative AI model that receives user emotion data as input and outputs the order in which 3D photos are generated.

[0063] The generation unit can determine the priority for generating 3D photos based on the time the 2D photos were taken. For example, the generation unit prioritizes 3D conversion of recently taken photos. For example, the generation unit analyzes the metadata of the photos to identify the time the photos were taken. Then, the generation unit determines the priority for generating 3D photos based on the identified time the photos were taken. The generation unit can also prioritize 3D conversion of photos taken on specific events or anniversaries. For example, the generation unit analyzes a user's calendar events and selects related photos. Then, the generation unit prioritizes 3D conversion of the selected photos. The generation unit can also prioritize 3D conversion of photos taken within a period specified by the user. For example, the generation unit analyzes a period specified by the user and selects related photos. Then, the generation unit prioritizes 3D conversion of the selected photos. This allows 3D photos to be generated in an optimal order based on the time the photos were taken. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI or without using a generation AI. For example, the generation unit can determine the priority using a generative AI model that takes the time the photo was taken as input and outputs the priority of generating 3D photos.

[0064] The generation unit can adjust the generation order of 3D photos based on the relevance of the 2D photos. For example, the generation unit sequentially generates 3D photos of photos taken at the same event. For example, the generation unit analyzes the metadata of the photos to identify highly related photos. Then, the generation unit adjusts the generation order of the 3D photos based on the identified relevance. The generation unit can also sequentially generate 3D photos of photos taken at the same location. For example, the generation unit analyzes the location information of the photos to identify highly related photos. Then, the generation unit adjusts the generation order of the 3D photos based on the identified relevance. The generation unit can also sequentially generate 3D photos of photos that include the same person. For example, the generation unit uses facial recognition technology to identify photos that include the same person. Then, the generation unit adjusts the generation order of the 3D photos based on the identified relevance. This allows 3D photos to be generated in an optimal order based on the relevance. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the order using a generative AI model that takes photo relevance data as input and outputs the order in which 3D photos are generated.

[0065] The experience unit can estimate the user's emotions and adjust the way the metaverse space is experienced based on the estimated emotions. For example, if the user is relaxed, the experience unit can provide a metaverse space that progresses at a leisurely pace. For example, the experience unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. Then, the experience unit can adjust the way the metaverse space is experienced based on the estimated emotions. Furthermore, if the user is in a hurry, the experience unit can provide a metaverse space that tours key points in a short period of time. For example, the experience unit can record the user's voice and estimate the user's emotions using voice analysis technology. Then, the experience unit can adjust the way the metaverse space is experienced based on the estimated emotions. Furthermore, if the user is excited, the experience unit can provide a metaverse space with visually stimulating effects. For example, the experience unit can analyze the user's text input and estimate the user's emotions using text analysis technology. Then, the experience unit can adjust the way the metaverse space is experienced based on the estimated emotions. This allows the user to experience the metaverse space optimally according to their emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the experience unit can be performed using, for example, a generative AI, or can be performed without using a generative AI. For example, the experience unit can adjust the experience method using a generative AI model that inputs the user's emotion data and outputs a method for experiencing the metaverse space.

[0066] When experiencing the metaverse space, the experience unit can select an appropriate experience method by referring to the user's past experience history. The experience unit, for example, suggests a similar experience based on experiences the user enjoyed in the past. For example, the experience unit stores the user's past experience history in a database and analyzes it using AI. The experience unit then selects an appropriate experience method based on the analysis results. The experience unit can also suggest a different experience based on experiences the user avoided in the past. For example, the experience unit analyzes the user's past experience history and identifies experiences that the user avoided. The experience unit then suggests a different experience based on the identified experiences. The experience unit can also select an optimal experience method from the user's past experience history. For example, the experience unit analyzes the user's past experience history in chronological order and selects an optimal experience method. This allows the optimal experience method to be provided based on the user's past experience history. Some or all of the above-described processing in the experience unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the experience unit can select an experience method using a generation AI model that inputs the user's past experience history and outputs an appropriate experience method.

[0067] The experience unit can customize the experience content based on the user's current living situation when experiencing the metaverse space. For example, if the user is busy, the experience unit can provide an experience that can be enjoyed in a short amount of time. For example, the experience unit can analyze the user's schedule and identify the user's current living situation. Then, the experience unit customizes the experience content based on the identified living situation. The experience unit can also provide an experience that can be enjoyed for a long time if the user is relaxed. For example, the experience unit can analyze the user's health status and identify the user's current living situation. Then, the experience unit customizes the experience content based on the identified living situation. The experience unit can also provide an experience related to a specific event if the user is participating in that event. For example, the experience unit can analyze the user's calendar events and select a related experience. Then, the experience unit provides the selected experience. This allows the optimal experience content to be provided according to the user's living situation. Some or all of the above-described processing in the experience unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the experience unit can customize the content using a generation AI model that uses the user's living situation data as input and customizes the experience content.

[0068] The experience unit can estimate the user's emotions and adjust the order of experiences in the metaverse space based on the estimated emotions. For example, if the user is emotional, the experience unit prioritizes providing emotional experiences. For example, the experience unit captures the user's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. Then, the experience unit adjusts the order of experiences in the metaverse space based on the estimated emotion. The experience unit can also prioritize providing enjoyable experiences if the user is having fun. For example, the experience unit records the user's voice and estimates the emotion using voice analysis technology. Then, the experience unit adjusts the order of experiences in the metaverse space based on the estimated emotion. The experience unit can also prioritize comforting experiences if the user is sad. For example, the experience unit analyzes the user's text input and estimates the emotion using text analysis technology. Then, the experience unit adjusts the order of experiences in the metaverse space based on the estimated emotion. This allows the user to experience the metaverse space in an optimal order depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the experience unit can be performed using, for example, a generative AI, or can be performed without using a generative AI. For example, the experience unit can adjust the order using a generative AI model that inputs the user's emotion data and outputs the order of experiences in the metaverse space.

[0069] When experiencing the metaverse space, the experience unit can select an appropriate experience method based on the user's geographic location information. The experience unit, for example, provides an experience related to the user's current location. For example, the experience unit acquires the user's GPS data and analyzes the user's current geographic location information. Then, the experience unit selects an appropriate experience method based on the analysis results. The experience unit can also provide an experience related to places the user has visited in the past. For example, the experience unit stores the user's past location information in a database and analyzes it using AI. Then, the experience unit selects an appropriate experience method based on the analysis results. The experience unit can also provide an experience related to a travel destination the user is planning. For example, the experience unit analyzes the user's travel plans and selects a related experience. Then, the experience unit provides the selected experience. This allows the optimal experience method to be provided based on the user's geographic location information. Some or all of the above-mentioned processing in the experience unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the experience unit can select an experience method using a generation AI model that inputs the user's geographic location information and outputs an appropriate experience method.

[0070] The experience unit can analyze the user's social media activity and suggest experience content when experiencing the metaverse space. The experience unit, for example, provides experiences related to content recently posted by the user on social media. For example, the experience unit analyzes the user's social media account and acquires recent posts. Then, the experience unit selects related experiences based on the acquired posts. The experience unit can also provide experiences related to content that the user has received many likes or comments on social media. For example, the experience unit analyzes the user's social media engagement data and selects popular content. Then, the experience unit provides related experiences based on the selected content. The experience unit can also provide experiences related to accounts the user follows on social media. For example, the experience unit analyzes the user's follow list and selects related experiences. This allows the optimal experience content to be provided based on the user's social media activity. Some or all of the above-described processing in the experience unit may be performed, for example, using a generative AI, or may be performed without using a generative AI. For example, the experience unit can input the user's social media activity data and suggest experience content using a generative AI model that suggests experience content. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described reception unit, generation unit, and experience unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives input of a 2D photo from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the 2D photo using a generation AI to generate a 3D photo. The experience unit is realized, for example, by the control unit 46A of the smart device 14 and allows the user to experience the generated Metaverse space. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, generation unit, and experience unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives input of a 2D photo from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the 2D photo using a generation AI to generate a 3D photo. The experience unit is realized, for example, by the control unit 46A of the smart glasses 214 and allows the user to experience the generated Metaverse space. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, generation unit, and experience unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives input of a 2D photo from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the 2D photo using a generation AI to generate a 3D photo. The experience unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and allows the user to experience the generated Metaverse space. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and experience unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives input of a 2D photo from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the 2D photo using a generation AI to generate a 3D photo. The experience unit is realized, for example, by the control unit 46A of the robot 414 and allows the user to experience the generated Metaverse space.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The reception unit can analyze the user's past photo input history and select the optimal input method. For example, it can prioritize and suggest input methods (such as voice and text) that the user has frequently used in the past. For example, the reception unit stores the user's past photo input history in a database and analyzes it using AI. Next, the reception unit selects the optimal input method based on the analysis results. The reception unit can also suggest a related input method based on the type or theme of photos the user has previously input. For example, the reception unit can analyze the user's past photo input history and select an input method related to a specific theme. The reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. For example, the reception unit can analyze the user's past input history in chronological order and select the optimal input method for a specific time period. This allows the optimal input method to be suggested based on the user's past input history. Some or all of the above-described processing in the reception unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reception unit can select an input method using a generation AI model that inputs the user's past photo input history and outputs the optimal input method.

[0073] When inputting 2D photos, the reception unit can filter them based on the user's current areas of interest or events. For example, photos related to events in which the user is currently participating can be preferentially input. For example, the reception unit can analyze the user's calendar events and social media activity to select related photos. Then, the reception unit preferentially inputs the selected photos. The reception unit can also automatically select and input related photos based on the user's areas of interest. For example, the reception unit can analyze the user's hobbies and past search history to select related photos. If the user is interested in a particular theme, the reception unit can preferentially input photos related to that theme. For example, the reception unit can analyze the user's social media activity to select photos related to the particular theme. This allows photos related to the user's areas of interest or events to be preferentially input. Some or all of the above-described processing in the reception unit can be performed using, for example, a generative AI, or can be performed without using a generative AI. For example, the reception unit can perform filtering using a generative AI model that inputs the user's areas of interest and event information and outputs related photos.

[0074] The reception unit can estimate the user's emotions and prioritize the photos to be input based on the estimated emotions. For example, if the user is emotional, it prioritizes the input of emotional photos. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. Then, the reception unit prioritizes the photos to be input based on the estimated emotion. Alternatively, if the user is having fun, the reception unit can prioritize the input of photos capturing happy moments. For example, the reception unit can record the user's voice and estimate the emotion using voice analysis technology. Then, the reception unit prioritizes the photos to be input based on the estimated emotion. Alternatively, if the user is sad, the reception unit can prioritize the input of comforting photos. For example, the reception unit can analyze the user's text input and estimate the emotion using text analysis technology. Then, the reception unit prioritizes the photos to be input based on the estimated emotion. This allows the most appropriate photos to be input preferentially according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit may determine the priority using a generation AI model that receives user emotion data as input and outputs the priority of the input photos.

[0075] The generation unit can estimate the user's emotions and adjust the 3D photo generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit generates a 3D photo that progresses at a leisurely pace. For example, the generation unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Then, the generation unit adjusts the 3D photo generation method based on the estimated emotions. Alternatively, if the user is in a hurry, the generation unit can generate a 3D photo that emphasizes the shortest route. For example, the generation unit records the user's voice and estimates the user's emotions using voice analysis technology. Then, the generation unit adjusts the 3D photo generation method based on the estimated emotions. Alternatively, if the user is excited, the generation unit can generate a 3D photo with a visually stimulating effect. For example, the generation unit analyzes the user's text input and estimates the user's emotions using text analysis technology. Then, the generation unit adjusts the 3D photo generation method based on the estimated emotions. This allows the generation of an optimal 3D photo according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit may adjust the generation method using a generation AI model that receives user emotion data as input and outputs a method for generating a 3D photo.

[0076] The generation unit can analyze each element of a 2D photo and adjust the level of detail based on the priority of the photo when reproducing a three-dimensional structure. For example, the generation unit can reproduce a person in the center of the photo with high detail and the background with low detail. For example, the generation unit can analyze each element of the photo using object recognition technology to identify important elements. The generation unit can then adjust the level of detail based on the identified important elements. The generation unit can also reproduce particularly important objects in the photo with high detail and other parts with low detail. For example, the generation unit can identify important parts of the photo using edge detection technology and adjust the level of detail. The generation unit can also reproduce parts of the photo specified by the user with high detail and other parts with low detail. For example, the generation unit can divide each part of the photo using segmentation technology and adjust the level of detail. This allows a 3D photo to be generated with an optimal level of detail depending on the importance of the photo. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using a generation AI or without a generation AI. For example, the generation unit can adjust the level of detail using a generative AI model that takes each element of the photo as input and outputs a 3D photo with adjusted level of detail.

[0077] The generation unit can apply different 3D generation algorithms depending on the category of the 2D photo. For example, an algorithm that emphasizes the depth of nature can be applied to landscape photos. For example, the generation unit can use image classification technology to classify the photo category. Then, the generation unit selects an appropriate 3D generation algorithm based on the classified category. The generation unit can also apply an algorithm that emphasizes the three-dimensionality of people to portrait photos. For example, the generation unit can use a deep learning algorithm to generate 3D images according to the photo category. The generation unit can also apply an algorithm that reproduces the detailed structure of a building to architectural photos. For example, the generation unit can reproduce the building structure using stereo matching technology. This allows the optimal 3D generation algorithm to be applied depending on the photo category. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can apply an algorithm using a generation AI model that inputs the photo category and outputs an appropriate 3D generation algorithm.

[0078] The generation unit can estimate the user's emotions and adjust the generation order of 3D photos based on the estimated emotions. For example, if the user is emotional, the generation unit prioritizes creating 3D images of emotional photos. For example, the generation unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Then, the generation unit adjusts the generation order of 3D photos based on the estimated emotions. Alternatively, if the user is having fun, the generation unit can prioritize creating 3D images of photos capturing happy moments. For example, the generation unit can record the user's voice and estimate the user's emotions using voice analysis technology. Then, the generation unit adjusts the generation order of 3D photos based on the estimated emotions. Alternatively, if the user is sad, the generation unit can prioritize creating 3D images of comforting photos. For example, the generation unit can analyze the user's text input and estimate the user's emotions using text analysis technology. Then, the generation unit adjusts the generation order of 3D photos based on the estimated emotions. This allows 3D photos to be generated in an optimal order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit may adjust the order using a generation AI model that receives user emotion data as input and outputs the order in which 3D photos are generated.

[0079] The experience unit can estimate the user's emotions and adjust the way the user experiences the metaverse space based on the estimated emotions. For example, if the user is relaxed, the experience unit can provide a metaverse space that progresses at a leisurely pace. For example, the experience unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. Then, the experience unit can adjust the way the user experiences the metaverse space based on the estimated emotions. Furthermore, if the user is in a hurry, the experience unit can provide a metaverse space that allows the user to visit key points in a short amount of time. For example, the experience unit can record the user's voice and estimate the user's emotions using voice analysis technology. Then, the experience unit can adjust the way the user experiences the metaverse space based on the estimated emotions. Furthermore, if the user is excited, the experience unit can provide a metaverse space with visually stimulating effects. For example, the experience unit can analyze the user's text input and estimate the user's emotions using text analysis technology. Then, the experience unit can adjust the way the user experiences the metaverse space based on the estimated emotions. This allows the user to experience the metaverse space optimally according to their emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the experience unit can be performed using, for example, a generative AI, or can be performed without using a generative AI. For example, the experience unit can adjust the experience method using a generative AI model that inputs the user's emotion data and outputs a method for experiencing the metaverse space.

[0080] When experiencing the metaverse space, the experience unit can select an appropriate experience method by referring to the user's past experience history. For example, the experience unit can suggest a similar experience based on experiences the user enjoyed in the past. For example, the experience unit stores the user's past experience history in a database and analyzes it using AI. Then, the experience unit selects an appropriate experience method based on the analysis results. The experience unit can also suggest a different experience based on experiences the user avoided in the past. For example, the experience unit analyzes the user's past experience history and identifies experiences that the user avoided. Then, the experience unit suggests a different experience based on the identified experiences. The experience unit can also select an optimal experience method from the user's past experience history. For example, the experience unit analyzes the user's past experience history in chronological order and selects an optimal experience method. This allows the optimal experience method to be provided based on the user's past experience history. Some or all of the above-mentioned processing in the experience unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the experience unit can select an experience method using a generation AI model that inputs the user's past experience history and outputs an appropriate experience method.

[0081] The experience unit can customize the experience content based on the user's current living situation when experiencing the metaverse space. For example, if the user is busy, the experience unit can provide an experience that can be enjoyed in a short amount of time. For example, the experience unit can analyze the user's schedule and identify the user's current living situation. Then, the experience unit customizes the experience content based on the identified living situation. Alternatively, if the user is relaxed, the experience unit can provide an experience that can be enjoyed for a long period of time. For example, the experience unit can analyze the user's health status and identify the user's current living situation. Then, the experience unit customizes the experience content based on the identified living situation. Alternatively, if the user is participating in a specific event, the experience unit can provide an experience related to the event. For example, the experience unit can analyze the user's calendar events and select a related experience. Then, the experience unit provides the selected experience. This allows the optimal experience content to be provided according to the user's living situation. Some or all of the above-described processing in the experience unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the experience unit can customize the content using a generation AI model that uses the user's living situation data as input and customizes the experience content.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The reception unit receives input of a 2D photo from a user. The 2D photo from the user includes, for example, a file format such as JPEG, PNG, or BMP, a resolution, a color mode, etc. The reception unit provides an interface for the user to upload the 2D photo and can also display guidelines for selecting and uploading a photo. Step 2: The generation unit uses the generation AI to analyze the 2D photo received by the reception unit and generate a 3D photo. The generation AI analyzes each element of the photo and recreates a three-dimensional structure. For example, it analyzes the depth of the people and background in the photo and generates a 3D model. The generation unit can also recreate past spaces based on the information in the photo. For example, it can recreate specific locations in 3D, such as scenery from a travel destination or a place where family members gather. Step 3: The generator generates a metaverse space based on the generated 3D photo. The generator uses a generation AI to build a metaverse space based on the photo information. Step 4: The experience unit allows the user to experience the metaverse space generated by the generation unit. The experience unit provides an interface that allows the user to freely move around and experience the metaverse space. For example, the user can experience the metaverse space using a VR headset. The user can also experience the metaverse space using a PC or smartphone.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0114] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0118] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0121] 7, a 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.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0128] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0131] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0137] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0138] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0139] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0140] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0141] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0142] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0143] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0144] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0145] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0146] 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.

[0147] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0148] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0149] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0150] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0151] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0152] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0153] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0154] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0155] [Explanation of symbols]

[0156] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of a 2D photograph from a user; a generation unit that analyzes the 2D photograph received by the reception unit and generates a 3D photograph; A unit for generating a metaverse space based on the 3D photograph generated by the generation unit; an experience unit that enables a user to experience the metaverse space generated by the generation unit; A system characterized by:

2. The reception unit Estimate the user's emotions and adjust the timing of 2D photo input based on the estimated emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past photo input history and select the appropriate input method 2. The system of claim 1.

4. The reception unit Filtering 2D photos based on the user's current interests or events when entering them 2. The system of claim 1.

5. The reception unit Estimate the user's emotions and prioritize the photos to be entered based on the estimated emotions.

2. The system of claim 1.

6. The reception unit When entering 2D photos, the app prioritizes photos that are more relevant based on the user's geographic location.

2. The system of claim 1.

7. The reception unit When entering 2D photos, the app analyzes the user's social media activity and inputs relevant photos.

2. The system of claim 1.

8. The generation unit Estimate the user's emotions and adjust the way 3D photos are generated based on the estimated emotions.

2. The system of claim 1.

9. The generation unit Analyzes each element of a 2D photo and adjusts the level of detail based on the priority of the photo when recreating the 3D structure 2. The system of claim 1.

10. The generation unit Apply different 3D generation algorithms depending on the category of 2D photos 2. The system of claim 1.

Images