system

Abstract

The system according to the embodiment aims to reproduce a photograph in three dimensions, allowing the user to experience the world of that photograph. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a control unit, and a provision unit. The reception unit receives a photo input from a user. The analysis unit analyzes the photo received by the reception unit and generates 3D data. The generation unit generates a 3D model based on the 3D data generated by the analysis unit. The control unit controls a VR device based on the 3D model generated by the generation unit. The provision unit provides the 3D data to the user through the VR device controlled by the control 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] Conventional technology has had the problem of making it difficult to reproduce a photograph in three dimensions and for users to experience that world.

[0005] The system according to the embodiment aims to reproduce a photograph in three dimensions, allowing the user to experience the world of that photograph. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, a control unit, and a provision unit. The reception unit receives a photo input from a user. The analysis unit analyzes the photo received by the reception unit and generates 3D data. The generation unit generates a 3D model based on the 3D data generated by the analysis unit. The control unit controls a VR device based on the 3D model generated by the generation unit. The provision unit provides the 3D data to the user through the VR device controlled by the control unit. [Effects of the Invention]

[0007] The system according to the embodiment can reproduce a photograph in three dimensions, allowing the user to experience the world of the photograph. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention analyzes photos using AI and converts them into three-dimensional data. In this system, a user inputs a photo, and the AI ​​analyzes the photo and generates three-dimensional data. The generated three-dimensional data allows users to immerse themselves in the world of the photo using devices such as VR. Users can re-experience the generated three-dimensional world and experience a time-travel experience to the era or place in which the photo was captured. For example, a user inputs a photo. Any photo can be input, such as a family photo or a landscape photo from a travel destination. Next, the AI ​​analyzes the input photo. The AI ​​analyzes each element of the photo in detail to generate three-dimensional data. For example, it analyzes the relative positions and depth of people, buildings, and scenery in the photo and creates a three-dimensional model based on that analysis. The generated three-dimensional data allows users to immerse themselves in the world of the photo using devices such as VR. By wearing VR goggles, users can experience the sensation of being inside the photo. For example, if the photo is a family photo, they can feel as if they are actually there as a family member. Furthermore, users can re-experience the generated three-dimensional world. For example, if the photo is a landscape photo from a travel destination, they can walk around the location as if they were actually visiting the place. This allows users to experience a time slip to the era and place of the photograph. This allows the system to enable users to rediscover the value of photographs from a new perspective. For example, by recreating old family photos in 3D, users can feel the memories of those times more vividly. Furthermore, by recreating historical photographs in 3D, users can gain a deeper understanding of the atmosphere and events of that era.

[0029] A photo analysis system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a control unit, and a provision unit. The reception unit accepts photo input from a user. The user can select any photo and upload it to the system. Any photo can be input, such as a family photo or a landscape photo from a travel destination. The analysis unit uses AI to analyze the photo accepted by the reception unit and generate 3D data. The analysis unit analyzes each element of the photo using, for example, deep learning to generate three-dimensional data. The generation unit uses 3D modeling technology to generate a 3D model based on the 3D data generated by the analysis unit. The generation unit generates the 3D model using, for example, polygon modeling or sculpting technology. The control unit controls a VR device based on the 3D model generated by the generation unit. The control unit controls a device such as VR goggles or VR gloves to provide the user with three-dimensional data. The provision unit provides the three-dimensional data to the user through a VR device controlled by the control unit. The provision unit provides, for example, an interface that allows the user to immerse themselves in a three-dimensional world using the VR device. As a result, the photo analysis system according to the embodiment allows users to input photos, generate three-dimensional data, and experience the data through a VR device, allowing the system to rediscover the value of photos from a new perspective.

[0030] The analysis unit can analyze each element of a photo using machine learning and generate 3D data. The analysis unit can analyze each element of a photo using, for example, deep learning and generate three-dimensional data. For example, the analysis unit can analyze the relative positions and depth of people, buildings, scenery, etc. in the photo and create a three-dimensional model based on that. The analysis unit can also analyze each element of a photo using a support vector machine. For example, the analysis unit can detect edges and corners in the photo and generate three-dimensional data based on that. The analysis unit can also analyze each element of a photo using a convolutional neural network. For example, the analysis unit can extract features of the photo and generate three-dimensional data based on that. In this way, by using deep learning, each element of a photo can be analyzed in detail and three-dimensional data can be generated.

[0031] The generation unit can generate a 3D model based on the 3D data using 3D modeling technology. The generation unit can generate a 3D model based on the 3D data using, for example, polygon modeling. For example, the generation unit can divide the 3D data into polygons and generate a 3D model based on the polygons. The generation unit can also generate a 3D model based on the 3D data using sculpting technology. For example, the generation unit can sculpt details based on the 3D data to generate a detailed 3D model. The generation unit can also generate a 3D model based on the 3D data using voxel-based modeling technology. For example, the generation unit can convert the 3D data into voxels and generate a 3D model based on the voxels. As a result, using 3D modeling technology can generate a highly accurate 3D model based on three-dimensional data.

[0032] The control unit can provide 3D data to a user through a VR device. The control unit can provide 3D data to a user through, for example, VR goggles. For example, the control unit can display three-dimensional data to a user wearing the VR goggles. The control unit can also provide 3D data to a user through VR gloves. For example, the control unit can provide an interface for a user wearing the VR gloves to operate the three-dimensional data. The control unit can also provide 3D data to a user through a VR chair. For example, the control unit can provide an environment for a user sitting in a VR chair to experience three-dimensional data. By providing three-dimensional data to a user through a VR device, the user can experience immersion in the world of a photograph.

[0033] The providing unit can provide an interface for the user to enter a 3D world using the VR device. The providing unit, for example, provides a user interface. For example, the providing unit provides a simple operation method for the user to enter a 3D world using the VR device. The providing unit can also provide a graphical user interface. For example, the providing unit provides an interface that the user can operate visually. The providing unit can also provide an audio interface. For example, the providing unit provides an interface that the user can operate by audio. In this way, by providing an interface for the user to enter a three-dimensional world using the VR device, the user can easily experience the three-dimensional world.

[0034] The reception unit can analyze the user's past photo input history and select the optimal input method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. For example, the reception unit retrieves the user's past input history from a database and identifies the input method that was frequently used. The reception unit can also suggest the optimal input method based on the types of photos the user has uploaded in the past. For example, the reception unit analyzes metadata of photos the user has uploaded in the past and selects the optimal input method. The reception unit can also predict and suggest the input method that will be used in 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 identifies the input method that will be used in a specific time period. In this way, the optimal input method can be suggested by analyzing the user's past photo input history.

[0035] The reception unit can filter the photos based on the user's current project or area of ​​interest when the photos are input. For example, the reception unit filters the photos so that the user only inputs photos related to the project currently in progress. For example, the reception unit acquires the user's project information from a database and identifies related photos. The reception unit can also preferentially input related photos based on the user's area of ​​interest. For example, the reception unit acquires the user's area of ​​interest from profile information and identifies related photos. The reception unit can also filter related photos by referring to the user's past project history. For example, the reception unit acquires the user's past project history from a database and identifies related photos. In this way, by filtering photos based on the user's current project or area of ​​interest, highly relevant photos can be preferentially input.

[0036] When a user inputs a photo, the reception unit can select the optimal input means depending on the user's input method (voice, text, image, etc.). For example, if the user selects voice input, the reception unit inputs a description of the photo using voice recognition technology. For example, the reception unit converts the user's voice into text using voice recognition software and saves it as a description of the photo. Furthermore, if the user selects text input, the reception unit can also provide a simple text input interface. For example, the reception unit provides an interface for the user to input a description of the photo using a keyboard. Furthermore, if the user selects image input, the reception unit can also analyze the content of the photo using image recognition technology. For example, the reception unit analyzes the content of the photo using image recognition software and saves it as a description of the photo. In this way, an interface that is easy for the user to use can be provided by selecting the optimal input means depending on the user's input method.

[0037] When inputting photos, the reception unit can prioritize inputting photos with high relevance based on the user's geographical location information. For example, the reception unit prioritizes inputting photos taken in locations close to the user's current location. For example, the reception unit acquires the user's current location from GPS data and identifies photos taken in the vicinity. The reception unit can also prioritize inputting photos related to locations the user has visited in the past. For example, the reception unit acquires the user's past location information from a database and identifies related photos. The reception unit can also prioritize inputting photos of events or places related to the user's current location. For example, the reception unit identifies photos of related events or places based on the user's current location. In this way, highly relevant photos can be prioritized by taking the user's geographical location information into consideration.

[0038] When inputting photos, the reception unit can analyze the user's social media activity and input related photos. The reception unit, for example, preferentially inputs photos shared by the user on social media. For example, the reception unit obtains and inputs photos shared from the user's social media account. The reception unit can also analyze the content posted by the user on social media and input related photos. For example, the reception unit can analyze the content posted by the user using text analysis technology and identify related photos. The reception unit can also input related photos with reference to the activity of the user's friends on social media. For example, the reception unit can analyze the content posted by the user's friends and identify related photos. In this way, the reception unit can preferentially input related photos by analyzing the user's social media activity.

[0039] The reception unit can customize the input method based on the user's past feedback when inputting a photo. For example, the reception unit preferentially provides an input method that the user has previously preferred. For example, the reception unit obtains the user's past feedback from a database and identifies the preferred input method. The reception unit can also customize the input interface based on the user's past feedback. For example, the reception unit adjusts the design and functions of the input interface based on the user's feedback. The reception unit can also optimize the input procedure by referring to the user's past feedback. For example, the reception unit simplifies the input procedure and improves usability based on the user's feedback. In this way, the input method can be optimized by reflecting the user's past feedback.

[0040] During analysis, the analysis unit can adjust the accuracy of the analysis based on the importance of the photo. The analysis unit, for example, performs a detailed analysis on important photos. For example, the analysis unit performs a detailed analysis based on the importance specified by the user. The analysis unit can also perform a concise analysis on general photos. For example, the analysis unit performs a concise analysis based on the importance specified by the user. The analysis unit can also adjust the level of detail of the analysis based on the importance specified by the user. For example, the analysis unit adjusts the level of detail of the analysis based on the importance specified by the user. In this way, by adjusting the level of detail of the analysis based on the importance of the photo, a detailed analysis can be performed on important photos.

[0041] During analysis, the analysis unit can apply different analysis algorithms based on the category of the photo. For example, the analysis unit applies a face recognition algorithm to photos of people. For example, the analysis unit uses the face recognition algorithm to detect faces in photos of people and perform analysis. The analysis unit can also apply a landscape analysis algorithm to photos of landscapes. For example, the analysis unit uses a landscape analysis algorithm to detect features in photos of landscapes and perform analysis. The analysis unit can also apply a building analysis algorithm to photos of buildings. For example, the analysis unit uses a building analysis algorithm to detect the structure of photos of buildings and perform analysis. This allows for more accurate analysis by applying different analysis algorithms depending on the category of the photo.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. For example, the analysis unit retrieves the user's past analysis results from a database and optimizes the analysis algorithm. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and suggests an optimal analysis method. The analysis unit can also improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0043] During analysis, the analysis unit can determine the order of analysis based on the time the photos were taken. The analysis unit, for example, prioritizes analyzing photos taken recently. For example, the analysis unit obtains the time of photo shooting from the metadata of the photos and identifies the photos taken recently. The analysis unit can also prioritize analyzing photos taken at a specific time. For example, the analysis unit identifies photos based on the time of photo shooting specified by the user. The analysis unit can also determine the priority of analysis based on the time of photo shooting specified by the user. For example, the analysis unit adjusts the order of analysis based on the time of photo shooting specified by the user. In this way, by determining the priority of analysis based on the time of photo shooting, more important photos can be analyzed preferentially.

[0044] During analysis, the analysis unit can adjust the priority of analysis based on the relevance of the photos. For example, the analysis unit prioritizes analysis of highly relevant photos. For example, the analysis unit analyzes the content of the photos and identifies highly relevant photos. The analysis unit can also adjust the order of analysis based on the relevance specified by the user. For example, the analysis unit adjusts the order of analysis based on the relevance specified by the user. The analysis unit can also adjust the order of analysis based on the content of the photos. For example, the analysis unit analyzes the content of the photos and identifies highly relevant photos. In this way, by adjusting the order of analysis based on the relevance of the photos, highly relevant photos can be analyzed preferentially.

[0045] During analysis, the analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise. For example, if the user has specialized knowledge, the analysis unit performs an analysis that makes extensive use of technical terms. For example, the analysis unit obtains the expertise level from the user's profile information and performs an analysis that makes extensive use of technical terms. Furthermore, if the user has general knowledge, the analysis unit can also perform a concise analysis. For example, the analysis unit obtains the expertise level from the user's profile information and performs a concise analysis. Furthermore, the analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise. For example, the analysis unit obtains the expertise level from the user's profile information and adjusts the use of technical terms. In this way, by adjusting the use of technical terms in the analysis based on the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0046] The generation unit can adjust the accuracy of the 3D model based on the importance of the photo during generation. For example, the generation unit generates a detailed 3D model for an important photo. For example, the generation unit generates a detailed 3D model based on the importance specified by the user. The generation unit can also generate a concise 3D model for an ordinary photo. For example, the generation unit generates a concise 3D model based on the importance specified by the user. The generation unit can also adjust the level of detail of the 3D model based on the importance specified by the user. For example, the generation unit adjusts the level of detail of the 3D model based on the importance specified by the user. In this way, by adjusting the level of detail of the 3D model based on the importance of the photo, a detailed 3D model can be generated for an important photo.

[0047] The generation unit can apply different 3D modeling techniques based on the photo category during generation. For example, the generation unit applies a technique for generating a detailed 3D model of a face to a portrait photo. For example, the generation unit uses face recognition technology to reproduce the face in detail in the portrait photo. The generation unit can also apply a technique for generating a wide-area 3D model to a landscape photo. For example, the generation unit applies a technique for reproducing a wide area of ​​the landscape photo. The generation unit can also apply a technique for reproducing the building structure in detail to a building photo. For example, the generation unit applies a technique for reproducing the building structure in detail. In this way, by applying different 3D modeling techniques depending on the photo category, a more accurate 3D model can be generated.

[0048] During generation, the generation unit can improve the accuracy of the 3D model based on the user's past generation results. The generation unit, for example, adjusts the 3D modeling technique based on the user's past generation results. For example, the generation unit retrieves the user's past generation results from a database and optimizes the 3D modeling technique. The generation unit can also improve the accuracy of the 3D model by referring to the user's past generation results. For example, the generation unit analyzes the user's past generation results and suggests an optimal 3D modeling technique. The generation unit can also improve the accuracy of the 3D model based on the user's past generation results. For example, the generation unit adjusts the 3D modeling technique based on the user's past generation results. In this way, the accuracy of the 3D model can be improved by referring to the user's past generation results.

[0049] During generation, the generation unit can determine the order of 3D models based on the dates when the photos were taken. For example, the generation unit prioritizes creating 3D models of recently taken photos. For example, the generation unit obtains the date of taking the photos from the metadata of the photos and identifies the most recently taken photos. The generation unit can also prioritize creating 3D models of photos taken at a specific time. For example, the generation unit identifies photos based on the date of taking the photos specified by the user. The generation unit can also determine the priority of 3D models based on the date of taking the photos specified by the user. For example, the generation unit adjusts the order of 3D models based on the date of taking the photos specified by the user. In this way, by determining the priority of 3D models based on the date of taking the photos, more important photos can be preferentially created as 3D models.

[0050] The generation unit can adjust the order of the 3D models based on the relevance of the photos during generation. For example, the generation unit prioritizes the creation of 3D models from highly relevant photos. For example, the generation unit analyzes the content of the photos and identifies highly relevant photos. The generation unit can also adjust the order of the 3D models based on the relevance specified by the user. For example, the generation unit adjusts the order of the 3D models based on the relevance specified by the user. The generation unit can also adjust the order of the 3D models based on the content of the photos. For example, the generation unit analyzes the content of the photos and identifies highly relevant photos. In this way, by adjusting the order of the 3D models based on the relevance of the photos, highly relevant photos can be prioritized as 3D models.

[0051] The generation unit can adjust the use of technical terminology in the 3D model based on the user's level of expertise during generation. For example, if the user has specialized knowledge, the generation unit generates a 3D model that uses a lot of technical terminology. For example, the generation unit obtains the expertise level from the user's profile information and generates a 3D model that uses a lot of technical terminology. The generation unit can also generate a concise 3D model if the user has general knowledge. For example, the generation unit obtains the expertise level from the user's profile information and generates a concise 3D model. The generation unit can also adjust the use of technical terminology in the 3D model based on the user's level of expertise. For example, the generation unit obtains the expertise level from the user's profile information and adjusts the use of technical terminology. This makes it possible to provide a 3D model that is easy for the user to understand by adjusting the use of technical terminology in the 3D model based on the user's level of expertise.

[0052] During control, the control unit can select an appropriate control method based on the user's past VR experience history. The control unit selects the optimal control method based on, for example, the user's preferred VR experiences in the past. For example, the control unit retrieves the user's past VR experience history from a database and identifies the preferred control method. The control unit can also customize the control method by referring to the user's past VR experience history. For example, the control unit optimizes the control method based on the user's past feedback. The control unit can also adjust the control method by reflecting the user's past feedback in real time. For example, the control unit improves the control method based on the user's feedback. In this way, the optimal control method can be selected by referring to the user's past VR experience history.

[0053] During control, the control unit can customize the control content based on the user's current task. For example, if the user wants to relax, the control unit provides a relaxing VR experience. For example, the control unit obtains the user's current task from the user's profile information and provides a relaxing VR experience. Furthermore, if the user wants to learn, the control unit can provide an educational VR experience. For example, the control unit obtains the user's current task from the user's profile information and provides an educational VR experience. Furthermore, if the user wants to be entertained, the control unit can provide a highly entertaining VR experience. For example, the control unit obtains the user's current task from the user's profile information and provides a highly entertaining VR experience. In this way, a more appropriate VR experience can be provided by customizing the control content according to the user's current task.

[0054] During control, the control unit can improve the control method based on user feedback. The control unit improves the control method of the VR experience based on, for example, user feedback. For example, the control unit acquires user feedback from a database and optimizes the control method. The control unit can also optimize the control method by referring to the user's past feedback. For example, the control unit optimizes the control procedure based on the user's past feedback. The control unit can also reflect the user's feedback in real time and adjust the control method. For example, the control unit improves the control method based on the user's feedback. In this way, the control method can be optimized by reflecting the user's feedback.

[0055] During control, the control unit can select an appropriate control method based on the user's geographical location information. The control unit provides a related VR experience based on, for example, the user's current location. For example, the control unit acquires the user's current location from GPS data and identifies a related VR experience. The control unit can also select an optimal control method based on the user's geographical location information. For example, the control unit acquires the user's current location from GPS data and selects an optimal control method. The control unit can also provide a VR experience of an event or place related to the user's current location. For example, the control unit identifies a VR experience of a related event or place based on the user's current location. This makes it possible to select an optimal control method by taking the user's geographical location information into consideration.

[0056] During control, the control unit can analyze the user's social media activities and suggest a control method. For example, the control unit can prioritize providing VR experiences shared by the user on social media. For example, the control unit can obtain and provide VR experiences shared from the user's social media account. The control unit can also analyze the user's social media posts to provide related VR experiences. For example, the control unit can analyze the user's posts using text analysis technology to identify related VR experiences. The control unit can also provide related VR experiences by referring to the activities of the user's friends on social media. For example, the control unit can analyze the user's friends' posts to identify related VR experiences. In this way, related VR experiences can be provided by analyzing the user's social media activities.

[0057] During control, the control unit can customize the control method based on the user's past feedback. For example, the control unit preferentially provides a control method that the user has previously preferred. For example, the control unit obtains the user's past feedback from a database and identifies the preferred control method. The control unit can also customize the control method based on the user's past feedback. For example, the control unit optimizes the control procedure based on the user's feedback. The control unit can also optimize the control procedure by referring to the user's past feedback. For example, the control unit improves the control method based on the user's feedback. In this way, the control method can be optimized by reflecting the user's past feedback.

[0058] At the time of provision, the providing unit can select an appropriate provision method based on the user's past experience history. For example, the providing unit preferentially provides a provision method that the user has previously preferred. For example, the providing unit retrieves the user's past experience history from a database and identifies the preferred provision method. The providing unit can also customize the provision method by referring to the user's past experience history. For example, the providing unit optimizes the provision method based on the user's past feedback. The providing unit can also adjust the provision method by reflecting the user's past feedback in real time. For example, the providing unit improves the provision method based on the user's feedback. In this way, the optimal provision method can be selected by referring to the user's past experience history.

[0059] The providing unit can customize the provided content based on the user's current task when providing the data. For example, if the user wants to relax, the providing unit provides relaxing three-dimensional data. For example, the providing unit acquires the user's current task from the user's profile information and provides relaxing three-dimensional data. Furthermore, if the user wants to learn, the providing unit can also provide educational three-dimensional data. For example, the providing unit acquires the user's current task from the user's profile information and provides educational three-dimensional data. Furthermore, if the user wants entertainment, the providing unit can also provide entertainment-oriented three-dimensional data. For example, the providing unit acquires the user's current task from the user's profile information and provides entertainment-oriented three-dimensional data. This makes it possible to provide more appropriate data by customizing the provided content according to the user's current task.

[0060] The providing unit can improve the providing method based on user feedback at the time of providing. The providing unit improves the method of providing three-dimensional data based on user feedback, for example. For example, the providing unit acquires user feedback from a database and optimizes the providing method. The providing unit can also optimize the providing method by referring to the user's past feedback. For example, the providing unit optimizes the providing procedure based on the user's past feedback. The providing unit can also reflect the user's feedback in real time and adjust the providing method. For example, the providing unit improves the providing method based on the user's feedback. In this way, the providing method can be optimized by reflecting the user's feedback.

[0061] The providing unit can select an appropriate providing method based on the user's geographical location information at the time of providing the data. The providing unit provides related three-dimensional data based on, for example, the user's current location. For example, the providing unit acquires the user's current location from GPS data and identifies the related three-dimensional data. The providing unit can also select an optimal providing method based on the user's geographical location information. For example, the providing unit acquires the user's current location from GPS data and selects an optimal providing method. The providing unit can also provide three-dimensional data of events and places related to the user's current location. For example, the providing unit identifies three-dimensional data of related events and places based on the user's current location. This makes it possible to select an optimal providing method by taking the user's geographical location information into consideration.

[0062] The providing unit can analyze the user's social media activity and suggest a providing method when providing the data. For example, the providing unit can prioritize providing three-dimensional data shared by the user on social media. For example, the providing unit can obtain and provide three-dimensional data shared from the user's social media account. The providing unit can also analyze the user's social media posts and provide related three-dimensional data. For example, the providing unit can analyze the user's posts using text analysis technology to identify related three-dimensional data. The providing unit can also provide related three-dimensional data by referring to the activities of the user's friends on social media. For example, the providing unit can analyze the posts of the user's friends and identify related three-dimensional data. In this way, related three-dimensional data can be provided by analyzing the user's social media activity.

[0063] The providing unit can customize the delivery method based on the user's past feedback when providing the service. For example, the providing unit preferentially provides a delivery method that the user has previously preferred. For example, the providing unit acquires the user's past feedback from a database and identifies the preferred delivery method. The providing unit can also customize the delivery method based on the user's past feedback. For example, the providing unit optimizes the delivery procedure based on the user's feedback. The providing unit can also optimize the delivery procedure by referring to the user's past feedback. For example, the providing unit improves the delivery method based on the user's feedback. In this way, the delivery method can be optimized by reflecting the user's past feedback.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The reception unit can analyze the user's past photo input history and select the optimal input method. For example, it can preferentially suggest input methods (such as voice and text) that the user has frequently used in the past. For example, the reception unit can retrieve the user's past input history from a database and identify frequently used input methods. The reception unit can also suggest the optimal input method based on the types of photos the user has uploaded in the past. For example, the reception unit can analyze metadata of photos the user has uploaded in the past and select the optimal input method. The reception unit can also predict and suggest an input method to be used in a specific time period from the user's past input history. For example, the reception unit can analyze the user's past input history in chronological order and identify the input method to be used in a specific time period. In this way, the optimal input method can be suggested by analyzing the user's past photo input history.

[0066] When inputting photos, the reception unit can filter the photos based on the user's current project or area of ​​interest. For example, the reception unit can filter the photos so that only photos related to the user's current project are input. For example, the reception unit can acquire the user's project information from a database and identify related photos. The reception unit can also preferentially input related photos based on the user's area of ​​interest. For example, the reception unit can acquire the user's area of ​​interest from profile information and identify related photos. The reception unit can also filter related photos by referring to the user's past project history. For example, the reception unit can acquire the user's past project history from a database and identify related photos. In this way, by filtering photos based on the user's current project or area of ​​interest, highly relevant photos can be preferentially input.

[0067] During analysis, the analysis unit can adjust the accuracy of the analysis based on the importance of the photo. For example, the analysis unit performs a detailed analysis on important photos. For example, the analysis unit performs a detailed analysis based on the importance specified by the user. The analysis unit can also perform a concise analysis on general photos. For example, the analysis unit performs a concise analysis based on the importance specified by the user. The analysis unit can also adjust the level of detail of the analysis based on the importance specified by the user. For example, the analysis unit adjusts the level of detail of the analysis based on the importance specified by the user. In this way, by adjusting the level of detail of the analysis based on the importance of the photo, a detailed analysis can be performed on important photos.

[0068] During generation, the generation unit can apply different 3D modeling techniques based on the category of the photo. For example, the generation unit applies a technique for generating a detailed 3D model of a face to a portrait photo. For example, the generation unit uses facial recognition technology to reproduce the face in detail in the portrait photo. The generation unit can also apply a technique for generating a wide-area 3D model to a landscape photo. For example, the generation unit applies a technique for reproducing a wide area of ​​the landscape photo. The generation unit can also apply a technique for reproducing the building structure in detail to a building photo. For example, the generation unit applies a technique for reproducing the building structure in detail. In this way, by applying different 3D modeling techniques depending on the category of the photo, a more accurate 3D model can be generated.

[0069] During control, the control unit can select an appropriate control method based on the user's past VR experience history. For example, the control unit selects the optimal control method based on the user's past preferred VR experiences. For example, the control unit retrieves the user's past VR experience history from a database and identifies the preferred control method. The control unit can also customize the control method by referring to the user's past VR experience history. For example, the control unit optimizes the control method based on the user's past feedback. The control unit can also adjust the control method by reflecting the user's past feedback in real time. For example, the control unit improves the control method based on the user's feedback. In this way, the optimal control method can be selected by referring to the user's past VR experience history.

[0070] The providing unit can select an appropriate providing method based on the user's geographical location information at the time of providing the data. For example, the providing unit provides related three-dimensional data based on the user's current location. For example, the providing unit acquires the user's current location from GPS data and identifies related three-dimensional data. The providing unit can also select an optimal providing method based on the user's geographical location information. For example, the providing unit acquires the user's current location from GPS data and selects an optimal providing method. The providing unit can also provide three-dimensional data of events and places related to the user's current location. For example, the providing unit identifies three-dimensional data of related events and places based on the user's current location. This makes it possible to select an optimal providing method by taking the user's geographical location information into consideration.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The reception unit accepts the input of a photo from the user. The user can select any photo and upload it to the system. For example, any photo can be input, such as a family photo or a landscape photo from a travel destination. Step 2: The analysis unit uses AI to analyze the photo received by the reception unit and generate 3D data. The analysis unit uses, for example, deep learning to analyze each element of the photo and generate three-dimensional data. Step 3: The generator generates a 3D model based on the 3D data generated by the analyzer using a 3D modeling technique, such as polygon modeling or sculpting. Step 4: The control unit controls a VR device based on the 3D model generated by the generation unit. The control unit controls a device such as VR goggles or VR gloves to provide the user with three-dimensional data. Step 5: The providing unit provides the user with three-dimensional data through the VR device controlled by the control unit. The providing unit provides, for example, an interface that allows the user to enter the three-dimensional world using the VR device.

[0073] (Example 2) A system according to an embodiment of the present invention analyzes photos using AI and converts them into three-dimensional data. In this system, a user inputs a photo, and the AI ​​analyzes the photo and generates three-dimensional data. The generated three-dimensional data allows users to immerse themselves in the world of the photo using devices such as VR. Users can re-experience the generated three-dimensional world and experience a time-travel experience to the era or place in which the photo was captured. For example, a user inputs a photo. Any photo can be input, such as a family photo or a landscape photo from a travel destination. Next, the AI ​​analyzes the input photo. The AI ​​analyzes each element of the photo in detail to generate three-dimensional data. For example, it analyzes the relative positions and depth of people, buildings, and scenery in the photo and creates a three-dimensional model based on that analysis. The generated three-dimensional data allows users to immerse themselves in the world of the photo using devices such as VR. By wearing VR goggles, users can experience the sensation of being inside the photo. For example, if the photo is a family photo, they can feel as if they are actually there as a family member. Furthermore, users can re-experience the generated three-dimensional world. For example, if the photo is a landscape photo from a travel destination, they can walk around the location as if they were actually visiting the place. This allows users to experience a time slip to the era and place of the photograph. This allows the system to enable users to rediscover the value of photographs from a new perspective. For example, by recreating old family photos in 3D, users can feel the memories of those times more vividly. Furthermore, by recreating historical photographs in 3D, users can gain a deeper understanding of the atmosphere and events of that era.

[0074] A photo analysis system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a control unit, and a provision unit. The reception unit accepts photo input from a user. The user can select any photo and upload it to the system. Any photo can be input, such as a family photo or a landscape photo from a travel destination. The analysis unit uses AI to analyze the photo accepted by the reception unit and generate 3D data. The analysis unit analyzes each element of the photo using, for example, deep learning to generate three-dimensional data. The generation unit uses 3D modeling technology to generate a 3D model based on the 3D data generated by the analysis unit. The generation unit generates the 3D model using, for example, polygon modeling or sculpting technology. The control unit controls a VR device based on the 3D model generated by the generation unit. The control unit controls a device such as VR goggles or VR gloves to provide the user with three-dimensional data. The provision unit provides the three-dimensional data to the user through a VR device controlled by the control unit. The provision unit provides, for example, an interface that allows the user to immerse themselves in a three-dimensional world using the VR device. As a result, the photo analysis system according to the embodiment allows users to input photos, generate three-dimensional data, and experience the data through a VR device, allowing the system to rediscover the value of photos from a new perspective.

[0075] The analysis unit can analyze each element of a photo using machine learning and generate 3D data. The analysis unit can analyze each element of a photo using, for example, deep learning and generate three-dimensional data. For example, the analysis unit can analyze the relative positions and depth of people, buildings, scenery, etc. in the photo and create a three-dimensional model based on that. The analysis unit can also analyze each element of a photo using a support vector machine. For example, the analysis unit can detect edges and corners in the photo and generate three-dimensional data based on that. The analysis unit can also analyze each element of a photo using a convolutional neural network. For example, the analysis unit can extract features of the photo and generate three-dimensional data based on that. In this way, by using deep learning, each element of a photo can be analyzed in detail and three-dimensional data can be generated.

[0076] The generation unit can generate a 3D model based on the 3D data using 3D modeling technology. The generation unit can generate a 3D model based on the 3D data using, for example, polygon modeling. For example, the generation unit can divide the 3D data into polygons and generate a 3D model based on the polygons. The generation unit can also generate a 3D model based on the 3D data using sculpting technology. For example, the generation unit can sculpt details based on the 3D data to generate a detailed 3D model. The generation unit can also generate a 3D model based on the 3D data using voxel-based modeling technology. For example, the generation unit can convert the 3D data into voxels and generate a 3D model based on the voxels. As a result, using 3D modeling technology can generate a highly accurate 3D model based on three-dimensional data.

[0077] The control unit can provide 3D data to a user through a VR device. The control unit can provide 3D data to a user through, for example, VR goggles. For example, the control unit can display three-dimensional data to a user wearing the VR goggles. The control unit can also provide 3D data to a user through VR gloves. For example, the control unit can provide an interface for a user wearing the VR gloves to operate the three-dimensional data. The control unit can also provide 3D data to a user through a VR chair. For example, the control unit can provide an environment for a user sitting in a VR chair to experience three-dimensional data. By providing three-dimensional data to a user through a VR device, the user can experience immersion in the world of a photograph.

[0078] The providing unit can provide an interface for the user to enter a 3D world using the VR device. The providing unit, for example, provides a user interface. For example, the providing unit provides a simple operation method for the user to enter a 3D world using the VR device. The providing unit can also provide a graphical user interface. For example, the providing unit provides an interface that the user can operate visually. The providing unit can also provide an audio interface. For example, the providing unit provides an interface that the user can operate by audio. In this way, by providing an interface for the user to enter a three-dimensional world using the VR device, the user can easily experience the three-dimensional world.

[0079] The reception unit can estimate the user's emotions and adjust the timing of photo input based on the estimated user emotions. For example, if the user is excited, the reception unit displays an interface that prompts the user to immediately input a photo. For example, the reception unit captures the user's facial expression with a camera and detects the excited state using an emotion estimation algorithm. Furthermore, if the user is relaxed, the reception unit can display an interface that prompts the user to input a photo at a slower pace. For example, the reception unit records the user's voice and detects the relaxed state using voice analysis technology. Furthermore, if the user is feeling stressed, the reception unit can provide an interface that allows the user to input a photo with simple operations. For example, the reception unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and detects the user's stress level using an emotion estimation algorithm. This allows the user to input a photo at a more appropriate timing by adjusting the timing of photo input according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The reception unit can analyze the user's past photo input history and select the optimal input method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. For example, the reception unit retrieves the user's past input history from a database and identifies the input method that was frequently used. The reception unit can also suggest the optimal input method based on the types of photos the user has uploaded in the past. For example, the reception unit analyzes metadata of photos the user has uploaded in the past and selects the optimal input method. The reception unit can also predict and suggest the input method that will be used in 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 identifies the input method that will be used in a specific time period. In this way, the optimal input method can be suggested by analyzing the user's past photo input history.

[0081] The reception unit can filter the photos based on the user's current project or area of ​​interest when the photos are input. For example, the reception unit filters the photos so that the user only inputs photos related to the project currently in progress. For example, the reception unit acquires the user's project information from a database and identifies related photos. The reception unit can also preferentially input related photos based on the user's area of ​​interest. For example, the reception unit acquires the user's area of ​​interest from profile information and identifies related photos. The reception unit can also filter related photos by referring to the user's past project history. For example, the reception unit acquires the user's past project history from a database and identifies related photos. In this way, by filtering photos based on the user's current project or area of ​​interest, highly relevant photos can be preferentially input.

[0082] When a user inputs a photo, the reception unit can select the optimal input means depending on the user's input method (voice, text, image, etc.). For example, if the user selects voice input, the reception unit inputs a description of the photo using voice recognition technology. For example, the reception unit converts the user's voice into text using voice recognition software and saves it as a description of the photo. Furthermore, if the user selects text input, the reception unit can also provide a simple text input interface. For example, the reception unit provides an interface for the user to input a description of the photo using a keyboard. Furthermore, if the user selects image input, the reception unit can also analyze the content of the photo using image recognition technology. For example, the reception unit analyzes the content of the photo using image recognition software and saves it as a description of the photo. In this way, an interface that is easy for the user to use can be provided by selecting the optimal input means depending on the user's input method.

[0083] The reception unit can estimate the user's emotions and determine the priority of photos to be input based on the estimated user emotions. For example, if the user is excited, the reception unit prioritizes inputting the most recent photos. For example, the reception unit may capture the user's facial expression with a camera and detect the excited state using an emotion estimation algorithm. Furthermore, if the user is relaxed, the reception unit may prioritize inputting older photos. For example, the reception unit may record the user's voice and detect the relaxed state using voice analysis technology. Furthermore, if the user is stressed, the reception unit may prioritize inputting photos that are easy to input. For example, the reception unit may collect the user's biometric data (heart rate and electrodermal activity) using a sensor and detect the user's stress state using an emotion estimation algorithm. This allows the priority of photos to be determined according to the user's emotions, thereby enabling more appropriate photos to be input preferentially. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] When inputting photos, the reception unit can prioritize inputting photos with high relevance based on the user's geographical location information. For example, the reception unit prioritizes inputting photos taken in locations close to the user's current location. For example, the reception unit acquires the user's current location from GPS data and identifies photos taken in the vicinity. The reception unit can also prioritize inputting photos related to locations the user has visited in the past. For example, the reception unit acquires the user's past location information from a database and identifies related photos. The reception unit can also prioritize inputting photos of events or places related to the user's current location. For example, the reception unit identifies photos of related events or places based on the user's current location. In this way, highly relevant photos can be prioritized by taking the user's geographical location information into consideration.

[0085] When inputting photos, the reception unit can analyze the user's social media activity and input related photos. The reception unit, for example, preferentially inputs photos shared by the user on social media. For example, the reception unit obtains and inputs photos shared from the user's social media account. The reception unit can also analyze the content posted by the user on social media and input related photos. For example, the reception unit can analyze the content posted by the user using text analysis technology and identify related photos. The reception unit can also input related photos with reference to the activity of the user's friends on social media. For example, the reception unit can analyze the content posted by the user's friends and identify related photos. In this way, the reception unit can preferentially input related photos by analyzing the user's social media activity.

[0086] The reception unit can customize the input method based on the user's past feedback when inputting a photo. For example, the reception unit preferentially provides an input method that the user has previously preferred. For example, the reception unit obtains the user's past feedback from a database and identifies the preferred input method. The reception unit can also customize the input interface based on the user's past feedback. For example, the reception unit adjusts the design and functions of the input interface based on the user's feedback. The reception unit can also optimize the input procedure by referring to the user's past feedback. For example, the reception unit simplifies the input procedure and improves usability based on the user's feedback. In this way, the input method can be optimized by reflecting the user's past feedback.

[0087] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, the analysis unit captures the user's facial expressions with a camera and detects the relaxed state using an emotion estimation algorithm. The analysis unit can also provide concise analysis results if the user is in a hurry. For example, the analysis unit records the user's voice and detects the state of hurry using voice analysis technology. The analysis unit can also provide visually appealing analysis results if the user is excited. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and detects the excited state using an emotion estimation algorithm. This allows the analysis unit to adjust the presentation method of the analysis according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] During analysis, the analysis unit can adjust the accuracy of the analysis based on the importance of the photo. The analysis unit, for example, performs a detailed analysis on important photos. For example, the analysis unit performs a detailed analysis based on the importance specified by the user. The analysis unit can also perform a concise analysis on general photos. For example, the analysis unit performs a concise analysis based on the importance specified by the user. The analysis unit can also adjust the level of detail of the analysis based on the importance specified by the user. For example, the analysis unit adjusts the level of detail of the analysis based on the importance specified by the user. In this way, by adjusting the level of detail of the analysis based on the importance of the photo, a detailed analysis can be performed on important photos.

[0089] During analysis, the analysis unit can apply different analysis algorithms based on the category of the photo. For example, the analysis unit applies a face recognition algorithm to photos of people. For example, the analysis unit uses the face recognition algorithm to detect faces in photos of people and perform analysis. The analysis unit can also apply a landscape analysis algorithm to photos of landscapes. For example, the analysis unit uses a landscape analysis algorithm to detect features in photos of landscapes and perform analysis. The analysis unit can also apply a building analysis algorithm to photos of buildings. For example, the analysis unit uses a building analysis algorithm to detect the structure of photos of buildings and perform analysis. This allows for more accurate analysis by applying different analysis algorithms depending on the category of the photo.

[0090] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. For example, the analysis unit retrieves the user's past analysis results from a database and optimizes the analysis algorithm. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and suggests an optimal analysis method. The analysis unit can also improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0091] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit performs a short and to-the-point analysis. For example, the analysis unit captures the user's facial expressions with a camera and uses an emotion estimation algorithm to detect a state of hurry. The analysis unit can also perform a detailed analysis if the user is relaxed. For example, the analysis unit records the user's voice and detects a relaxed state using voice analysis technology. The analysis unit can also perform a visually appealing analysis if the user is excited. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and detects an excited state using an emotion estimation algorithm. This allows the analysis unit to adjust the length of the analysis according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] During analysis, the analysis unit can determine the order of analysis based on the time the photos were taken. The analysis unit, for example, prioritizes analyzing photos taken recently. For example, the analysis unit obtains the time of photo shooting from the metadata of the photos and identifies the photos taken recently. The analysis unit can also prioritize analyzing photos taken at a specific time. For example, the analysis unit identifies photos based on the time of photo shooting specified by the user. The analysis unit can also determine the priority of analysis based on the time of photo shooting specified by the user. For example, the analysis unit adjusts the order of analysis based on the time of photo shooting specified by the user. In this way, by determining the priority of analysis based on the time of photo shooting, more important photos can be analyzed preferentially.

[0093] During analysis, the analysis unit can adjust the priority of analysis based on the relevance of the photos. For example, the analysis unit prioritizes analysis of highly relevant photos. For example, the analysis unit analyzes the content of the photos and identifies highly relevant photos. The analysis unit can also adjust the order of analysis based on the relevance specified by the user. For example, the analysis unit adjusts the order of analysis based on the relevance specified by the user. The analysis unit can also adjust the order of analysis based on the content of the photos. For example, the analysis unit analyzes the content of the photos and identifies highly relevant photos. In this way, by adjusting the order of analysis based on the relevance of the photos, highly relevant photos can be analyzed preferentially.

[0094] During analysis, the analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise. For example, if the user has specialized knowledge, the analysis unit performs an analysis that makes extensive use of technical terms. For example, the analysis unit obtains the expertise level from the user's profile information and performs an analysis that makes extensive use of technical terms. Furthermore, if the user has general knowledge, the analysis unit can also perform a concise analysis. For example, the analysis unit obtains the expertise level from the user's profile information and performs a concise analysis. Furthermore, the analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise. For example, the analysis unit obtains the expertise level from the user's profile information and adjusts the use of technical terms. In this way, by adjusting the use of technical terms in the analysis based on the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0095] The generation unit can estimate the user's emotions and adjust the generation method of the 3D model based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a detailed 3D model. For example, the generation unit captures the user's facial expressions with a camera and detects the relaxed state using an emotion estimation algorithm. Also, if the user is in a hurry, the generation unit can generate a simple 3D model. For example, the generation unit records the user's voice and detects the state of hurry using voice analysis technology. Also, if the user is excited, the generation unit can generate a visually appealing 3D model. For example, the generation unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and detects the excited state using an emotion estimation algorithm. This allows the generation of a more appropriate 3D model by adjusting the generation method of the 3D model according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0096] The generation unit can adjust the accuracy of the 3D model based on the importance of the photo during generation. For example, the generation unit generates a detailed 3D model for an important photo. For example, the generation unit generates a detailed 3D model based on the importance specified by the user. The generation unit can also generate a concise 3D model for an ordinary photo. For example, the generation unit generates a concise 3D model based on the importance specified by the user. The generation unit can also adjust the level of detail of the 3D model based on the importance specified by the user. For example, the generation unit adjusts the level of detail of the 3D model based on the importance specified by the user. In this way, by adjusting the level of detail of the 3D model based on the importance of the photo, a detailed 3D model can be generated for an important photo.

[0097] The generation unit can apply different 3D modeling techniques based on the photo category during generation. For example, the generation unit applies a technique for generating a detailed 3D model of a face to a portrait photo. For example, the generation unit uses face recognition technology to reproduce the face in detail in the portrait photo. The generation unit can also apply a technique for generating a wide-area 3D model to a landscape photo. For example, the generation unit applies a technique for reproducing a wide area of ​​the landscape photo. The generation unit can also apply a technique for reproducing the building structure in detail to a building photo. For example, the generation unit applies a technique for reproducing the building structure in detail. In this way, by applying different 3D modeling techniques depending on the photo category, a more accurate 3D model can be generated.

[0098] During generation, the generation unit can improve the accuracy of the 3D model based on the user's past generation results. The generation unit, for example, adjusts the 3D modeling technique based on the user's past generation results. For example, the generation unit retrieves the user's past generation results from a database and optimizes the 3D modeling technique. The generation unit can also improve the accuracy of the 3D model by referring to the user's past generation results. For example, the generation unit analyzes the user's past generation results and suggests an optimal 3D modeling technique. The generation unit can also improve the accuracy of the 3D model based on the user's past generation results. For example, the generation unit adjusts the 3D modeling technique based on the user's past generation results. In this way, the accuracy of the 3D model can be improved by referring to the user's past generation results.

[0099] The generation unit can estimate the user's emotions and adjust the length of the 3D model based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates a short, concise 3D model. For example, the generation unit captures the user's facial expressions with a camera and detects the user's state of hurry using an emotion estimation algorithm. The generation unit can also generate a detailed 3D model if the user is relaxed. For example, the generation unit can record the user's voice and detect the user's state of relaxation using voice analysis technology. The generation unit can also generate a visually appealing 3D model if the user is excited. For example, the generation unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and detect the user's state of excitement using an emotion estimation algorithm. This allows the generation of a more appropriate 3D model by adjusting the length of the 3D model according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0100] During generation, the generation unit can determine the order of 3D models based on the dates when the photos were taken. For example, the generation unit prioritizes creating 3D models of recently taken photos. For example, the generation unit obtains the date of taking the photos from the metadata of the photos and identifies the most recently taken photos. The generation unit can also prioritize creating 3D models of photos taken at a specific time. For example, the generation unit identifies photos based on the date of taking the photos specified by the user. The generation unit can also determine the priority of 3D models based on the date of taking the photos specified by the user. For example, the generation unit adjusts the order of 3D models based on the date of taking the photos specified by the user. In this way, by determining the priority of 3D models based on the date of taking the photos, more important photos can be preferentially created as 3D models.

[0101] The generation unit can adjust the order of the 3D models based on the relevance of the photos during generation. For example, the generation unit prioritizes the creation of 3D models from highly relevant photos. For example, the generation unit analyzes the content of the photos and identifies highly relevant photos. The generation unit can also adjust the order of the 3D models based on the relevance specified by the user. For example, the generation unit adjusts the order of the 3D models based on the relevance specified by the user. The generation unit can also adjust the order of the 3D models based on the content of the photos. For example, the generation unit analyzes the content of the photos and identifies highly relevant photos. In this way, by adjusting the order of the 3D models based on the relevance of the photos, highly relevant photos can be prioritized as 3D models.

[0102] The generation unit can adjust the use of technical terminology in the 3D model based on the user's level of expertise during generation. For example, if the user has specialized knowledge, the generation unit generates a 3D model that uses a lot of technical terminology. For example, the generation unit obtains the expertise level from the user's profile information and generates a 3D model that uses a lot of technical terminology. The generation unit can also generate a concise 3D model if the user has general knowledge. For example, the generation unit obtains the expertise level from the user's profile information and generates a concise 3D model. The generation unit can also adjust the use of technical terminology in the 3D model based on the user's level of expertise. For example, the generation unit obtains the expertise level from the user's profile information and adjusts the use of technical terminology. This makes it possible to provide a 3D model that is easy for the user to understand by adjusting the use of technical terminology in the 3D model based on the user's level of expertise.

[0103] The control unit can estimate the user's emotions and adjust the control method of the VR device based on the estimated user emotions. For example, if the user is relaxed, the control unit can provide a VR experience at a leisurely pace. For example, the control unit can capture the user's facial expressions with a camera and detect the relaxed state using an emotion estimation algorithm. Also, if the user is in a hurry, the control unit can provide a VR experience with quick operations. For example, the control unit can record the user's voice and detect the user's state of hurry using voice analysis technology. Also, if the user is excited, the control unit can provide a visually stimulating VR experience. For example, the control unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and detect the user's excited state using an emotion estimation algorithm. This allows the control unit to adjust the control method of the VR device according to the user's emotions, thereby providing a more appropriate VR experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] During control, the control unit can select an appropriate control method based on the user's past VR experience history. The control unit selects the optimal control method based on, for example, the user's preferred VR experiences in the past. For example, the control unit retrieves the user's past VR experience history from a database and identifies the preferred control method. The control unit can also customize the control method by referring to the user's past VR experience history. For example, the control unit optimizes the control method based on the user's past feedback. The control unit can also adjust the control method by reflecting the user's past feedback in real time. For example, the control unit improves the control method based on the user's feedback. In this way, the optimal control method can be selected by referring to the user's past VR experience history.

[0105] During control, the control unit can customize the control content based on the user's current task. For example, if the user wants to relax, the control unit provides a relaxing VR experience. For example, the control unit obtains the user's current task from the user's profile information and provides a relaxing VR experience. Furthermore, if the user wants to learn, the control unit can provide an educational VR experience. For example, the control unit obtains the user's current task from the user's profile information and provides an educational VR experience. Furthermore, if the user wants to be entertained, the control unit can provide a highly entertaining VR experience. For example, the control unit obtains the user's current task from the user's profile information and provides a highly entertaining VR experience. In this way, a more appropriate VR experience can be provided by customizing the control content according to the user's current task.

[0106] During control, the control unit can improve the control method based on user feedback. The control unit improves the control method of the VR experience based on, for example, user feedback. For example, the control unit acquires user feedback from a database and optimizes the control method. The control unit can also optimize the control method by referring to the user's past feedback. For example, the control unit optimizes the control procedure based on the user's past feedback. The control unit can also reflect the user's feedback in real time and adjust the control method. For example, the control unit improves the control method based on the user's feedback. In this way, the control method can be optimized by reflecting the user's feedback.

[0107] The control unit can estimate the user's emotions and determine the control priority of the VR device based on the estimated user emotions. For example, if the user is relaxed, the control unit can prioritize providing a relaxing VR experience. For example, the control unit can capture the user's facial expressions with a camera and detect the relaxed state using an emotion estimation algorithm. Furthermore, if the user is in a hurry, the control unit can prioritize providing a VR experience with quick operations. For example, the control unit can record the user's voice and detect the user's state of hurry using voice analysis technology. Furthermore, if the user is excited, the control unit can prioritize providing a visually stimulating VR experience. For example, the control unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and detect the user's excited state using an emotion estimation algorithm. This allows the control unit to determine the control priority of the VR device based on the user's emotions, thereby providing a more appropriate VR experience. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0108] During control, the control unit can select an appropriate control method based on the user's geographical location information. The control unit provides a related VR experience based on, for example, the user's current location. For example, the control unit acquires the user's current location from GPS data and identifies a related VR experience. The control unit can also select an optimal control method based on the user's geographical location information. For example, the control unit acquires the user's current location from GPS data and selects an optimal control method. The control unit can also provide a VR experience of an event or place related to the user's current location. For example, the control unit identifies a VR experience of a related event or place based on the user's current location. This makes it possible to select an optimal control method by taking the user's geographical location information into consideration.

[0109] During control, the control unit can analyze the user's social media activities and suggest a control method. For example, the control unit can prioritize providing VR experiences shared by the user on social media. For example, the control unit can obtain and provide VR experiences shared from the user's social media account. The control unit can also analyze the user's social media posts to provide related VR experiences. For example, the control unit can analyze the user's posts using text analysis technology to identify related VR experiences. The control unit can also provide related VR experiences by referring to the activities of the user's friends on social media. For example, the control unit can analyze the user's friends' posts to identify related VR experiences. In this way, related VR experiences can be provided by analyzing the user's social media activities.

[0110] During control, the control unit can customize the control method based on the user's past feedback. For example, the control unit preferentially provides a control method that the user has previously preferred. For example, the control unit obtains the user's past feedback from a database and identifies the preferred control method. The control unit can also customize the control method based on the user's past feedback. For example, the control unit optimizes the control procedure based on the user's feedback. The control unit can also optimize the control procedure by referring to the user's past feedback. For example, the control unit improves the control method based on the user's feedback. In this way, the control method can be optimized by reflecting the user's past feedback.

[0111] The providing unit can estimate the user's emotions and adjust the method of providing 3D data based on the estimated user emotions. For example, if the user is relaxed, the providing unit provides three-dimensional data at a leisurely pace. For example, the providing unit can capture the user's facial expressions with a camera and detect the user's relaxed state using an emotion estimation algorithm. Furthermore, if the user is in a hurry, the providing unit can provide three-dimensional data with quick operations. For example, the providing unit can record the user's voice and detect the user's state of hurry using voice analysis technology. Furthermore, if the user is excited, the providing unit can provide visually stimulating three-dimensional data. For example, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and detect the user's excited state using an emotion estimation algorithm. This allows for more appropriate data provision by adjusting the method of providing three-dimensional data according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0112] At the time of provision, the providing unit can select an appropriate provision method based on the user's past experience history. For example, the providing unit preferentially provides a provision method that the user has previously preferred. For example, the providing unit retrieves the user's past experience history from a database and identifies the preferred provision method. The providing unit can also customize the provision method by referring to the user's past experience history. For example, the providing unit optimizes the provision method based on the user's past feedback. The providing unit can also adjust the provision method by reflecting the user's past feedback in real time. For example, the providing unit improves the provision method based on the user's feedback. In this way, the optimal provision method can be selected by referring to the user's past experience history.

[0113] The providing unit can customize the provided content based on the user's current task when providing the data. For example, if the user wants to relax, the providing unit provides relaxing three-dimensional data. For example, the providing unit acquires the user's current task from the user's profile information and provides relaxing three-dimensional data. Furthermore, if the user wants to learn, the providing unit can also provide educational three-dimensional data. For example, the providing unit acquires the user's current task from the user's profile information and provides educational three-dimensional data. Furthermore, if the user wants entertainment, the providing unit can also provide entertainment-oriented three-dimensional data. For example, the providing unit acquires the user's current task from the user's profile information and provides entertainment-oriented three-dimensional data. This makes it possible to provide more appropriate data by customizing the provided content according to the user's current task.

[0114] The providing unit can improve the providing method based on user feedback at the time of providing. The providing unit improves the method of providing three-dimensional data based on user feedback, for example. For example, the providing unit acquires user feedback from a database and optimizes the providing method. The providing unit can also optimize the providing method by referring to the user's past feedback. For example, the providing unit optimizes the providing procedure based on the user's past feedback. The providing unit can also reflect the user's feedback in real time and adjust the providing method. For example, the providing unit improves the providing method based on the user's feedback. In this way, the providing method can be optimized by reflecting the user's feedback.

[0115] The providing unit can estimate the user's emotions and determine the priority of providing 3D data based on the estimated user emotions. For example, if the user is relaxed, the providing unit can prioritize providing relaxing three-dimensional data. For example, the providing unit can capture the user's facial expressions with a camera and detect the relaxed state using an emotion estimation algorithm. Furthermore, if the user is in a hurry, the providing unit can prioritize providing three-dimensional data with quick operations. For example, the providing unit can record the user's voice and detect the user's state of hurry using voice analysis technology. Furthermore, if the user is excited, the providing unit can prioritize providing visually stimulating three-dimensional data. For example, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and detect the user's excited state using an emotion estimation algorithm. This allows for more appropriate data provision by determining the priority of providing three-dimensional data according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0116] The providing unit can select an appropriate providing method based on the user's geographical location information at the time of providing the data. The providing unit provides related three-dimensional data based on, for example, the user's current location. For example, the providing unit acquires the user's current location from GPS data and identifies the related three-dimensional data. The providing unit can also select an optimal providing method based on the user's geographical location information. For example, the providing unit acquires the user's current location from GPS data and selects an optimal providing method. The providing unit can also provide three-dimensional data of events and places related to the user's current location. For example, the providing unit identifies three-dimensional data of related events and places based on the user's current location. This makes it possible to select an optimal providing method by taking the user's geographical location information into consideration.

[0117] The providing unit can analyze the user's social media activity and suggest a providing method when providing the data. For example, the providing unit can prioritize providing three-dimensional data shared by the user on social media. For example, the providing unit can obtain and provide three-dimensional data shared from the user's social media account. The providing unit can also analyze the user's social media posts and provide related three-dimensional data. For example, the providing unit can analyze the user's posts using text analysis technology to identify related three-dimensional data. The providing unit can also provide related three-dimensional data by referring to the activities of the user's friends on social media. For example, the providing unit can analyze the posts of the user's friends and identify related three-dimensional data. In this way, related three-dimensional data can be provided by analyzing the user's social media activity.

[0118] The providing unit can customize the delivery method based on the user's past feedback when providing the service. For example, the providing unit preferentially provides a delivery method that the user has previously preferred. For example, the providing unit acquires the user's past feedback from a database and identifies the preferred delivery method. The providing unit can also customize the delivery method based on the user's past feedback. For example, the providing unit optimizes the delivery procedure based on the user's feedback. The providing unit can also optimize the delivery procedure by referring to the user's past feedback. For example, the providing unit improves the delivery method based on the user's feedback. In this way, the delivery method can be optimized by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, control unit, and provision 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 reception device 38 of the smart device 14 and receives a photo input from a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the photo using AI to generate 3D data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a 3D model using 3D modeling technology. The control unit is realized, for example, by the control unit 46A of the smart device 14 and controls the VR device based on the generated 3D model. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides three-dimensional data to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, generation unit, control unit, and provision 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 microphone 238 of the smart glasses 214 and receives a photo input from a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the photo using AI to generate 3D data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a 3D model using 3D modeling technology. The control unit is realized, for example, by the control unit 46A of the smart glasses 214 and controls the VR device based on the generated 3D model. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides three-dimensional data to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, control unit, and provision 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 microphone 238 of the headset-type terminal 314 and receives a photo input from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the photo using AI to generate 3D data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a 3D model using 3D modeling technology. The control unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and controls the VR device based on the generated 3D model. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314 and provides three-dimensional data to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, control unit, and provision 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 microphone 238 of the robot 414 and receives a photo input from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the photo using AI to generate 3D data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a 3D model using 3D modeling technology. The control unit is realized, for example, by the control unit 46A of the robot 414 and controls the VR device based on the generated 3D model. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides three-dimensional data to the user.

[0119] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0120] The reception unit can analyze the user's past photo input history and select the optimal input method. For example, it can preferentially suggest input methods (such as voice and text) that the user has frequently used in the past. For example, the reception unit can retrieve the user's past input history from a database and identify frequently used input methods. The reception unit can also suggest the optimal input method based on the types of photos the user has uploaded in the past. For example, the reception unit can analyze metadata of photos the user has uploaded in the past and select the optimal input method. The reception unit can also predict and suggest an input method to be used in a specific time period from the user's past input history. For example, the reception unit can analyze the user's past input history in chronological order and identify the input method to be used in a specific time period. In this way, the optimal input method can be suggested by analyzing the user's past photo input history.

[0121] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, the analysis unit can capture the user's facial expressions with a camera and detect a relaxed state using an emotion estimation algorithm. The analysis unit can also provide concise analysis results if the user is in a hurry. For example, the analysis unit can record the user's voice and detect a state of hurry using voice analysis technology. The analysis unit can also provide visually appealing analysis results if the user is excited. For example, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and detect an excited state using an emotion estimation algorithm. This allows the analysis unit to adjust the way the analysis is presented based on the user's emotions, thereby providing more appropriate analysis results.

[0122] The generation unit can estimate the user's emotions and adjust the generation method of the 3D model based on the estimated user's emotions. For example, if the user is relaxed, a detailed 3D model is generated. For example, the generation unit captures the user's facial expressions with a camera and detects the relaxed state using an emotion estimation algorithm. Alternatively, if the user is in a hurry, the generation unit can generate a simple 3D model. For example, the generation unit records the user's voice and detects the state of hurry using voice analysis technology. Alternatively, if the user is excited, the generation unit can generate a visually appealing 3D model. For example, the generation unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and detects the excited state using an emotion estimation algorithm. This allows the generation of a more appropriate 3D model by adjusting the generation method of the 3D model according to the user's emotions.

[0123] The control unit can estimate the user's emotions and adjust the control method of the VR device based on the estimated user's emotions. For example, if the user is relaxed, the control unit can provide a VR experience at a leisurely pace. For example, the control unit can capture the user's facial expressions with a camera and detect a relaxed state using an emotion estimation algorithm. Alternatively, if the user is in a hurry, the control unit can provide a VR experience with quick operations. For example, the control unit can record the user's voice and detect a state of hurry using voice analysis technology. Alternatively, if the user is excited, the control unit can provide a visually stimulating VR experience. For example, the control unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and detect an excited state using an emotion estimation algorithm. This allows the control unit to adjust the control method of the VR device according to the user's emotions, thereby providing a more appropriate VR experience.

[0124] The providing unit can estimate the user's emotions and adjust the method of providing 3D data based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide three-dimensional data at a leisurely pace. For example, the providing unit can capture the user's facial expressions with a camera and detect the user's relaxed state using an emotion estimation algorithm. Furthermore, if the user is in a hurry, the providing unit can provide three-dimensional data with quick operations. For example, the providing unit can record the user's voice and detect the user's state of hurry using voice analysis technology. Furthermore, if the user is excited, the providing unit can provide visually stimulating three-dimensional data. For example, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and detect the user's excited state using an emotion estimation algorithm. This allows the providing unit to adjust the method of providing three-dimensional data according to the user's emotions, thereby providing more appropriate data.

[0125] When inputting photos, the reception unit can filter the photos based on the user's current project or area of ​​interest. For example, the reception unit can filter the photos so that only photos related to the user's current project are input. For example, the reception unit can acquire the user's project information from a database and identify related photos. The reception unit can also preferentially input related photos based on the user's area of ​​interest. For example, the reception unit can acquire the user's area of ​​interest from profile information and identify related photos. The reception unit can also filter related photos by referring to the user's past project history. For example, the reception unit can acquire the user's past project history from a database and identify related photos. In this way, by filtering photos based on the user's current project or area of ​​interest, highly relevant photos can be preferentially input.

[0126] During analysis, the analysis unit can adjust the accuracy of the analysis based on the importance of the photo. For example, the analysis unit performs a detailed analysis on important photos. For example, the analysis unit performs a detailed analysis based on the importance specified by the user. The analysis unit can also perform a concise analysis on general photos. For example, the analysis unit performs a concise analysis based on the importance specified by the user. The analysis unit can also adjust the level of detail of the analysis based on the importance specified by the user. For example, the analysis unit adjusts the level of detail of the analysis based on the importance specified by the user. In this way, by adjusting the level of detail of the analysis based on the importance of the photo, a detailed analysis can be performed on important photos.

[0127] During generation, the generation unit can apply different 3D modeling techniques based on the category of the photo. For example, the generation unit applies a technique for generating a detailed 3D model of a face to a portrait photo. For example, the generation unit uses facial recognition technology to reproduce the face in detail in the portrait photo. The generation unit can also apply a technique for generating a wide-area 3D model to a landscape photo. For example, the generation unit applies a technique for reproducing a wide area of ​​the landscape photo. The generation unit can also apply a technique for reproducing the building structure in detail to a building photo. For example, the generation unit applies a technique for reproducing the building structure in detail. In this way, by applying different 3D modeling techniques depending on the category of the photo, a more accurate 3D model can be generated.

[0128] During control, the control unit can select an appropriate control method based on the user's past VR experience history. For example, the control unit selects the optimal control method based on the user's past preferred VR experiences. For example, the control unit retrieves the user's past VR experience history from a database and identifies the preferred control method. The control unit can also customize the control method by referring to the user's past VR experience history. For example, the control unit optimizes the control method based on the user's past feedback. The control unit can also adjust the control method by reflecting the user's past feedback in real time. For example, the control unit improves the control method based on the user's feedback. In this way, the optimal control method can be selected by referring to the user's past VR experience history.

[0129] The providing unit can select an appropriate providing method based on the user's geographical location information at the time of providing the data. For example, the providing unit provides related three-dimensional data based on the user's current location. For example, the providing unit acquires the user's current location from GPS data and identifies related three-dimensional data. The providing unit can also select an optimal providing method based on the user's geographical location information. For example, the providing unit acquires the user's current location from GPS data and selects an optimal providing method. The providing unit can also provide three-dimensional data of events and places related to the user's current location. For example, the providing unit identifies three-dimensional data of related events and places based on the user's current location. This makes it possible to select an optimal providing method by taking the user's geographical location information into consideration.

[0130] The processing flow of the second embodiment will be briefly explained below.

[0131] Step 1: The reception unit accepts the input of a photo from the user. The user can select any photo and upload it to the system. For example, any photo can be input, such as a family photo or a landscape photo from a travel destination. Step 2: The analysis unit uses AI to analyze the photo received by the reception unit and generate 3D data. The analysis unit uses, for example, deep learning to analyze each element of the photo and generate three-dimensional data. Step 3: The generator generates a 3D model based on the 3D data generated by the analyzer using a 3D modeling technique, such as polygon modeling or sculpting. Step 4: The control unit controls a VR device based on the 3D model generated by the generation unit. The control unit controls a device such as VR goggles or VR gloves to provide the user with three-dimensional data. Step 5: The providing unit provides the user with three-dimensional data through the VR device controlled by the control unit. The providing unit provides, for example, an interface that allows the user to enter the three-dimensional world using the VR device.

[0132] 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.

[0133] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0137] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0153] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0183] 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.

[0184] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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).

[0189] 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.

[0190] 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."

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0202] 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.

[0203] [Explanation of symbols]

[0204] 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 an input of a photo from a user; an analysis unit that analyzes the photograph received by the reception unit and generates 3D data; a generation unit that generates a 3D model based on the 3D data generated by the analysis unit; a control unit that controls a VR device based on the 3D model generated by the generation unit; a providing unit that provides 3D data to a user through a VR device controlled by the control unit. A system characterized by:

2. The analysis unit Machine learning is used to analyze each element of a photo and generate 3D data.

2. The system of claim 1.

3. The generation unit Generate a 3D model based on 3D data using 3D modeling technology 2. The system of claim 1.

4. The control unit Providing 3D data to users through VR devices 2. The system of claim 1.

5. The providing unit Provides an interface that allows users to immerse themselves in a 3D world using a VR device 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the timing of photo uploads based on the estimated user emotions.

2. The system of claim 1.

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

8. The reception unit As photos are entered, they are filtered based on the user's current project or area of ​​interest 2. The system of claim 1.

Images