System

Abstract

An object of the system according to the embodiment is to generate and customize a 2D model from a 3D travel photography without expertise.SOLUTION: A system includes a photograph input unit, an analysis unit, a generation unit, a customization unit, and an output unit. The photograph inputting section inputs the travel photography of 2D. The analyzing unit analyzes the travel photography of the 2D inputted by the photograph inputting unit. The generation unit generates a 3D model on the basis of a result analyzed by the analysis unit. The customization unit customizes the 3D model generated by the generation unit. The outputting unit outputs the 3D model customized by the customizing unit.SELECTED DRAWING: Figure 1

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] Previous technology had the problem that the process of generating 3D models from 2D travel photos was complex and difficult to execute without specialized knowledge.

[0005] The system according to the embodiment aims to generate and customize 3D models from 2D travel photos without requiring specialized knowledge. [Means for solving the problem]

[0006] A system according to an embodiment includes a photo input unit, an analysis unit, a generation unit, a customization unit, and an output unit. The photo input unit inputs 2D travel photos. The analysis unit analyzes the 2D travel photos input by the photo input unit. The generation unit generates a 3D model based on the results of the analysis by the analysis unit. The customization unit customizes the 3D model generated by the generation unit. The output unit outputs the 3D model customized by the customization unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to generate and customize 3D models from 2D travel photos without any specialized knowledge. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The 3D model generation system according to an embodiment of the present invention automatically analyzes 2D travel photos, generates 3D models, customizes them, and outputs them. This allows the 3D model generation system to instantly generate 3D models from 2D travel photos provided by users, allowing them to create travel mementos and souvenirs.

[0029] A 3D model generation system according to an embodiment includes a photo input unit, an analysis unit, a generation unit, a customization unit, and an output unit. The photo input unit inputs 2D travel photos, such as photos of beautiful beaches or historical buildings taken by a user. The analysis unit analyzes the 2D travel photos input by the photo input unit, such as analyzing the shapes, colors, and textures of objects and scenery in the photos. The generation unit generates a 3D model based on the results of the analysis by the analysis unit. For example, a beach photo generates a 3D model that reproduces the shape of waves and the texture of a sandy beach. The customization unit customizes the 3D model generated by the generation unit, such as by changing the color of the model or emphasizing specific features. The output unit outputs the 3D model customized by the customization unit. For example, the 3D model can be printed as a real souvenir using a 3D printer or saved as digital data for use in virtual reality or augmented reality (VR) applications. This allows the 3D model generation system according to an embodiment to generate, customize, and output a 3D model from 2D travel photos.

[0030] The photo input unit can automatically acquire metadata from photos and generate 3D models based on the metadata. For example, when a user uploads a photo, the system automatically analyzes the photo's metadata and acquires location information and the date and time the photo was taken. For example, the system can identify the location where the photo was taken based on GPS data and use that information to generate a 3D model. This allows the system to generate a more detailed 3D model using the photo's metadata.

[0031] The photo input unit can provide an interactive tool for the user to emphasize a specific part. For example, when the user uploads a photo, the photo input unit provides an interactive tool for emphasizing a specific part. For example, the user can drag with a mouse to select the part to be emphasized. This allows the user to generate a 3D model with the specific part emphasized.

[0032] The photo input unit can provide a function for uploading multiple photos at once and integrating them to generate a single 3D model. For example, the photo input unit can provide a function for a user to upload multiple photos at once and integrate the photos to generate a single 3D model. For example, a 3D model can be created by combining photos taken from different angles. This allows multiple photos to be integrated to generate a single 3D model.

[0033] The photo input unit provides a function that allows a user to add a description by voice when uploading a photo, and the generation of a 3D model can be supplemented based on the description. The photo input unit provides a function that allows a user to add a description by voice when uploading a photo, for example, by providing a voice description of the background of the photo or the situation at the time of shooting. This allows the generation of a 3D model to be supplemented based on the user's voice description.

[0034] The analysis unit can detect the movement and changes of objects in a photo and generate a dynamic 3D model. For example, when the generative AI analyzes a photo, the analysis unit uses an algorithm to detect the movement and changes of objects in the photo. For example, it can detect the movement of waves or trees swaying in the wind. This makes it possible to generate a dynamic 3D model that reflects the movement and changes of objects in the photo.

[0035] The analysis unit can automatically generate photographs from different viewpoints when analyzing a photograph, and create a more detailed 3D model based on the photographs. For example, the analysis unit uses an algorithm to automatically generate photographs from different viewpoints when analyzing a photograph. For example, it can recreate multiple viewpoints from a single photograph. This allows a more detailed 3D model to be generated based on the photographs from different viewpoints.

[0036] The analysis unit can simulate changes in different time periods and seasons when analyzing photos and generate 3D models based on the results. For example, the analysis unit uses an algorithm to simulate changes in different time periods and seasons when analyzing photos. For example, it can convert a daytime photo into a nighttime scene. This allows it to generate 3D models that reflect changes in different time periods and seasons.

[0037] The analysis unit can generate a 3D model based on a theme specified by the user when analyzing the photo. For example, the analysis unit can generate a 3D model based on a theme specified by the user when analyzing the photo. For example, the analysis unit can recreate a fantasy-style landscape. This allows the generation of a 3D model based on a theme specified by the user.

[0038] The customization unit can provide an interactive editing tool that allows the user to manually modify the generated 3D model. For example, the customization unit provides an interactive editing tool that allows the user to manually modify the generated 3D model. For example, the shape of the model can be changed by mouse or touch operation. This allows the user to manually modify the 3D model.

[0039] The customization unit can provide a function of automatically applying a color or texture specified by a user when customizing a 3D model. For example, the customization unit provides a function of automatically applying a color or texture specified by a user when customizing a 3D model. For example, the customization unit applies a color selected by the user to the entire model. This allows the color or texture specified by the user to be automatically applied.

[0040] The customization unit may provide a function for converting the generated 3D model into a different art style. For example, the customization unit may use an algorithm for converting the generated 3D model into a different art style. For example, the customization unit may convert the generated 3D model into a Picasso-esque abstract style. This allows the generated 3D model to be converted into a different art style.

[0041] The customization unit can provide a community function for referring to customization examples created by other users when customizing a 3D model. The customization unit provides, for example, a community function for sharing customization examples created by other users. For example, a user can upload their own customization example. This allows the user to customize a 3D model by referring to the customization examples of other users.

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

[0043] When a user uploads a photo, the photo input unit can automatically recognize specific objects contained in the photo and generate a 3D model based on that object. For example, it can automatically recognize buildings, cars, people, etc. in the photo and generate individual 3D models for each object. This allows users to create 3D models that focus on specific objects. It is also possible for users to input additional information about recognized objects to generate more detailed 3D models.

[0044] The analysis unit analyzes the position of light reflections and shadows in a photograph to generate a more realistic 3D model. For example, it identifies the position of the sun and the direction of the light source and reproduces the position of shadows and light reflections based on that. This allows the generation of a 3D model that faithfully reproduces the lighting conditions in the photograph. It is also possible to generate a 3D model that reflects day and night and seasonal changes by simulating different lighting conditions.

[0045] The generator can generate a 3D model based on a theme specified by the user. For example, if the user specifies a theme such as "futuristic city" or "medieval castle," the generator generates a 3D model incorporating design elements based on that theme. This allows the user to create a 3D model based on a theme that suits their preferences. In addition, by providing customization options based on the theme, more detailed adjustments can be made.

[0046] The customization section can provide interactive editing tools that allow users to manually modify the generated 3D model. For example, users can change the shape of the model or add specific parts using mouse or touch operations. This allows users to freely customize the 3D model to their liking. The editing tools also include a function to preview changes in real time, allowing users to instantly see the results of their changes.

[0047] The output section can provide the ability to output the generated 3D model in different formats. For example, it can output in a variety of formats, such as an STL file for 3D printers, an FBX file for VR / AR, or a GLTF file for the web. This allows users to use 3D models in a format that suits their purpose. Optimization options are also provided depending on the output format, allowing 3D models to be output in the optimal form for each format.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The photo input unit inputs 2D travel photos, such as photos of beautiful beaches or historical buildings taken by the user. Step 2: The analysis unit analyzes the 2D travel photos input by the photo input unit, for example, analyzing the shapes, colors, and textures of objects and scenery in the photos. Step 3: The generator generates a 3D model based on the results of the analysis by the analyzer. For example, from a photo of a beach, a 3D model that reproduces the shape of the waves and the texture of the sand is generated. Step 4: The customization unit customizes the 3D model generated by the generation unit, for example, by changing the color of the model or highlighting specific parts. Step 5: The output unit outputs the 3D model customized by the customization unit. For example, it can be printed as a real souvenir using a 3D printer, or saved as digital data for use in VR or AR.

[0050] (Example 2) The 3D model generation system according to an embodiment of the present invention automatically analyzes 2D travel photos, generates 3D models, customizes them, and outputs them. This allows the 3D model generation system to instantly generate 3D models from 2D travel photos provided by users, allowing them to create travel mementos and souvenirs.

[0051] A 3D model generation system according to an embodiment includes a photo input unit, an analysis unit, a generation unit, a customization unit, and an output unit. The photo input unit inputs 2D travel photos, such as photos of beautiful beaches or historical buildings taken by a user. The analysis unit analyzes the 2D travel photos input by the photo input unit, such as analyzing the shapes, colors, and textures of objects and scenery in the photos. The generation unit generates a 3D model based on the results of the analysis by the analysis unit. For example, a beach photo generates a 3D model that reproduces the shape of waves and the texture of a sandy beach. The customization unit customizes the 3D model generated by the generation unit, such as by changing the color of the model or emphasizing specific features. The output unit outputs the 3D model customized by the customization unit. For example, the 3D model can be printed as a real souvenir using a 3D printer or saved as digital data for use in virtual reality or augmented reality (VR) applications. This allows the 3D model generation system according to an embodiment to generate, customize, and output a 3D model from 2D travel photos.

[0052] The photo input unit can automatically acquire metadata from photos and generate 3D models based on the metadata. For example, when a user uploads a photo, the system automatically analyzes the photo's metadata and acquires location information and the date and time the photo was taken. For example, the system can identify the location where the photo was taken based on GPS data and use that information to generate a 3D model. This allows the system to generate a more detailed 3D model using the photo's metadata.

[0053] The photo input unit can provide an interactive tool for the user to emphasize a specific part. For example, when the user uploads a photo, the photo input unit provides an interactive tool for emphasizing a specific part. For example, the user can drag with a mouse to select the part to be emphasized. This allows the user to generate a 3D model with the specific part emphasized.

[0054] The photo input unit can use the emotion estimation function to analyze the emotions of users regarding photos uploaded by the user and generate a 3D model that emphasizes parts with particularly high emotional value. The photo input unit, for example, uses the emotion estimation function to analyze the emotions of users regarding photos uploaded by the user in real time. For example, it analyzes facial expressions and tone of voice to calculate an emotion score. This allows the generation of a 3D model that emphasizes parts with particularly high emotional value based on the user's emotions.

[0055] The photo input unit can provide a function for uploading multiple photos at once and integrating them to generate a single 3D model. For example, the photo input unit can provide a function for a user to upload multiple photos at once and integrate the photos to generate a single 3D model. For example, a 3D model can be created by combining photos taken from different angles. This allows multiple photos to be integrated to generate a single 3D model.

[0056] The photo input unit provides a function that allows a user to add a description by voice when uploading a photo, and the generation of a 3D model can be supplemented based on the description. The photo input unit provides a function that allows a user to add a description by voice when uploading a photo, for example, by providing a voice description of the background of the photo or the situation at the time of shooting. This allows the generation of a 3D model to be supplemented based on the user's voice description.

[0057] The photo input unit can use the emotion estimation function to estimate the emotion a user is feeling when uploading a photo in real time, and provide an interface for eliciting positive emotions. The photo input unit, for example, uses the emotion estimation function to analyze the emotion a user is feeling when uploading a photo in real time. For example, the photo input unit analyzes the user's emotion using a camera or microphone. This makes it possible to provide an interface that elicits positive emotions according to the user's emotion.

[0058] The analysis unit can detect the movement and changes of objects in a photo and generate a dynamic 3D model. For example, when the generative AI analyzes a photo, the analysis unit uses an algorithm to detect the movement and changes of objects in the photo. For example, it can detect the movement of waves or trees swaying in the wind. This makes it possible to generate a dynamic 3D model that reflects the movement and changes of objects in the photo.

[0059] The analysis unit can automatically generate photographs from different viewpoints when analyzing a photograph, and create a more detailed 3D model based on the photographs. For example, the analysis unit uses an algorithm to automatically generate photographs from different viewpoints when analyzing a photograph. For example, it can recreate multiple viewpoints from a single photograph. This allows a more detailed 3D model to be generated based on the photographs from different viewpoints.

[0060] The analysis unit can use the emotion estimation function to analyze the facial expressions and poses of people in the photo and generate a 3D model that reflects those emotions. The analysis unit, for example, uses the emotion estimation function to analyze the facial expressions and poses of people in the photo. For example, it can detect smiling or surprised expressions. This allows it to generate a 3D model that reflects the emotions of people in the photo.

[0061] The analysis unit can simulate changes in different time periods and seasons when analyzing photos and generate 3D models based on the results. For example, the analysis unit uses an algorithm to simulate changes in different time periods and seasons when analyzing photos. For example, it can convert a daytime photo into a nighttime scene. This allows it to generate 3D models that reflect changes in different time periods and seasons.

[0062] The analysis unit can generate a 3D model based on a theme specified by the user when analyzing the photo. For example, the analysis unit can generate a 3D model based on a theme specified by the user when analyzing the photo. For example, the analysis unit can recreate a fantasy-style landscape. This allows the generation of a 3D model based on a theme specified by the user.

[0063] The analysis unit can use the emotion estimation function to analyze the user's emotions toward the scenery and objects in the photo and generate a 3D model that reflects those emotions. For example, the analysis unit can use the emotion estimation function to analyze the user's emotions toward the scenery and objects in the photo. For example, it can detect the user's emotion toward a beautiful landscape. This allows the generation of a 3D model that reflects the user's emotions.

[0064] The customization unit can provide an interactive editing tool that allows the user to manually modify the generated 3D model. For example, the customization unit provides an interactive editing tool that allows the user to manually modify the generated 3D model. For example, the shape of the model can be changed by mouse or touch operation. This allows the user to manually modify the 3D model.

[0065] The customization unit can provide a function of automatically applying a color or texture specified by a user when customizing a 3D model. For example, the customization unit provides a function of automatically applying a color or texture specified by a user when customizing a 3D model. For example, the customization unit applies a color selected by the user to the entire model. This allows the color or texture specified by the user to be automatically applied.

[0066] The customization unit can use the emotion estimation function to analyze the emotions of the user when customizing and make customization suggestions to elicit positive emotions. The customization unit, for example, uses the emotion estimation function to analyze the emotions of the user when customizing in real time. For example, the customization unit analyzes the user's emotions using a camera or microphone. This makes it possible to make positive customization suggestions based on the user's emotions.

[0067] The customization unit may provide a function for converting the generated 3D model into a different art style. For example, the customization unit may use an algorithm for converting the generated 3D model into a different art style. For example, the customization unit may convert the generated 3D model into a Picasso-esque abstract style. This allows the generated 3D model to be converted into a different art style.

[0068] The customization unit can provide a community function for referring to customization examples created by other users when customizing a 3D model. The customization unit provides, for example, a community function for sharing customization examples created by other users. For example, a user can upload their own customization example. This allows the user to customize a 3D model by referring to the customization examples of other users.

[0069] The customization unit can use the emotion estimation function to collect other users' emotional reactions to the 3D model customized by the user and provide customization feedback based on the collected emotional reactions. For example, the customization unit uses the emotion estimation function to collect other users' emotional reactions to the 3D model customized by the user in real time, for example, by analyzing facial expressions and voices. This allows the customization unit to provide customization feedback based on the emotional reactions of other users.

[0070] The customization unit can use the emotion estimation function to collect other users' emotional reactions to the 3D model customized by the user and provide customization feedback based on the collected emotional reactions. For example, the customization unit uses the emotion estimation function to collect other users' emotional reactions to the 3D model customized by the user in real time, for example, by analyzing facial expressions and voices. This allows the customization unit to provide customization feedback based on the emotional reactions of other users.

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

[0072] When a user uploads a photo, the photo input unit can automatically recognize specific objects contained in the photo and generate a 3D model based on that object. For example, it can automatically recognize buildings, cars, people, etc. in the photo and generate individual 3D models for each object. This allows users to create 3D models that focus on specific objects. It is also possible for users to input additional information about recognized objects to generate more detailed 3D models.

[0073] The analysis unit analyzes the position of light reflections and shadows in a photograph to generate a more realistic 3D model. For example, it identifies the position of the sun and the direction of the light source and reproduces the position of shadows and light reflections based on that. This allows the generation of a 3D model that faithfully reproduces the lighting conditions in the photograph. It is also possible to generate a 3D model that reflects day and night and seasonal changes by simulating different lighting conditions.

[0074] The generator can generate a 3D model based on a theme specified by the user. For example, if the user specifies a theme such as "futuristic city" or "medieval castle," the generator generates a 3D model incorporating design elements based on that theme. This allows the user to create a 3D model based on a theme that suits their preferences. In addition, by providing customization options based on the theme, more detailed adjustments can be made.

[0075] The customization section can provide interactive editing tools that allow users to manually modify the generated 3D model. For example, users can change the shape of the model or add specific parts using mouse or touch operations. This allows users to freely customize the 3D model to their liking. The editing tools also include a function to preview changes in real time, allowing users to instantly see the results of their changes.

[0076] The output section can provide the ability to output the generated 3D model in different formats. For example, it can output in a variety of formats, such as an STL file for 3D printers, an FBX file for VR / AR, or a GLTF file for the web. This allows users to use 3D models in a format that suits their purpose. Optimization options are also provided depending on the output format, allowing 3D models to be output in the optimal form for each format.

[0077] The photo input unit uses the emotion estimation function to analyze the emotions of users regarding uploaded photos and generate a 3D model that emphasizes parts with particularly high emotional value. For example, to highlight a landscape or memorable place that the user found particularly moving, specific parts are reproduced in detail based on the emotion score. This allows for the generation of a more personalized 3D model based on the user's emotions. It is also possible to add specific effects and decorations based on the emotion score.

[0078] The analysis unit uses emotion estimation to analyze the facial expressions and poses of people in photos and generate 3D models that reflect those emotions. For example, it can detect smiling or surprised expressions and generate 3D models with poses and expressions that reflect those emotions. This allows for the creation of 3D models that faithfully reproduce the emotions of people in photos. It is also possible to add specific animations and effects based on the emotions.

[0079] The customization unit uses the emotion estimation function to analyze the user's emotions when customizing and can make customization suggestions to elicit positive emotions. For example, when the user is having fun, it can suggest bright colors and fun design options. This allows the user to have a more enjoyable customization experience. It can also automatically add specific effects and decorations based on the user's emotions.

[0080] The customization unit can use the emotion estimation function to collect other users' emotional reactions to a 3D model customized by the user and provide customization feedback based on that. For example, if another user expresses emotion or surprise in response to a customized 3D model, feedback can be provided based on that emotional reaction. This allows the user to refer to the emotional reactions of other users and perform better customization. It is also possible to suggest specific customization options based on the emotional reaction.

[0081] The photo input unit can use the emotion estimation function to estimate the user's emotion in real time when uploading a photo and provide an interface to elicit positive emotions. For example, the system can analyze the user's emotion using the camera or microphone and display messages or effects designed to elicit positive emotions. This allows the user to have a more enjoyable uploading experience. It is also possible to automatically apply specific filters or effects based on the user's emotion.

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

[0083] Step 1: The photo input unit inputs 2D travel photos, such as photos of beautiful beaches or historical buildings taken by the user. Step 2: The analysis unit analyzes the 2D travel photos input by the photo input unit, for example, analyzing the shapes, colors, and textures of objects and scenery in the photos. Step 3: The generator generates a 3D model based on the results of the analysis by the analyzer. For example, from a photo of a beach, a 3D model that reproduces the shape of the waves and the texture of the sand is generated. Step 4: The customization unit customizes the 3D model generated by the generation unit, for example, by changing the color of the model or highlighting specific parts. Step 5: The output unit outputs the 3D model customized by the customization unit. For example, it can be printed as a real souvenir using a 3D printer, or saved as digital data for use in VR or AR.

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

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

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

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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. [Explanation of symbols]

[0151] 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 photo input section for inputting 2D travel photos; an analysis unit that analyzes the 2D travel photos input by the photo input unit; a generation unit that generates a 3D model based on the results of the analysis by the analysis unit; a customization unit that customizes the 3D model generated by the generation unit; an output unit that outputs the 3D model customized by the customization unit; A system characterized by:

2. The photo input unit Automatically retrieves metadata from photos and generates 3D models based on said metadata 2. The system of claim 1.

3. The analysis unit Detects movement and changes in objects in photos and generates dynamic 3D models 2. The system of claim 1.

4. The customization unit Provides interactive editing tools that allow users to manually modify the generated 3D model.

2. The system of claim 1.

5. The photo input unit Analyzes the emotions surrounding photos uploaded by users and generates 3D models that emphasize the parts with the highest emotional value.

2. The system of claim 1.

Images