System

Abstract

An object of the system according to the embodiment is to analyze a subject and an environment and propose settings of an optimal composition and a close-up.SOLUTION: A system according to an embodiment includes a subject and environment analysis unit, a composition proposal unit, and a shift adjustment unit. The subject and environment analysis unit analyzes a subject and an environment on the spot. The composition proposal unit proposes an optimal composition on the basis of a result of the analysis performed by the subject and environment analysis unit. The shift adjustment unit proposes an optimal shift setting based on the composition proposed by the composition proposal 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] With conventional technology, it was difficult to set the optimal composition and close-up / close-up settings that took into account the subject and the environment when taking a photograph.

[0005] The system according to the embodiment aims to analyze the subject and the environment, and propose optimal composition and close-up / close-up settings. [Means for solving the problem]

[0006] The system according to the embodiment includes a subject and environment analysis unit, a composition suggestion unit, and a focus adjustment unit. The subject and environment analysis unit analyzes the subject and its environment. The composition suggestion unit suggests an optimal composition based on the results of the analysis by the subject and environment analysis unit. The focus adjustment unit suggests optimal focus settings based on the composition suggested by the composition suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the subject and the environment, and propose optimal composition and close-up / close-up settings. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 photography support system according to the embodiment of the present invention uses a generative AI built into a smartphone or camera to help users take the best possible photos by considering the subject, the environment, composition, close-ups, etc. This allows the photography support system to easily take high-quality photos.

[0029] A photography support system according to an embodiment includes a subject and environment analysis unit, a composition suggestion unit, and a focus adjustment unit. The subject and environment analysis unit analyzes the subject and the surrounding environment. For example, when taking a landscape photo using a smartphone or camera, the generation AI recognizes elements such as mountains, ocean, and sky and analyzes how each element is arranged. When taking a portrait photo, the generation AI recognizes the subject's face and facial expression and analyzes them together with the surrounding environment. The composition suggestion unit proposes an optimal composition based on the results of the subject and environment analysis. For example, for a landscape photo, the generation AI proposes a composition such as "positioning the subject in the center of the screen and incorporating natural scenery into the background." For a portrait photo, the generation AI proposes a composition such as "positioning the subject on the right side of the screen and incorporating natural scenery into the background." The focus adjustment unit proposes optimal focus settings based on the composition proposed by the composition suggestion unit. For example, for a landscape photo, the generation AI proposes a setting such as "pulling the camera back a little to capture a wider view of the entire landscape." Furthermore, for portrait photos, the generation AI suggests settings such as "move the camera a little closer to emphasize the subject's face." This allows the photography support system according to the embodiment to enable users to easily take high-quality photos. For example, users can take the best photos in a variety of scenes, such as landscape photos at travel destinations or commemorative family photos. Furthermore, even beginner photographers can take professional-quality photos.

[0030] The subject and environment analysis unit can analyze the subject's movements and changes in facial expression in real time and notify the user of the optimal shutter opportunity. For example, the generation AI in the subject and environment analysis unit analyzes the camera's video data in real time and detects the subject's movements and changes in facial expression. For example, it notifies the user of the optimal shutter opportunity to capture the moment the subject smiles or strikes a specific pose. The generation AI also analyzes the subject's movements and changes in facial expression and notifies the user of the optimal shutter opportunity by voice or on the screen. For example, the generation AI may notify the user by voice such as "Now is your chance to take a picture" or display "Shutter opportunity" on the screen. This allows the user to take the picture at the optimal timing.

[0031] The subject and environment analysis unit can refer to the subject's past photo data, learn the subject's most preferred composition and settings, and reflect this in the analysis. For example, the subject and environment analysis unit uses a generation AI to retrieve the subject's past photo data from a database and learn the subject's most preferred composition and settings. For example, it analyzes the metadata of photos taken in the past to understand the subject's preferences and tendencies. The generation AI also analyzes the subject's past photo data, learns the subject's most preferred composition and settings, and reflects this in the analysis. For example, the generation AI extracts the subject's preferred composition and settings from past photo data and reflects them in the current photo. This makes it possible to take photos based on the user's preferences.

[0032] The composition suggestion unit allows the generation AI to propose multiple compositions and further optimize them based on the composition selected by the user. For example, the generation AI proposes multiple compositions and further optimizes them based on the composition selected by the user. For example, the generation AI adjusts the subject position and background for the composition selected by the user. The generation AI also proposes multiple compositions and optimizes them based on the composition selected by the user. For example, the generation AI adjusts the subject position and background for the composition selected by the user to provide the optimal composition. This makes it possible to take an optimal photo based on the composition selected by the user.

[0033] The composition suggestion unit can dynamically adjust the composition by reflecting the subject's movement and changes in the environment in real time. For example, the generation AI in the composition suggestion unit analyzes the subject's movement and changes in the environment in real time and dynamically adjusts the composition. For example, the generation AI automatically recalculates the composition every time the subject moves and maintains the optimal placement. The generation AI also reflects the subject's movement and changes in the environment in real time and dynamically adjusts the composition. For example, the generation AI automatically recalculates the composition every time the subject moves and maintains the optimal placement. This makes it possible to maintain the optimal composition even in dynamic scenes.

[0034] The focus adjustment unit allows the generation AI to measure the size and distance of the subject in real time and automatically adjust the optimal focus settings. For example, the generation AI analyzes the camera's video data in real time and measures the size and distance of the subject. For example, it automatically adjusts the optimal focus settings as the subject moves closer or farther away. The generation AI also measures the size and distance of the subject in real time and automatically adjusts the optimal focus settings. For example, it automatically adjusts the optimal focus settings as the subject moves closer or farther away. This makes it possible to automatically adjust the optimal focus settings according to the size and distance of the subject.

[0035] The zoom adjustment unit also takes background elements into account when adjusting the zoom, making it possible to optimize the overall balance. For example, the generation AI analyzes the elements of the subject and background, and optimizes the overall balance when adjusting the zoom. For example, it takes into account the position of the background scenery and buildings to make adjustments so that the subject stands out the most. The generation AI also takes background elements into account when adjusting the zoom, making it possible to optimize the overall balance. For example, it takes into account the position of the background scenery and buildings to make adjustments so that the subject stands out the most. This makes it possible to optimize the overall balance by taking background elements into account.

[0036] The focus adjustment unit applies focus adjustment to zooming in and out when shooting video, maintaining optimal framing even in dynamic scenes. For example, the generation AI controls zooming in and out in real time when shooting video, maintaining optimal framing even in dynamic scenes. For example, it automatically zooms in and out every time the subject moves. The generation AI also applies focus adjustment to zooming in and out when shooting video, maintaining optimal framing even in dynamic scenes. For example, the generation AI automatically zooms in and out every time the subject moves, maintaining optimal framing. This makes it possible to maintain optimal framing even in dynamic scenes.

[0037] The close-up adjustment unit can apply the close-up adjustment to optimal placement in scenes with multiple subjects. For example, the generation AI analyzes multiple subjects in real time and proposes optimal close-up settings. For example, it adjusts the settings so that everyone is evenly represented in a group photo. The generation AI can also apply the close-up adjustment to optimal placement in scenes with multiple subjects. For example, it adjusts the settings so that everyone is evenly represented in a group photo. This makes it possible to maintain optimal placement even in scenes with multiple subjects.

[0038] The optimal setting guidance section can dynamically adjust settings by reflecting environmental changes (e.g., changes in light or weather) in real time during setting guidance. In the optimal setting guidance section, for example, the generation AI analyzes environmental changes in real time and dynamically adjusts settings. For example, the exposure and white balance are automatically adjusted according to changes in light. The generation AI also reflects environmental changes in real time during setting guidance and dynamically adjusts settings. For example, the generation AI automatically adjusts exposure and white balance according to changes in light. This makes it possible to reflect environmental changes in real time and dynamically adjust settings.

[0039] The optimal setting guidance unit applies the setting guidance to the optimal settings when shooting video, allowing high-quality footage to be captured even in dynamic scenes. For example, the generation AI suggests optimal settings when shooting video in real time, allowing high-quality footage to be captured even in dynamic scenes. For example, the generation AI adjusts the exposure and white balance according to the movement of the subject. The generation AI also applies the setting guidance to the optimal settings when shooting video, allowing high-quality footage to be captured even in dynamic scenes. For example, the generation AI adjusts the exposure and white balance according to the movement of the subject. This makes it possible to capture high-quality footage even in dynamic scenes.

[0040] The optimal setting guidance unit applies setting guidance to different shooting modes (for example, portrait mode and night mode) and can suggest optimal settings according to the scene. For example, the generation AI proposes optimal settings according to different shooting modes. For example, in portrait mode, it proposes settings that emphasize the subject's face, and in night mode, it proposes settings that are suitable for low light. The generation AI also applies setting guidance to different shooting modes and suggests optimal settings according to the scene. For example, in portrait mode, it proposes settings that emphasize the subject's face, and in night mode, it proposes settings that are suitable for low light. This makes it possible to suggest optimal settings according to the scene.

[0041] The high-quality photo capture unit uses a generation AI to automatically analyze images after capture and perform optimal editing and correction. The high-quality photo capture unit, for example, uses a generation AI to analyze images after capture and automatically perform optimal editing and correction. For example, it automatically adjusts exposure and white balance, removes noise, etc. The generation AI also automatically analyzes images after capture and performs optimal editing and correction. For example, it automatically adjusts exposure and white balance, removes noise, etc. This allows the image to be automatically analyzed after capture and perform optimal editing and correction.

[0042] The high-quality photo capture unit applies high-quality photo capture to the video snapshot function, making it possible to extract the best moment from a video. For example, the generation AI uses the video snapshot function to extract the best moment. For example, it automatically selects the moment when the subject smiles or strikes a particular pose. The generation AI also applies high-quality photo capture to the video snapshot function to extract the best moment from a video. For example, it automatically selects the moment when the subject smiles or strikes a particular pose. This makes it possible to extract the best moment from a video.

[0043] The high-quality photo capture unit applies high-quality photo capture to multi-angle shooting using multiple cameras and can automatically select photos from the optimal angle. For example, the generation AI performs multi-angle shooting using multiple cameras and automatically selects photos from the optimal angle. For example, it selects the angle at which the subject's face looks most beautiful. The generation AI also applies high-quality photo capture to multi-angle shooting using multiple cameras and automatically selects photos from the optimal angle. For example, the generation AI selects the angle at which the subject's face looks most beautiful. This makes it possible to automatically select photos from the optimal angle when shooting multi-angle using multiple cameras.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The photography support system can further include an audio guide unit. The audio guide unit provides the user with audio photography advice in real time. For example, when taking a landscape photo, the audio guide unit can issue specific instructions such as "Point the camera a little more to the right." When taking a portrait photo, the audio guide unit can issue instructions such as "Ask the subject to smile." This allows the user to take higher quality photos by following the audio guide.

[0046] The photography support system can further include a weather forecasting unit. The weather forecasting unit predicts the weather at the photography location in real time and notifies the user of the optimal timing for photography. For example, it can notify the user before it rains, saying, "Now is the time to take a photo." It can also predict when the sun will shine and provide information such as, "It will be sunny in 10 minutes." This allows the user to take photos at the optimal timing regardless of the weather.

[0047] The photography support system can further include a background sound analysis unit. The background sound analysis unit analyzes the sound environment of the shooting location and suggests the optimal timing for taking a photo. For example, it can notify the user by saying, "Now is the time to take a photo," in order to capture a quiet moment. It can also issue instructions such as "Please wait a moment" if the background sound is loud. This allows the user to take a photo at the optimal timing taking into account the background sound.

[0048] The photography support system can also analyze background elements and automatically select the most suitable background. For example, when taking a landscape photo, it analyzes elements such as mountains, ocean, and sky and selects the most beautiful background. Also, when taking a portrait, it can incorporate natural scenery into the background. This allows you to take the best photo taking into account the background elements.

[0049] The photography support system can also predict the movement of the subject and automatically adjust the optimal shutter speed. For example, if the subject is moving fast at a sporting event, the shutter speed will be increased to capture the movement. On the other hand, if the subject is moving slowly, the shutter speed can be decreased to express the movement. This makes it possible to set the optimal shutter speed even for scenes with a lot of movement.

[0050] The photography support system can also analyze the subject's clothing and color tone and automatically adjust the optimal color balance. For example, if the subject is wearing brightly colored clothing, the color balance can be adjusted to make the colors stand out. Also, if the subject is wearing monotone clothing, the color balance can be adjusted to achieve overall harmony. This makes it possible to set the optimal color balance according to the subject's clothing and color tone.

[0051] The photography support system can also analyze the age and gender of the subject and suggest optimal shooting settings. For example, it can suggest bright and soft settings when taking photos of children, and subdued settings when taking photos of seniors. It can also suggest settings that highlight the distinctive features of each person when taking photos of men and women. This makes it possible to suggest optimal shooting settings according to the age and gender of the subject.

[0052] The photography support system can also analyze the subject's pose and suggest the most suitable pose. For example, it can suggest a pose that will make the subject smile naturally. It can also suggest a pose that makes the subject look relaxed. This allows the system to take the best photo based on the subject's pose.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The subject and environment analysis unit analyzes the subject and its surroundings. For example, when taking a landscape photo using a smartphone or camera, the generation AI recognizes elements such as mountains, ocean, and sky, and analyzes how each element is positioned. When taking a portrait, the generation AI recognizes the subject's face and facial expression and analyzes them in conjunction with the surrounding environment. Step 2: The composition suggestion unit proposes the optimal composition based on the results of the analysis of the subject and the environment. For example, for a landscape photo, the generation AI suggests a composition such as "place the subject in the center of the screen and include a natural landscape in the background." For a portrait photo, the generation AI also suggests a composition such as "place the subject on the right side of the screen and include a natural landscape in the background." Step 3: The focus adjustment unit proposes optimal focus settings based on the composition proposed by the composition suggestion unit. For example, for landscape photos, the generation AI suggests settings such as "pull the camera back a little to capture a wider view of the entire landscape." For portraits, the generation AI also suggests settings such as "move the camera closer a little to emphasize the subject's face."

[0055] (Example 2) The photography support system according to the embodiment of the present invention uses a generative AI built into a smartphone or camera to help users take the best possible photos by considering the subject, the environment, composition, close-ups, etc. This allows the photography support system to easily take high-quality photos.

[0056] A photography support system according to an embodiment includes a subject and environment analysis unit, a composition suggestion unit, and a focus adjustment unit. The subject and environment analysis unit analyzes the subject and the surrounding environment. For example, when taking a landscape photo using a smartphone or camera, the generation AI recognizes elements such as mountains, ocean, and sky and analyzes how each element is arranged. When taking a portrait photo, the generation AI recognizes the subject's face and facial expression and analyzes them together with the surrounding environment. The composition suggestion unit proposes an optimal composition based on the results of the subject and environment analysis. For example, for a landscape photo, the generation AI proposes a composition such as "positioning the subject in the center of the screen and incorporating natural scenery into the background." For a portrait photo, the generation AI proposes a composition such as "positioning the subject on the right side of the screen and incorporating natural scenery into the background." The focus adjustment unit proposes optimal focus settings based on the composition proposed by the composition suggestion unit. For example, for a landscape photo, the generation AI proposes a setting such as "pulling the camera back a little to capture a wider view of the entire landscape." Furthermore, for portrait photos, the generation AI suggests settings such as "move the camera a little closer to emphasize the subject's face." This allows the photography support system according to the embodiment to enable users to easily take high-quality photos. For example, users can take the best photos in a variety of scenes, such as landscape photos at travel destinations or commemorative family photos. Furthermore, even beginner photographers can take professional-quality photos.

[0057] The subject and environment analysis unit can analyze the subject's movements and changes in facial expression in real time and notify the user of the optimal shutter opportunity. For example, the generation AI in the subject and environment analysis unit analyzes the camera's video data in real time and detects the subject's movements and changes in facial expression. For example, it notifies the user of the optimal shutter opportunity to capture the moment the subject smiles or strikes a specific pose. The generation AI also analyzes the subject's movements and changes in facial expression and notifies the user of the optimal shutter opportunity by voice or on the screen. For example, the generation AI may notify the user by voice such as "Now is your chance to take a picture" or display "Shutter opportunity" on the screen. This allows the user to take the picture at the optimal timing.

[0058] The subject and environment analysis unit can refer to the subject's past photo data, learn the subject's most preferred composition and settings, and reflect this in the analysis. For example, the subject and environment analysis unit uses a generation AI to retrieve the subject's past photo data from a database and learn the subject's most preferred composition and settings. For example, it analyzes the metadata of photos taken in the past to understand the subject's preferences and tendencies. The generation AI also analyzes the subject's past photo data, learns the subject's most preferred composition and settings, and reflects this in the analysis. For example, the generation AI extracts the subject's preferred composition and settings from past photo data and reflects them in the current photo. This makes it possible to take photos based on the user's preferences.

[0059] The subject and environment analysis unit can use the emotion estimation function to analyze the subject's emotions and suggest settings to capture the most emotional moment. For example, the generative AI in the subject and environment analysis unit analyzes the subject's facial expressions and uses the emotion estimation function to estimate their emotions. For example, it suggests the optimal settings to capture the moment when the subject shows a smile or a surprised expression. The generative AI also analyzes the subject's emotions and suggests settings to capture the most emotional moment. For example, the generative AI adjusts the camera settings to capture the moment when the subject is moved. This makes it possible to take emotionally rich photos.

[0060] The composition suggestion unit allows the generation AI to propose multiple compositions and further optimize them based on the composition selected by the user. For example, the generation AI proposes multiple compositions and further optimizes them based on the composition selected by the user. For example, the generation AI adjusts the subject position and background for the composition selected by the user. The generation AI also proposes multiple compositions and optimizes them based on the composition selected by the user. For example, the generation AI adjusts the subject position and background for the composition selected by the user to provide the optimal composition. This makes it possible to take an optimal photo based on the composition selected by the user.

[0061] The composition suggestion unit can dynamically adjust the composition by reflecting the subject's movement and changes in the environment in real time. For example, the generation AI in the composition suggestion unit analyzes the subject's movement and changes in the environment in real time and dynamically adjusts the composition. For example, the generation AI automatically recalculates the composition every time the subject moves and maintains the optimal placement. The generation AI also reflects the subject's movement and changes in the environment in real time and dynamically adjusts the composition. For example, the generation AI automatically recalculates the composition every time the subject moves and maintains the optimal placement. This makes it possible to maintain the optimal composition even in dynamic scenes.

[0062] The composition suggestion unit can use the emotion estimation function to suggest a composition that corresponds to the user's emotion, enabling the user to take an emotionally satisfying photo. In the composition suggestion unit, for example, the generation AI analyzes the user's emotion and suggests a composition that corresponds to the emotion. For example, when the user is relaxed, the generation AI suggests a composition that has spaciousness. In addition, the generation AI suggests a composition that corresponds to the user's emotion, enabling the user to take an emotionally satisfying photo. For example, when the user is relaxed, the generation AI suggests a composition that has spaciousness, enabling the user to take an emotionally satisfying photo. This makes it possible to take a photo with a composition that corresponds to the user's emotion.

[0063] The focus adjustment unit allows the generation AI to measure the size and distance of the subject in real time and automatically adjust the optimal focus settings. For example, the generation AI analyzes the camera's video data in real time and measures the size and distance of the subject. For example, it automatically adjusts the optimal focus settings as the subject moves closer or farther away. The generation AI also measures the size and distance of the subject in real time and automatically adjusts the optimal focus settings. For example, it automatically adjusts the optimal focus settings as the subject moves closer or farther away. This makes it possible to automatically adjust the optimal focus settings according to the size and distance of the subject.

[0064] The zoom adjustment unit also takes background elements into account when adjusting the zoom, making it possible to optimize the overall balance. For example, the generation AI analyzes the elements of the subject and background, and optimizes the overall balance when adjusting the zoom. For example, it takes into account the position of the background scenery and buildings to make adjustments so that the subject stands out the most. The generation AI also takes background elements into account when adjusting the zoom, making it possible to optimize the overall balance. For example, it takes into account the position of the background scenery and buildings to make adjustments so that the subject stands out the most. This makes it possible to optimize the overall balance by taking background elements into account.

[0065] The close-up adjustment unit uses the emotion estimation function to suggest close-up settings according to the subject's emotion, making it possible to take photos with emotional impact. For example, the generation AI analyzes the subject's emotion and suggests close-up settings according to the emotion. For example, when the subject is smiling, it zooms in to emphasize the emotion. The generation AI also suggests close-up settings according to the subject's emotion, making it possible to take photos with emotional impact. For example, when the subject is smiling, it zooms in to emphasize the emotion. This makes it possible to take photos with emotional impact.

[0066] The focus adjustment unit applies focus adjustment to zooming in and out when shooting video, maintaining optimal framing even in dynamic scenes. For example, the generation AI controls zooming in and out in real time when shooting video, maintaining optimal framing even in dynamic scenes. For example, it automatically zooms in and out every time the subject moves. The generation AI also applies focus adjustment to zooming in and out when shooting video, maintaining optimal framing even in dynamic scenes. For example, the generation AI automatically zooms in and out every time the subject moves, maintaining optimal framing. This makes it possible to maintain optimal framing even in dynamic scenes.

[0067] The close-up adjustment unit can apply the close-up adjustment to optimal placement in scenes with multiple subjects. For example, the generation AI analyzes multiple subjects in real time and proposes optimal close-up settings. For example, it adjusts the settings so that everyone is evenly represented in a group photo. The generation AI can also apply the close-up adjustment to optimal placement in scenes with multiple subjects. For example, it adjusts the settings so that everyone is evenly represented in a group photo. This makes it possible to maintain optimal placement even in scenes with multiple subjects.

[0068] The zoom adjustment unit can use the emotion estimation function to suggest zoom variations according to the emotion of the subject, allowing the user to select from them. In the zoom adjustment unit, for example, the generation AI analyzes the emotion of the subject and suggests zoom variations according to the emotion. For example, when the subject is smiling, the generation AI presents the option of zooming in or out. The generation AI also suggests zoom variations according to the emotion of the subject, allowing the user to select from them. For example, when the subject is smiling, the generation AI presents the option of zooming in or out, allowing the user to select from them. This allows the user to select from zoom variations according to the emotion.

[0069] The optimal setting guidance section can dynamically adjust settings by reflecting environmental changes (e.g., changes in light or weather) in real time during setting guidance. In the optimal setting guidance section, for example, the generation AI analyzes environmental changes in real time and dynamically adjusts settings. For example, the exposure and white balance are automatically adjusted according to changes in light. The generation AI also reflects environmental changes in real time during setting guidance and dynamically adjusts settings. For example, the generation AI automatically adjusts exposure and white balance according to changes in light. This makes it possible to reflect environmental changes in real time and dynamically adjust settings.

[0070] The optimal setting guidance unit can use the emotion estimation function to suggest settings that correspond to the user's emotions, enabling the user to take emotionally satisfying photos. In the optimal setting guidance unit, for example, the generation AI analyzes the user's emotions and suggests settings that correspond to the emotions. For example, when the user is relaxed, a gentle setting is suggested. The generation AI also suggests settings that correspond to the user's emotions, enabling the user to take emotionally satisfying photos. For example, when the user is relaxed, the generation AI suggests a gentle setting, enabling the user to take emotionally satisfying photos. In this way, it is possible to take emotionally satisfying photos with settings that correspond to the user's emotions.

[0071] The optimal setting guidance unit applies the setting guidance to the optimal settings when shooting video, allowing high-quality footage to be captured even in dynamic scenes. For example, the generation AI suggests optimal settings when shooting video in real time, allowing high-quality footage to be captured even in dynamic scenes. For example, the generation AI adjusts the exposure and white balance according to the movement of the subject. The generation AI also applies the setting guidance to the optimal settings when shooting video, allowing high-quality footage to be captured even in dynamic scenes. For example, the generation AI adjusts the exposure and white balance according to the movement of the subject. This makes it possible to capture high-quality footage even in dynamic scenes.

[0072] The optimal setting guidance unit applies setting guidance to different shooting modes (for example, portrait mode and night mode) and can suggest optimal settings according to the scene. For example, the generation AI proposes optimal settings according to different shooting modes. For example, in portrait mode, it proposes settings that emphasize the subject's face, and in night mode, it proposes settings that are suitable for low light. The generation AI also applies setting guidance to different shooting modes and suggests optimal settings according to the scene. For example, in portrait mode, it proposes settings that emphasize the subject's face, and in night mode, it proposes settings that are suitable for low light. This makes it possible to suggest optimal settings according to the scene.

[0073] The optimal setting guidance unit uses an emotion estimation function to suggest setting variations according to the user's emotions, thereby expanding the options available. In the optimal setting guidance unit, for example, the generation AI analyzes the user's emotions and suggests setting variations according to the emotions. For example, when the user is relaxed, it suggests a gentle setting. The generation AI also suggests setting variations according to the user's emotions, thereby expanding the options available. For example, when the user is relaxed, it suggests a gentle setting, thereby expanding the options available. In this way, it is possible to suggest setting variations according to the user's emotions, thereby expanding the options available.

[0074] The high-quality photo capture unit uses a generation AI to automatically analyze images after capture and perform optimal editing and correction. The high-quality photo capture unit, for example, uses a generation AI to analyze images after capture and automatically perform optimal editing and correction. For example, it automatically adjusts exposure and white balance, removes noise, etc. The generation AI also automatically analyzes images after capture and performs optimal editing and correction. For example, it automatically adjusts exposure and white balance, removes noise, etc. This allows the image to be automatically analyzed after capture and perform optimal editing and correction.

[0075] The high-quality photo capture unit uses an emotion estimation function to automatically apply filters and effects according to the user's emotions, making it possible to generate photos with emotional impact. For example, the generation AI analyzes the user's emotions and automatically applies filters and effects according to the emotions. For example, when the user is happy, a bright filter is applied. The generation AI also uses the emotion estimation function to automatically apply filters and effects according to the user's emotions, making it possible to generate photos with emotional impact. For example, the generation AI applies a bright filter when the user is happy. This makes it possible to automatically apply filters and effects according to the emotions, making it possible to generate photos with emotional impact.

[0076] The high-quality photo capture unit applies high-quality photo capture to the video snapshot function, making it possible to extract the best moment from a video. For example, the generation AI uses the video snapshot function to extract the best moment. For example, it automatically selects the moment when the subject smiles or strikes a particular pose. The generation AI also applies high-quality photo capture to the video snapshot function to extract the best moment from a video. For example, it automatically selects the moment when the subject smiles or strikes a particular pose. This makes it possible to extract the best moment from a video.

[0077] The high-quality photo capture unit applies high-quality photo capture to multi-angle shooting using multiple cameras and can automatically select photos from the optimal angle. For example, the generation AI performs multi-angle shooting using multiple cameras and automatically selects photos from the optimal angle. For example, it selects the angle at which the subject's face looks most beautiful. The generation AI also applies high-quality photo capture to multi-angle shooting using multiple cameras and automatically selects photos from the optimal angle. For example, the generation AI selects the angle at which the subject's face looks most beautiful. This makes it possible to automatically select photos from the optimal angle when shooting multi-angle using multiple cameras.

[0078] The high-quality photo capture unit uses an emotion estimation function to suggest photo variations that correspond to the user's emotions, thereby expanding the options. In the high-quality photo capture unit, for example, the generation AI analyzes the user's emotions and suggests photo variations that correspond to the emotions. For example, when the user is relaxed, it suggests calm photos. The generation AI also uses the emotion estimation function to suggest photo variations that correspond to the user's emotions, thereby expanding the options. For example, when the user is relaxed, the generation AI suggests calm photos, thereby expanding the options. This makes it possible to suggest photo variations that correspond to the user's emotions, thereby expanding the options.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The photography support system can further include an audio guide unit. The audio guide unit provides the user with audio photography advice in real time. For example, when taking a landscape photo, the audio guide unit can issue specific instructions such as "Point the camera a little more to the right." When taking a portrait photo, the audio guide unit can issue instructions such as "Ask the subject to smile." This allows the user to take higher quality photos by following the audio guide.

[0081] The photography support system can further include a weather forecasting unit. The weather forecasting unit predicts the weather at the photography location in real time and notifies the user of the optimal timing for photography. For example, it can notify the user before it rains, saying, "Now is the time to take a photo." It can also predict when the sun will shine and provide information such as, "It will be sunny in 10 minutes." This allows the user to take photos at the optimal timing regardless of the weather.

[0082] The photography support system can further include a background sound analysis unit. The background sound analysis unit analyzes the sound environment of the shooting location and suggests the optimal timing for taking a photo. For example, it can notify the user by saying, "Now is the time to take a photo," in order to capture a quiet moment. It can also issue instructions such as "Please wait a moment" if the background sound is loud. This allows the user to take a photo at the optimal timing taking into account the background sound.

[0083] The photography support system can also estimate the subject's emotions and automatically apply the optimal filter based on the estimated emotions. For example, if the subject is smiling, a bright filter can be applied to emphasize the emotion. Or, if the subject is surprised, a filter that enhances contrast can be applied. This makes it possible to automatically generate emotionally rich photos.

[0084] The photography support system can also estimate the user's emotions and suggest the optimal shooting mode based on the estimated emotions. For example, if the user is relaxed, it can suggest portrait mode, allowing the user to take photos that are emotionally satisfying. If the user is excited, it can suggest sports mode, allowing the user to capture scenes with movement. This makes it possible to select the optimal shooting mode according to the user's emotions.

[0085] The photography support system can also estimate the subject's emotions and automatically adjust the optimal shutter speed based on the estimated emotions. For example, if the subject is smiling, the shutter speed can be increased to capture the moment. Also, if the subject is moved, the shutter speed can be slowed to express the emotion. This allows for the capture of photos rich in emotion.

[0086] The photography support system can also estimate the subject's emotions and suggest optimal compositions based on the estimated emotions. For example, if the subject is smiling, the system can place the subject in the center to emphasize their emotion. Or, if the subject looks surprised, the system can place the subject on the edge of the frame to incorporate a wide range of the background. This allows the system to suggest compositions rich in emotion.

[0087] The photography support system can also estimate the subject's emotions and automatically adjust the optimal exposure settings based on the estimated emotions. For example, if the subject is smiling, the exposure can be brightened to emphasize their emotion. Also, if the subject is moved, the exposure can be darkened to express their emotion. This allows you to take photos that are rich in emotion.

[0088] The photography support system can also estimate the subject's emotions and automatically adjust the optimal white balance based on the estimated emotions. For example, if the subject is smiling, a warm white balance can be applied to emphasize the emotion. On the other hand, if the subject is surprised, a cool white balance can be applied. This allows for the capture of photos rich in emotion.

[0089] The photography support system can also analyze background elements and automatically select the most suitable background. For example, when taking a landscape photo, it analyzes elements such as mountains, ocean, and sky and selects the most beautiful background. Also, when taking a portrait, it can incorporate natural scenery into the background. This allows you to take the best photo taking into account the background elements.

[0090] The photography support system can also predict the movement of the subject and automatically adjust the optimal shutter speed. For example, if the subject is moving fast at a sporting event, the shutter speed will be increased to capture the movement. On the other hand, if the subject is moving slowly, the shutter speed can be decreased to express the movement. This makes it possible to set the optimal shutter speed even for scenes with a lot of movement.

[0091] The photography support system can also analyze the subject's clothing and color tone and automatically adjust the optimal color balance. For example, if the subject is wearing brightly colored clothing, the color balance can be adjusted to make the colors stand out. Also, if the subject is wearing monotone clothing, the color balance can be adjusted to achieve overall harmony. This makes it possible to set the optimal color balance according to the subject's clothing and color tone.

[0092] The photography support system can also analyze the age and gender of the subject and suggest optimal shooting settings. For example, it can suggest bright and soft settings when taking photos of children, and subdued settings when taking photos of seniors. It can also suggest settings that highlight the distinctive features of each person when taking photos of men and women. This makes it possible to suggest optimal shooting settings according to the age and gender of the subject.

[0093] The photography support system can also analyze the subject's pose and suggest the most suitable pose. For example, it can suggest a pose that will make the subject smile naturally. It can also suggest a pose that makes the subject look relaxed. This allows the system to take the best photo based on the subject's pose.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The subject and environment analysis unit analyzes the subject and its surroundings. For example, when taking a landscape photo using a smartphone or camera, the generation AI recognizes elements such as mountains, ocean, and sky, and analyzes how each element is positioned. When taking a portrait, the generation AI recognizes the subject's face and facial expression and analyzes them in conjunction with the surrounding environment. Step 2: The composition suggestion unit proposes the optimal composition based on the results of the analysis of the subject and the environment. For example, for a landscape photo, the generation AI suggests a composition such as "place the subject in the center of the screen and include a natural landscape in the background." For a portrait photo, the generation AI also suggests a composition such as "place the subject on the right side of the screen and include a natural landscape in the background." Step 3: The focus adjustment unit proposes optimal focus settings based on the composition proposed by the composition suggestion unit. For example, for landscape photos, the generation AI suggests settings such as "pull the camera back a little to capture a wider view of the entire landscape." For portraits, the generation AI also suggests settings such as "move the camera closer a little to emphasize the subject's face."

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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]

[0163] 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 subject and environment analysis unit that analyzes the subject and its environment; a composition suggestion unit that suggests an optimal composition based on the results of the analysis by the subject and environment analysis unit; a focus adjustment unit that proposes an optimal focus setting based on the composition proposed by the composition proposal unit. A system characterized by:

2. The subject and environment analysis unit The system analyzes the subject's movements and facial expressions in real time and notifies the user of the best photo opportunity.

2. The system of claim 1.

3. The composition suggestion unit The generative AI proposes multiple compositions and performs further optimization based on the composition selected by the user.

2. The system of claim 1.

4. The side pull adjustment unit is Generative AI measures the size and distance of the subject in real time and automatically adjusts the optimal settings.

2. The system of claim 1.

5. The optimal setting guidance section The emotion estimation function is used to suggest the settings according to the user's emotions, enabling the user to take photos that are emotionally satisfying.

2. The system of claim 1.

6. The high-quality photography department Generative AI automatically analyzes images after shooting and performs optimal editing and correction.

2. The system of claim 1.

7. The subject and environment analysis unit Emotion estimation function is used to analyze the subject's emotions and suggest settings to capture the most emotional moments.

2. The system of claim 1.

8. The composition suggestion unit By using an emotion estimation function, the composition according to the user's emotion is suggested, enabling the user to take an emotionally satisfying photo.

2. The system of claim 1.

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