System

Abstract

An object of the system according to the embodiment is to enable anyone to easily retouch a photograph or create an original filter.SOLUTION: A system according to an embodiment includes a retouch function providing unit, an operation interface unit, and a filter generation unit. The retouch providing module provides a photo retouch using the generated AI. The operation interface unit provides an intuitive operation to the user. The filter generation unit generates the original filter using the generation AI.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, retouching photos and creating original filters required specialized knowledge, making it difficult for average users to operate.

[0005] The system according to the embodiment aims to enable anyone to easily retouch photos and create original filters. [Means for solving the problem]

[0006] The system according to the embodiment includes a retouching function providing unit, an operation interface unit, and a filter generating unit. The retouching function providing unit provides a photo retouching function using a generating AI. The operation interface unit provides an intuitive operation to the user. The filter generating unit creates an original filter using the generating AI. [Effects of the Invention]

[0007] The system according to the embodiment allows anyone to easily retouch photos and create original filters. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The retouching service system according to an embodiment of the present invention provides a photo retouching function using a generative AI, allowing users to modify their own photos to create original images. This allows users to easily create and share original photos.

[0029] A retouching service system according to an embodiment includes a retouching function providing unit, an operation interface unit, and a filter generating unit. The retouching function providing unit provides a photo retouching function using a generation AI. For example, the generation AI performs retouching on photos uploaded by a user, such as blurring the background or brightening skin tones. The generation AI can also apply specific effects based on user instructions. For example, if a user requests that the background of a photo be blurred, the generation AI performs background blurring based on the user's instructions. The operation interface unit provides intuitive operations to the user. For example, the operation interface unit provides buttons and sliders that allow easy use of basic retouching functions such as color correction, brightness adjustment, and contrast adjustment. The operation interface unit also provides a function that allows the user to preview the results of the retouching in real time. For example, when a user moves a slider, the results are immediately reflected in the photo. The filter generating unit creates original filters using the generation AI. For example, if a user requests that a vintage-style filter be created, the generation AI generates a vintage-style filter based on the user's instructions. The generation AI can also create custom filters based on the user's preferences. For example, the generation AI can learn the history of filters used by the user in the past and suggest new filters based on that history. This allows the retouching service system according to the embodiment to allow users to easily create original photos. For example, users can retouch photos of memorable trips to make them more beautiful, or edit and save family photos with special filters. Users can also share their creations on social media to let many people see them.

[0030] The retouching function providing unit can use the generation AI to learn the user's past retouching history and automatically suggest retouching settings optimized for each individual user. For example, the generation AI can learn the user's past retouching history and automatically suggest retouching settings optimized for each individual user. For example, the generation AI can prioritize suggestions for filters and adjustments that the user uses frequently. The generation AI can also customize retouching settings based on the user's preferences. For example, the generation AI can learn the user's preferred color tones and effects and suggest retouching based on those. This makes it possible to suggest optimal retouching settings based on the user's past retouching history.

[0031] The retouching function providing unit uses the generation AI to analyze the content of a photo and automatically perform retouching according to a specific theme. For example, the retouching function providing unit uses the generation AI to analyze the content of a photo and automatically perform retouching according to a specific theme. For example, in the case of a landscape photo, the generation AI performs retouching to make the sky color more vivid. In addition, in the case of a portrait photo, the generation AI can perform retouching to brighten skin tones and blur the background. Furthermore, the generation AI can also perform retouching according to specific events or seasons. For example, warm colors can be applied to Christmas photos and vivid colors to summer photos. This allows retouching according to the content of the photo to be performed automatically.

[0032] The retouching function providing unit can provide a video retouching function using the generation AI, allowing a user to select and retouch specific frames of a video. The retouching function providing unit can, for example, provide a video retouching function using the generation AI, allowing a user to select and retouch specific frames of a video. For example, the generation AI can select a specific scene in a video and adjust the color tone. The generation AI can also apply effects to specific frames of a video. For example, a user can select a specific frame in a video and apply a vintage-style filter to that frame. This allows a specific frame of a video to be retouched.

[0033] The retouching function providing unit can provide a function that uses the generation AI to perform retouching in real time and allows the user to check a preview of the retouched image through a camera. The retouching function providing unit, for example, provides a function that uses the generation AI to perform retouching in real time and allows the user to check a preview of the retouched image through a camera. For example, the generation AI can adjust skin tone in real time through a camera. The generation AI can also perform background blurring in real time. Furthermore, the generation AI can adjust color tone in real time and display the result on a preview screen. This allows the user to check a preview of the retouched image in real time.

[0034] The operation interface unit can provide an interface that learns the user's operation history and automatically arranges frequently used functions as shortcuts. The operation interface unit, for example, learns the user's operation history and provides an interface that automatically arranges frequently used functions as shortcuts. For example, the operation interface unit displays retouch functions that the user uses frequently as shortcuts. The operation interface unit can also customize the arrangement of shortcuts based on the user's preferences. Furthermore, the operation interface unit can analyze the user's operation patterns and suggest optimal shortcuts. This makes it possible to automatically arrange shortcuts based on the user's operation history.

[0035] The operation interface unit can provide an interface that allows a user to issue retouching instructions by voice using voice recognition technology. The operation interface unit can provide an interface that allows a user to issue retouching instructions by voice using voice recognition technology, for example. For example, the operation interface unit can allow a user to perform retouching by voice instruction such as "increase brightness." The operation interface unit can also analyze voice commands and execute specific retouching functions. Furthermore, the operation interface unit can learn the user's voice patterns to improve the accuracy of voice input. This allows a user to issue retouching instructions by voice.

[0036] The operation interface unit can provide an interface using virtual reality or augmented reality, allowing the user to intuitively perform retouching operations. The operation interface unit can provide, for example, an interface using virtual reality (VR), allowing the user to intuitively perform retouching operations. For example, the operation interface unit can retouch photos using a VR headset. The operation interface unit can also provide an interface using augmented reality (AR), allowing the user to perform operations superimposed on real space. For example, the operation interface unit can apply a filter to real scenery using an AR device. This allows the user to intuitively perform retouching operations.

[0037] The operation interface unit provides an interface customization function, allowing the user to change the layout of buttons and sliders to suit their preferences. The operation interface unit, for example, provides an interface customization function, allowing the user to change the layout of buttons and sliders to suit their preferences. For example, the operation interface unit can prioritize the layout of functions that the user uses frequently. The operation interface unit can also hide functions that the user does not need. Furthermore, the operation interface unit can learn the user's operation patterns and suggest optimal layouts. This allows the user to customize the interface.

[0038] The filter generation unit can use generation AI to learn a user's past filter usage history and automatically generate a filter optimized for each individual user. For example, the filter generation unit uses generation AI to learn a user's past filter usage history and automatically generate a filter optimized for each individual user. For example, the generation AI learns the filter patterns that a user frequently uses and suggests a new filter based on that. The generation AI can also generate custom filters based on the user's preferences. This makes it possible to automatically generate an optimal filter based on the user's past filter usage history.

[0039] The filter generation unit can use the generation AI to analyze the content of a photo and automatically generate a filter that matches a specific theme. For example, the generation AI can analyze the content of a photo and automatically generate a filter that matches a specific theme. For example, in the case of a landscape photo, the generation AI can generate a filter that makes the sky color more vivid. In addition, in the case of a portrait photo, the generation AI can generate a filter that brightens the skin tone. Furthermore, the generation AI can generate filters that match specific events or seasons. This makes it possible to automatically generate filters that match the content of the photo.

[0040] The filter generation unit can use the generation AI to create an original filter for a video, allowing the user to apply it to the entire video. The filter generation unit can, for example, use the generation AI to create an original filter for a video, allowing the user to apply it to the entire video. For example, the generation AI can generate a filter that unifies the color tone of the video. The generation AI can also generate a filter to be applied to a specific scene in the video. This makes it possible to create an original filter that can be applied to the entire video.

[0041] The filter generation unit generates a filter in real time using a generation AI, and can provide a function that allows the user to check a preview of the filtered image through the camera. The filter generation unit, for example, generates a filter in real time using a generation AI, and can provide a function that allows the user to check a preview of the filtered image through the camera. For example, the generation AI adjusts the color tone in real time through the camera. The generation AI can also apply effects in real time. Furthermore, the generation AI can generate a filter in real time and display the result on a preview screen. This allows the user to check a preview of the filtered image in real time.

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

[0043] The retouching service system may further include a suggestion unit that learns the user's past retouching history and automatically suggests retouching settings optimized for each individual user. For example, it may be possible to prioritize and suggest filters and adjustments that the user frequently uses. The suggestion unit may also customize retouching settings based on the user's preferences. Furthermore, the suggestion unit may learn the history of filters that the user has used in the past and suggest new filters based on that history. This makes it possible to suggest optimal retouching settings based on the user's past retouching history.

[0044] The retouching service system may further include a suggestion unit that learns the user's past retouching history and automatically suggests retouching settings optimized for each individual user. For example, it may be possible to prioritize and suggest filters and adjustments that the user frequently uses. The suggestion unit may also customize retouching settings based on the user's preferences. Furthermore, the suggestion unit may learn the history of filters that the user has used in the past and suggest new filters based on that history. This makes it possible to suggest optimal retouching settings based on the user's past retouching history.

[0045] The retouching service system may further include a suggestion unit that learns the user's past retouching history and automatically suggests retouching settings optimized for each individual user. For example, it may be possible to prioritize and suggest filters and adjustments that the user frequently uses. The suggestion unit may also customize retouching settings based on the user's preferences. Furthermore, the suggestion unit may learn the history of filters that the user has used in the past and suggest new filters based on that history. This makes it possible to suggest optimal retouching settings based on the user's past retouching history.

[0046] The retouching service system may further include a suggestion unit that learns the user's past retouching history and automatically suggests retouching settings optimized for each individual user. For example, it may be possible to prioritize and suggest filters and adjustments that the user frequently uses. The suggestion unit may also customize retouching settings based on the user's preferences. Furthermore, the suggestion unit may learn the history of filters that the user has used in the past and suggest new filters based on that history. This makes it possible to suggest optimal retouching settings based on the user's past retouching history.

[0047] The retouching service system may further include a suggestion unit that learns the user's past retouching history and automatically suggests retouching settings optimized for each individual user. For example, it may be possible to prioritize and suggest filters and adjustments that the user frequently uses. The suggestion unit may also customize retouching settings based on the user's preferences. Furthermore, the suggestion unit may learn the history of filters that the user has used in the past and suggest new filters based on that history. This makes it possible to suggest optimal retouching settings based on the user's past retouching history.

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

[0049] Step 1: The retouching function provider uses the generation AI to provide a photo retouching function. For example, the generation AI retouches photos uploaded by users by blurring the background or brightening skin tones. The generation AI can also apply specific effects based on user instructions. For example, if a user instructs the AI ​​to "blur the background of this photo," the AI ​​will blur the background based on that instruction. Step 2: The operation interface provides intuitive operations to the user. For example, the operation interface provides buttons and sliders that allow easy use of basic retouching functions such as color correction, brightness adjustment, and contrast adjustment. The operation interface also provides a function that allows the user to preview the results of retouching in real time. For example, when the user moves a slider, the results are immediately reflected in the photo. Step 3: The filter generation unit uses the generation AI to create an original filter. For example, if the user requests a "vintage-style filter," the generation AI generates a vintage-style filter based on that instruction. The generation AI can also create custom filters based on the user's preferences. For example, the generation AI can learn the history of filters the user has used in the past and suggest new filters based on that history.

[0050] (Example 2) The retouching service system according to an embodiment of the present invention provides a photo retouching function using a generative AI, allowing users to modify their own photos to create original images. This allows users to easily create and share original photos.

[0051] A retouching service system according to an embodiment includes a retouching function providing unit, an operation interface unit, and a filter generating unit. The retouching function providing unit provides a photo retouching function using a generation AI. For example, the generation AI performs retouching on photos uploaded by a user, such as blurring the background or brightening skin tones. The generation AI can also apply specific effects based on user instructions. For example, if a user requests that the background of a photo be blurred, the generation AI performs background blurring based on the user's instructions. The operation interface unit provides intuitive operations to the user. For example, the operation interface unit provides buttons and sliders that allow easy use of basic retouching functions such as color correction, brightness adjustment, and contrast adjustment. The operation interface unit also provides a function that allows the user to preview the results of the retouching in real time. For example, when a user moves a slider, the results are immediately reflected in the photo. The filter generating unit creates original filters using the generation AI. For example, if a user requests that a vintage-style filter be created, the generation AI generates a vintage-style filter based on the user's instructions. The generation AI can also create custom filters based on the user's preferences. For example, the generation AI can learn the history of filters used by the user in the past and suggest new filters based on that history. This allows the retouching service system according to the embodiment to allow users to easily create original photos. For example, users can retouch photos of memorable trips to make them more beautiful, or edit and save family photos with special filters. Users can also share their creations on social media to let many people see them.

[0052] The retouching function providing unit can use the generation AI to estimate the user's emotions and make retouching suggestions according to those emotions. For example, the retouching function providing unit uses the generation AI to estimate the user's emotions and make retouching suggestions according to those emotions. For example, if the user is feeling "happy," the generation AI will suggest bright colors and pop filters. On the other hand, if the user is looking for a "calm atmosphere," the generation AI will suggest sepia-toned filters and soft colors. This makes it possible to make retouching suggestions according to the user's emotions.

[0053] The retouching function providing unit can use the generation AI to learn the user's past retouching history and automatically suggest retouching settings optimized for each individual user. For example, the generation AI can learn the user's past retouching history and automatically suggest retouching settings optimized for each individual user. For example, the generation AI can prioritize suggestions for filters and adjustments that the user uses frequently. The generation AI can also customize retouching settings based on the user's preferences. For example, the generation AI can learn the user's preferred color tones and effects and suggest retouching based on those. This makes it possible to suggest optimal retouching settings based on the user's past retouching history.

[0054] The retouching function providing unit uses the generation AI to analyze the content of a photo and automatically perform retouching according to a specific theme. For example, the retouching function providing unit uses the generation AI to analyze the content of a photo and automatically perform retouching according to a specific theme. For example, in the case of a landscape photo, the generation AI performs retouching to make the sky color more vivid. In addition, in the case of a portrait photo, the generation AI can perform retouching to brighten skin tones and blur the background. Furthermore, the generation AI can also perform retouching according to specific events or seasons. For example, warm colors can be applied to Christmas photos and vivid colors to summer photos. This allows retouching according to the content of the photo to be performed automatically.

[0055] The retouching function providing unit can provide a video retouching function using the generation AI, allowing a user to select and retouch specific frames of a video. The retouching function providing unit can, for example, provide a video retouching function using the generation AI, allowing a user to select and retouch specific frames of a video. For example, the generation AI can select a specific scene in a video and adjust the color tone. The generation AI can also apply effects to specific frames of a video. For example, a user can select a specific frame in a video and apply a vintage-style filter to that frame. This allows a specific frame of a video to be retouched.

[0056] The retouching function providing unit can provide a function that uses the generation AI to perform retouching in real time and allows the user to check a preview of the retouched image through a camera. The retouching function providing unit, for example, provides a function that uses the generation AI to perform retouching in real time and allows the user to check a preview of the retouched image through a camera. For example, the generation AI can adjust skin tone in real time through a camera. The generation AI can also perform background blurring in real time. Furthermore, the generation AI can adjust color tone in real time and display the result on a preview screen. This allows the user to check a preview of the retouched image in real time.

[0057] The retouching function providing unit can use the emotion estimation function to detect stress or dissatisfaction the user feels during retouching and provide support or guidance accordingly. The retouching function providing unit can, for example, use the emotion estimation function to detect stress or dissatisfaction the user feels during retouching and provide support or guidance accordingly. For example, the emotion estimation function can analyze the user's facial expression and display a tutorial if the user feels stressed. The emotion estimation function can also analyze the user's voice and display a help message if the user feels dissatisfied. Furthermore, the emotion estimation function can analyze the user's operation pattern and present operation procedures if the user is having trouble. This makes it possible to provide support or guidance according to the user's stress or dissatisfaction.

[0058] The operation interface unit can use the emotion estimation function to analyze the user's emotion during operation in real time and dynamically adjust the interface that the user finds most comfortable. The operation interface unit can, for example, use the emotion estimation function to analyze the user's emotion during operation in real time and dynamically adjust the interface that the user finds most comfortable. For example, the emotion estimation function can analyze the user's facial expression and simplify the interface if the user is feeling stressed. The emotion estimation function can also analyze the user's voice and customize the interface if the user is feeling comfortable. Furthermore, the emotion estimation function can analyze the user's operation pattern and provide an optimal interface. This makes it possible to dynamically adjust the interface according to the user's emotion.

[0059] The operation interface unit can provide an interface that learns the user's operation history and automatically arranges frequently used functions as shortcuts. The operation interface unit, for example, learns the user's operation history and provides an interface that automatically arranges frequently used functions as shortcuts. For example, the operation interface unit displays retouch functions that the user uses frequently as shortcuts. The operation interface unit can also customize the arrangement of shortcuts based on the user's preferences. Furthermore, the operation interface unit can analyze the user's operation patterns and suggest optimal shortcuts. This makes it possible to automatically arrange shortcuts based on the user's operation history.

[0060] The operation interface unit can provide an interface that allows a user to issue retouching instructions by voice using voice recognition technology. The operation interface unit can provide an interface that allows a user to issue retouching instructions by voice using voice recognition technology, for example. For example, the operation interface unit can allow a user to perform retouching by voice instruction such as "increase brightness." The operation interface unit can also analyze voice commands and execute specific retouching functions. Furthermore, the operation interface unit can learn the user's voice patterns to improve the accuracy of voice input. This allows a user to issue retouching instructions by voice.

[0061] The operation interface unit can provide an interface using virtual reality or augmented reality, allowing the user to intuitively perform retouching operations. The operation interface unit can provide, for example, an interface using virtual reality (VR), allowing the user to intuitively perform retouching operations. For example, the operation interface unit can retouch photos using a VR headset. The operation interface unit can also provide an interface using augmented reality (AR), allowing the user to perform operations superimposed on real space. For example, the operation interface unit can apply a filter to real scenery using an AR device. This allows the user to intuitively perform retouching operations.

[0062] The operation interface unit provides an interface customization function, allowing the user to change the layout of buttons and sliders to suit their preferences. The operation interface unit, for example, provides an interface customization function, allowing the user to change the layout of buttons and sliders to suit their preferences. For example, the operation interface unit can prioritize the layout of functions that the user uses frequently. The operation interface unit can also hide functions that the user does not need. Furthermore, the operation interface unit can learn the user's operation patterns and suggest optimal layouts. This allows the user to customize the interface.

[0063] The operation interface unit uses the emotion estimation function to provide real-time feedback on the level of satisfaction felt by the user during operation, which can be useful for improving the interface. The operation interface unit uses, for example, the emotion estimation function to provide real-time feedback on the level of satisfaction felt by the user during operation, which can be useful for improving the interface. For example, the emotion estimation function analyzes the user's facial expression and evaluates the level of satisfaction. The emotion estimation function can also analyze the user's voice and evaluate the level of satisfaction. Furthermore, the emotion estimation function can analyze the user's operation pattern and evaluate the level of satisfaction. This makes it possible to improve the interface based on the user's satisfaction.

[0064] The filter generation unit can use the generation AI to estimate the user's emotions and automatically generate an original filter that corresponds to the emotion. For example, the generation AI can estimate the user's emotions and automatically generate an original filter that corresponds to the emotion. For example, if the user feels "nostalgic," the generation AI can suggest a sepia-toned filter. Also, if the user feels "happy," the generation AI can suggest a bright-toned filter. Furthermore, the generation AI can generate a custom filter based on the user's emotions. This makes it possible to automatically generate an original filter that corresponds to the user's emotions.

[0065] The filter generation unit can use generation AI to learn a user's past filter usage history and automatically generate a filter optimized for each individual user. For example, the filter generation unit uses generation AI to learn a user's past filter usage history and automatically generate a filter optimized for each individual user. For example, the generation AI learns the filter patterns that a user frequently uses and suggests a new filter based on that. The generation AI can also generate custom filters based on the user's preferences. This makes it possible to automatically generate an optimal filter based on the user's past filter usage history.

[0066] The filter generation unit can use the generation AI to analyze the content of a photo and automatically generate a filter that matches a specific theme. For example, the generation AI can analyze the content of a photo and automatically generate a filter that matches a specific theme. For example, in the case of a landscape photo, the generation AI can generate a filter that makes the sky color more vivid. In addition, in the case of a portrait photo, the generation AI can generate a filter that brightens the skin tone. Furthermore, the generation AI can generate filters that match specific events or seasons. This makes it possible to automatically generate filters that match the content of the photo.

[0067] The filter generation unit can use the generation AI to create an original filter for a video, allowing the user to apply it to the entire video. The filter generation unit can, for example, use the generation AI to create an original filter for a video, allowing the user to apply it to the entire video. For example, the generation AI can generate a filter that unifies the color tone of the video. The generation AI can also generate a filter to be applied to a specific scene in the video. This makes it possible to create an original filter that can be applied to the entire video.

[0068] The filter generation unit generates a filter in real time using a generation AI, and can provide a function that allows the user to check a preview of the filtered image through the camera. The filter generation unit, for example, generates a filter in real time using a generation AI, and can provide a function that allows the user to check a preview of the filtered image through the camera. For example, the generation AI adjusts the color tone in real time through the camera. The generation AI can also apply effects in real time. Furthermore, the generation AI can generate a filter in real time and display the result on a preview screen. This allows the user to check a preview of the filtered image in real time.

[0069] The filter generation unit can use the emotion estimation function to detect stress or frustration felt by the user while creating a filter, and provide support or guidance accordingly. The filter generation unit can, for example, use the emotion estimation function to detect stress or frustration felt by the user while creating a filter, and provide support or guidance accordingly. For example, the emotion estimation function can analyze the user's facial expression and display a tutorial if the user feels stressed. The emotion estimation function can also analyze the user's voice and display a help message if the user feels frustrated. Furthermore, the emotion estimation function can analyze the user's operation patterns and present operating procedures if the user is having trouble. This makes it possible to provide support or guidance according to the user's stress or frustration.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The retouching service system may further include a suggestion unit that estimates the user's emotions and suggests retouching based on the estimated emotions. For example, if the user feels "happy," the suggestion unit can suggest bright colors and pop filters. If the user desires a "calm atmosphere," the suggestion unit can suggest sepia-toned filters and soft colors. If the user feels "nostalgic," the suggestion unit can suggest sepia-toned filters and vintage-style effects. This makes it possible to suggest retouching based on the user's emotions.

[0072] The retouching service system may further include a suggestion unit that estimates the user's emotions and suggests retouching based on the estimated emotions. For example, if the user feels "happy," the suggestion unit can suggest bright colors and pop filters. If the user desires a "calm atmosphere," the suggestion unit can suggest sepia-toned filters and soft colors. If the user feels "nostalgic," the suggestion unit can suggest sepia-toned filters and vintage-style effects. This makes it possible to suggest retouching based on the user's emotions.

[0073] The retouching service system may further include a suggestion unit that estimates the user's emotions and suggests retouching based on the estimated emotions. For example, if the user feels "happy," the suggestion unit can suggest bright colors and pop filters. If the user desires a "calm atmosphere," the suggestion unit can suggest sepia-toned filters and soft colors. If the user feels "nostalgic," the suggestion unit can suggest sepia-toned filters and vintage-style effects. This makes it possible to suggest retouching based on the user's emotions.

[0074] The retouching service system may further include a suggestion unit that estimates the user's emotions and suggests retouching based on the estimated emotions. For example, if the user feels "happy," the suggestion unit can suggest bright colors and pop filters. If the user desires a "calm atmosphere," the suggestion unit can suggest sepia-toned filters and soft colors. If the user feels "nostalgic," the suggestion unit can suggest sepia-toned filters and vintage-style effects. This makes it possible to suggest retouching based on the user's emotions.

[0075] The retouching service system may further include a suggestion unit that estimates the user's emotions and suggests retouching based on the estimated emotions. For example, if the user feels "happy," the suggestion unit can suggest bright colors and pop filters. If the user desires a "calm atmosphere," the suggestion unit can suggest sepia-toned filters and soft colors. If the user feels "nostalgic," the suggestion unit can suggest sepia-toned filters and vintage-style effects. This makes it possible to suggest retouching based on the user's emotions.

[0076] The retouching service system may further include a suggestion unit that learns the user's past retouching history and automatically suggests retouching settings optimized for each individual user. For example, it may be possible to prioritize and suggest filters and adjustments that the user frequently uses. The suggestion unit may also customize retouching settings based on the user's preferences. Furthermore, the suggestion unit may learn the history of filters that the user has used in the past and suggest new filters based on that history. This makes it possible to suggest optimal retouching settings based on the user's past retouching history.

[0077] The retouching service system may further include a suggestion unit that learns the user's past retouching history and automatically suggests retouching settings optimized for each individual user. For example, it may be possible to prioritize and suggest filters and adjustments that the user frequently uses. The suggestion unit may also customize retouching settings based on the user's preferences. Furthermore, the suggestion unit may learn the history of filters that the user has used in the past and suggest new filters based on that history. This makes it possible to suggest optimal retouching settings based on the user's past retouching history.

[0078] The retouching service system may further include a suggestion unit that learns the user's past retouching history and automatically suggests retouching settings optimized for each individual user. For example, it may be possible to prioritize and suggest filters and adjustments that the user frequently uses. The suggestion unit may also customize retouching settings based on the user's preferences. Furthermore, the suggestion unit may learn the history of filters that the user has used in the past and suggest new filters based on that history. This makes it possible to suggest optimal retouching settings based on the user's past retouching history.

[0079] The retouching service system may further include a suggestion unit that learns the user's past retouching history and automatically suggests retouching settings optimized for each individual user. For example, it may be possible to prioritize and suggest filters and adjustments that the user frequently uses. The suggestion unit may also customize retouching settings based on the user's preferences. Furthermore, the suggestion unit may learn the history of filters that the user has used in the past and suggest new filters based on that history. This makes it possible to suggest optimal retouching settings based on the user's past retouching history.

[0080] The retouching service system may further include a suggestion unit that learns the user's past retouching history and automatically suggests retouching settings optimized for each individual user. For example, it may be possible to prioritize and suggest filters and adjustments that the user frequently uses. The suggestion unit may also customize retouching settings based on the user's preferences. Furthermore, the suggestion unit may learn the history of filters that the user has used in the past and suggest new filters based on that history. This makes it possible to suggest optimal retouching settings based on the user's past retouching history.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The retouching function provider uses the generation AI to provide a photo retouching function. For example, the generation AI retouches photos uploaded by users by blurring the background or brightening skin tones. The generation AI can also apply specific effects based on user instructions. For example, if a user instructs the AI ​​to "blur the background of this photo," the AI ​​will blur the background based on that instruction. Step 2: The operation interface provides intuitive operations to the user. For example, the operation interface provides buttons and sliders that allow easy use of basic retouching functions such as color correction, brightness adjustment, and contrast adjustment. The operation interface also provides a function that allows the user to preview the results of retouching in real time. For example, when the user moves a slider, the results are immediately reflected in the photo. Step 3: The filter generation unit uses the generation AI to create an original filter. For example, if the user requests a "vintage-style filter," the generation AI generates a vintage-style filter based on that instruction. The generation AI can also create custom filters based on the user's preferences. For example, the generation AI can learn the history of filters the user has used in the past and suggest new filters based on that history.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 retouching function providing unit that provides a photo retouching function using generative AI; an operation interface unit that provides an intuitive operation to a user; A filter generation unit that creates an original filter using generation AI. A system characterized by:

2. The retouch function providing unit The generation AI learns the user's past retouching history and automatically proposes retouching settings optimized for each individual user.

2. The system of claim 1.

3. The retouch function providing unit The generation AI is used to perform retouching in real time, and the user is provided with a function that allows them to check a preview of the retouched image through a camera.

2. The system of claim 1.

4. The operation interface unit The user's emotions during operation are analyzed in real time, and the interface that the user feels most comfortable with is dynamically adjusted.

2. The system of claim 1.

5. The operation interface unit An interface using virtual reality or augmented reality is provided, allowing the user to intuitively perform retouching operations.

2. The system of claim 1.

6. The filter generation unit The generation AI is used to estimate the user's emotions, and the original filter is automatically generated according to the emotions.

2. The system of claim 1.

7. The filter generation unit Using the generative AI to create the original filter for a video, allowing the user to apply it to the entire video.

2. The system of claim 1.

8. The filter generation unit Detecting stress or frustration felt by the user during filter creation and providing support or guidance accordingly 2. The system of claim 1.

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