System

Abstract

A system in accordance with an embodiment is directed to allowing a user to generate a high resolution customizable image from text and apply fine adjustments and multiple styles.SOLUTION: A system according to an embodiment includes a text input unit, a generation unit, an adjustment unit, and a style application unit. The text input unit receives a text of a user. The generation unit analyzes the text received by the text input unit and generates a corresponding high-resolution image. The adjustment unit performs fine adjustment of the image generated by the generation unit. The style application unit applies a plurality of styles to the image adjusted by the adjustment unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Previous techniques have made it difficult for users to generate high-resolution, customizable images from text and apply fine adjustments and multiple styles.

[0005] The system of the embodiment aims to enable users to generate high-resolution customizable images from text, with fine adjustments and multiple styles applied. [Means for solving the problem]

[0006] The system according to the embodiment includes a text input unit, a generation unit, an adjustment unit, and a style application unit. The text input unit accepts text from a user. The generation unit analyzes the text accepted by the text input unit and generates a corresponding high-resolution image. The adjustment unit fine-tunes the image generated by the generation unit. The style application unit applies multiple styles to the image adjusted by the adjustment unit. [Effects of the Invention]

[0007] Embodiments of the system allow users to generate high-resolution, customizable images from text, with fine adjustments and multiple styles applied. [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 image generation system according to the present invention allows users to generate high-resolution, customizable images from text, and then fine-tune and style them, allowing users to easily create the image of their dreams.

[0029] An image generation system according to an embodiment includes a text input unit, a generation unit, an adjustment unit, and a style application unit. The text input unit accepts user text. For example, it can accept sentences or keywords entered by the user. The text input unit can also accept voice input. The generation unit analyzes the text accepted by the text input unit and generates a corresponding high-resolution image. For example, the generation AI analyzes the text using a text generation AI (e.g., LLM) and generates an image. The generation AI can also analyze both the text and the image and generate an image using a multimodal generation AI. The adjustment unit makes fine adjustments to the image generated by the generation unit. For example, the adjustment unit can adjust the hue, brightness, and contrast of the image. The adjustment unit can also change only specific parts. For example, it can change the color of trees in a landscape image or change the color of the sky to a sunset. The style application unit applies various styles to the image adjusted by the adjustment unit. For example, the style application unit can select a style such as an oil painting style, an illustration style, or a watercolor style and apply it to the image. This allows users to generate high-resolution, customizable images from text, allowing them to fine-tune and style them. For example, images can be easily created for advertisements and presentations, and can also be used for personal creative activities.

[0030] The generator can understand the context of the text in detail and integrate multiple scenes and situations into a single image. For example, the generator uses a generative AI to analyze the context of the text and integrate different scenes into a single image. For example, for the text "beach sunset and mountain scenery," the generator can generate an image that combines a beach sunset and a mountain scenery. The generator can also evaluate the importance of scenes and situations based on the context of the text and highlight important scenes. For example, for the text "urban night view and quiet park," the generator can generate an image depicting a quiet park against a backdrop of urban night scenery. The generator can also analyze the relationships between scenes and situations and combine related scenes. For example, for the text "historic buildings and modern cityscapes," the generator can generate an image that harmonizes historical buildings and modern cityscapes. This allows the generator to integrate multiple scenes and situations into a single image, providing more complex and detailed images.

[0031] The generation unit can generate images from different viewpoints based on the content of the text and allow the user to select from them. For example, the generation unit generates images from different viewpoints, such as a bird's-eye view or a ground-level view, based on the content of the text using a generation AI. For example, for the text "cityscape," images from both a bird's-eye view and a ground-level view are generated. The generation unit can also generate images from viewpoints of different times of day or seasons based on the content of the text. For example, for the text "cherry blossoms in spring and autumn leaves," images of both spring cherry blossoms and autumn leaves are generated. The generation unit can also regenerate images based on the viewpoint selected by the user. For example, if the user selects "night view," an image from a night view viewpoint is generated. This allows the user to select the optimal viewpoint by generating images from different viewpoints.

[0032] The text input unit can accept voice input, and the generation unit can analyze the voice input to generate an image. The text input unit, for example, analyzes what the user is saying in real time and generates an image based on the voice input. For example, if a user says, "A landscape with blue skies and white clouds," an image based on that content is generated. The text input unit can also convert voice into text using voice recognition technology. For example, voice recognition software automatically analyzes the voice and saves it as text. The generation unit analyzes the voice input and generates an image in the same way as the text input. For example, a generation AI generates an image based on the voice input. The generation unit can also analyze the emotion of the voice input and generate an image based on the emotion. For example, a voice input with a positive emotion generates an image with bright colors. This allows users to generate images more intuitively using voice input.

[0033] The generation unit can animate images generated from text to provide dynamic visual content. For example, the generation unit uses a generation AI to animate still images based on the content of the text. For example, for the text "Trees swaying in the wind," the generation unit generates an animation of trees swaying in the wind. The generation unit can also generate an animation that continuously displays multiple scenes based on the content of the text. For example, for the text "Scenery from sunrise to sunset," the generation unit generates an animation that continuously displays scenery from sunrise to sunset. The generation unit can also apply animation effects specified by the user. For example, it applies effects such as fade-in and fade-out, zoom-in and zoom-out. This allows for animation to provide more dynamic and attractive visual content.

[0034] The adjustment unit can select a specific portion of an image and convert only that portion into a different style. For example, the adjustment unit can select a specific portion of an image and convert only that portion into an oil painting style. For example, trees in a landscape image can be converted into an oil painting style. The adjustment unit can also select a specific portion of an image and convert only that portion into an illustration style. For example, buildings in a landscape image can be converted into an illustration style. The adjustment unit can also select a specific portion of an image and convert only that portion into a watercolor style. For example, the sky in a landscape image can be converted into a watercolor style. In this way, by converting specific portions of an image into different styles, it is possible to provide images with a higher level of customization.

[0035] The adjustment unit can automatically suggest adjustments based on text specified by a user. For example, the adjustment unit automatically suggests adjustments to the hue and brightness of an image based on text specified by a user. For example, for the text "sunset landscape," it suggests adjusting to a warmer color tone. The adjustment unit can also automatically suggest adjustments to the contrast and saturation of an image based on text specified by a user. For example, for the text "vivid flowers," it suggests adjustments to increase the contrast and saturation. The adjustment unit can also automatically suggest adjustments to specific parts of an image based on text specified by a user. For example, for the text "blue sky," it suggests adjusting the color tone of the sky. This allows for more efficient image adjustments by automatically suggesting adjustments based on text specified by a user.

[0036] The adjustment unit can learn the user's past adjustment history and automatically optimize adjustments from the next time onwards. For example, the adjustment unit uses a generative AI to learn the user's past adjustment history and automatically optimize adjustments from the next time onwards. For example, it can suggest adjustments based on the color tones and styles that the user has used in the past. The adjustment unit can also store the user's past adjustment history in a database and learn from it using a machine learning algorithm. For example, it can analyze the patterns of adjustments the user has made in the past and reflect them in adjustments from the next time onwards. The adjustment unit can also suggest adjustments in real time based on the user's past adjustment history. For example, while the user is adjusting an image, it can suggest optimal adjustments based on the past adjustment history. In this way, by learning the user's past adjustment history, adjustments can be made more efficiently from the next time onwards.

[0037] The style application unit can combine and apply multiple styles, allowing the user to create an original style. The style application unit, for example, provides a function that allows the user to select multiple styles and combine them to create an original style. For example, a style that combines an oil painting style and a watercolor painting style can be created. The style application unit can also provide an interface that allows the user to customize the color tone and texture of the style. For example, the user can use a color palette or texture selection tool. The style application unit can also provide a function that allows the user to save and reuse the original style created by the user. For example, the user can save the style created by the user as a template and apply it to other images. This allows the user to create an original style by combining multiple styles.

[0038] The style application unit can display a comparison of images before and after application of a style in real time, allowing the user to instantly confirm the differences. The style application unit, for example, displays images before and after application of a style side by side, providing an interface that allows the user to instantly confirm the differences. For example, the images before and after application are compared using a slider. The style application unit can also display a preview of images before and after application of a style in real time. For example, when a style is selected, the image after application is immediately displayed. The style application unit can also display images before and after application of a style side by side, allowing the user to visually compare them. For example, the image before application and the image after application are displayed side by side. This allows the user to instantly confirm the differences by displaying a comparison of images before and after application of a style in real time.

[0039] The style application unit can add a function that allows a user to customize a style by himself / herself. For example, the style application unit provides an interface that allows a user to customize the color tone or texture of a style. For example, by using a color palette or a texture selection tool. The style application unit can also provide a function that allows a user to adjust style parameters. For example, by adjusting the thickness or transparency of a brush. The style application unit can also provide a function that allows a user to save and reuse a style customized by a user. For example, a style customized by a user can be saved as a template and applied to other images. This allows a user to customize a style by himself / herself, thereby providing more personalized images.

[0040] The style application unit can add a function to learn styles from different cultures and eras and apply them. For example, the style application unit provides a function that allows the generation AI to learn styles from different cultures and apply them. For example, applying a Japanese ukiyo-e style or a French impressionist style. The style application unit can also learn styles from different eras and apply them. For example, applying a Renaissance painting style or a modern art style. The style application unit can also regenerate images based on the style of a culture or era selected by the user. For example, if a user selects "Japanese ukiyo-e style," an image based on that style is generated. This allows for the provision of a wider variety of images by applying styles from different cultures and eras.

[0041] The interface may be added with a voice operation function, allowing the user to issue commands by voice. For example, the interface may be added with a voice operation function, allowing the user to issue commands by voice. For example, when the user says, "Change the color to blue," the color of the image changes to blue. The interface may also use voice recognition technology to analyze voice commands and execute corresponding operations. For example, voice recognition software automatically analyzes the voice and executes operations according to the instructions. The interface may also use the voice operation function to execute multiple operations in succession. For example, when the user says, "Save the image and open the next image," the interface will save the image and open the next image in succession. Thus, adding the voice operation function allows the user to operate the interface more intuitively.

[0042] The interface may incorporate virtual reality (VR) technology to enable a user to generate and adjust an image in a virtual space. For example, the interface may incorporate virtual reality (VR) technology to enable a user to generate an image in a virtual space. For example, an image may be generated in a virtual space using a VR headset. The interface may also use virtual reality technology to enable a user to adjust the image in the virtual space. For example, an image may be adjusted in color or brightness using a VR controller. The interface may also use virtual reality technology to enable a user to select and modify a specific portion of the image in the virtual space. For example, an image may be modified by clicking on a specific portion of the image in the virtual space. In this way, the introduction of virtual reality technology allows a user to generate and adjust images in a more immersive environment.

[0043] The interface may incorporate augmented reality (AR) technology to enable an image generated in the real world to be superimposed on the real world. For example, the interface may incorporate augmented reality (AR) technology to enable an image generated in the real world to be superimposed on the real world. For example, the image generated in the real world may be superimposed and displayed using a smartphone camera. The interface may also use augmented reality technology to enable a user to manipulate the image generated in the real world. For example, the user may tap the smartphone screen to adjust the position of the image superimposed on the real world. The interface may also use augmented reality technology to enable a user to save and share the image generated in the real world. For example, the image superimposed on the real world may be saved as a screenshot and shared on a social networking site. In this way, the introduction of augmented reality technology enables an image generated in the real world to be superimposed on the real world.

[0044] The generation unit can display the generation process of the high-resolution image in real time, allowing the user to check the progress. The generation unit, for example, displays the generation process of the high-resolution image in real time and provides an interface that allows the user to check the progress. For example, the image being generated can be displayed together with a progress bar. The generation unit can also display each step of the generation process in detail. For example, each step of image analysis, generation, and adjustment can be displayed. The generation unit can also update the progress of the generation process in real time and notify the user. For example, the progress of the generation process can be notified by a pop-up message. In this way, the user can check the progress by displaying the generation process of the high-resolution image in real time.

[0045] The generation unit can add a function that allows the user to make more detailed adjustments to the high-resolution image after it has been generated. For example, the generation unit provides an interface that allows the user to make fine adjustments to color tone, brightness, contrast, and the like after the high-resolution image has been generated. For example, adjustments are made using sliders. The generation unit can also adjust specific parts of the generated image. For example, a part of the image can be selected and the color tone and brightness of only that part can be adjusted. The generation unit can also apply a style to the generated image. For example, an oil painting style or watercolor painting style can be applied to the generated image. This allows for fine adjustments to be made after the high-resolution image has been generated, enabling more detailed customization.

[0046] The generation unit can add a function for simultaneously generating high-resolution images at different resolutions and formats. For example, the generation unit provides a function for simultaneously generating high-resolution images at different resolutions and formats. For example, images at 4K resolution and 1080p resolution are generated simultaneously. The generation unit can also simultaneously generate images in different formats. For example, images in JPEG format and PNG format are generated simultaneously. The generation unit can also generate images based on a resolution and format specified by the user. For example, multiple images are generated simultaneously based on a resolution and format specified by the user. This allows for simultaneous generation of images at different resolutions and formats to meet the diverse needs of users.

[0047] The generation unit can add a function to generate an image optimized for a purpose specified by a user when generating a high-resolution image. The generation unit provides a function to generate an image optimized for a purpose specified by a user when generating a high-resolution image, for example. For example, a high-resolution image for a poster is generated. The generation unit can also generate a high-resolution image for a website. For example, an image for a website banner is generated. The generation unit can also generate a high-resolution image for a presentation. For example, an image for a presentation slide is generated. In this way, by generating an image optimized for a purpose specified by a user, a more appropriate image can be provided.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The generation unit can learn the user's past generation history and automatically optimize subsequent generations. For example, it can suggest a generation based on the color tone or style that the user has used in the past. The generation unit can also store the user's past generation history in a database and learn using a machine learning algorithm. For example, it can analyze the patterns of images the user has generated in the past and reflect them in subsequent generations. The generation unit can also suggest a generation in real time based on the user's past generation history. For example, while the user is generating an image, it can suggest the optimal generation based on the past generation history. In this way, by learning the user's past generation history, subsequent generations can be performed more efficiently.

[0050] The generation unit can add a function to learn styles from different cultures and eras and apply them. For example, the generation AI can learn styles from different cultures and apply them. For example, it can apply a Japanese ukiyo-e style or a French impressionist style. The generation unit can also learn styles from different eras and apply them. For example, it can apply a Renaissance painting style or a modern art style. The generation unit can also regenerate images based on the style of a culture or era selected by the user. For example, if a user selects "Japanese ukiyo-e style," it generates an image based on that style. This allows the application of styles from different cultures and eras to provide a wider variety of images.

[0051] The generation unit can display the generation process of the high-resolution image in real time, allowing the user to check the progress. For example, the generation process of the high-resolution image can be displayed in real time, providing an interface that allows the user to check the progress. For example, the image being generated can be displayed together with a progress bar. The generation unit can also display each step of the generation process in detail. For example, each step of image analysis, generation, and adjustment can be displayed. The generation unit can also update the progress of the generation process in real time and notify the user. For example, the progress of the generation process can be notified by a pop-up message. In this way, the generation process of the high-resolution image can be displayed in real time, allowing the user to check the progress.

[0052] The generation unit can add a function for simultaneously generating high-resolution images at different resolutions and formats. For example, a function for simultaneously generating high-resolution images at different resolutions and formats is provided. For example, images at 4K resolution and 1080p resolution are generated simultaneously. The generation unit can also simultaneously generate images in different formats. For example, images in JPEG format and PNG format are generated simultaneously. The generation unit can also generate images based on a resolution and format specified by the user. For example, multiple images are generated simultaneously based on a resolution and format specified by the user. This allows the various needs of users to be met by simultaneously generating images at different resolutions and formats.

[0053] The generation unit can add a function to generate an image optimized for a purpose specified by a user when generating a high-resolution image. For example, a function to generate an image optimized for a purpose specified by a user when generating a high-resolution image is provided. For example, a high-resolution image for a poster is generated. The generation unit can also generate a high-resolution image for a website. For example, an image for a website banner is generated. The generation unit can also generate a high-resolution image for a presentation. For example, an image for a presentation slide is generated. In this way, by generating an image optimized for a purpose specified by a user, a more appropriate image can be provided.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The text input unit accepts text from the user. For example, it can accept sentences or keywords entered by the user. The text input unit can also accept voice input. Step 2: The generation unit analyzes the text received by the text input unit and generates a corresponding high-resolution image. For example, the generation AI may use a text generation AI (e.g., LLM) to analyze the text and generate an image. The generation AI may also use a multimodal generation AI to analyze both the text and the image and generate an image. Step 3: The adjustment unit makes fine adjustments to the image generated by the generation unit. For example, the adjustment unit can adjust the color, brightness, and contrast of the image. The adjustment unit can also change only specific parts. For example, the adjustment unit can change the color of trees in a landscape image or change the color of the sky to a sunset. Step 4: The style application unit applies various styles to the image adjusted by the adjustment unit. For example, the style application unit can select a style such as oil painting, illustration, or watercolor painting and apply it to the image.

[0056] (Example 2) The image generation system according to the present invention allows users to generate high-resolution, customizable images from text, and then fine-tune and style them, allowing users to easily create the image of their dreams.

[0057] An image generation system according to an embodiment includes a text input unit, a generation unit, an adjustment unit, and a style application unit. The text input unit accepts user text. For example, it can accept sentences or keywords entered by the user. The text input unit can also accept voice input. The generation unit analyzes the text accepted by the text input unit and generates a corresponding high-resolution image. For example, the generation AI analyzes the text using a text generation AI (e.g., LLM) and generates an image. The generation AI can also analyze both the text and the image and generate an image using a multimodal generation AI. The adjustment unit makes fine adjustments to the image generated by the generation unit. For example, the adjustment unit can adjust the hue, brightness, and contrast of the image. The adjustment unit can also change only specific parts. For example, it can change the color of trees in a landscape image or change the color of the sky to a sunset. The style application unit applies various styles to the image adjusted by the adjustment unit. For example, the style application unit can select a style such as an oil painting style, an illustration style, or a watercolor style and apply it to the image. This allows users to generate high-resolution, customizable images from text, allowing them to fine-tune and style them. For example, images can be easily created for advertisements and presentations, and can also be used for personal creative activities.

[0058] The generation unit can analyze the sentiment of the text and generate an image based on the sentiment. For example, the generation unit uses a generative AI to analyze the sentiment of the text and generate an image with bright colors for text with a positive sentiment. For example, for the text "fun picnic," it generates an image of bright sunshine and smiling people. The generation unit can also generate an image with dark colors for text with a negative sentiment. For example, for the text "stormy night," it generates an image of dark clouds and heavy rain. The generation unit can also adjust the color tone and brightness of the image according to the intensity of the sentiment. For example, for text with a strong sentiment of joy, it generates an image with more vivid colors. In this way, by generating images based on the sentiment of the text, it is possible to provide images that appeal to more emotions.

[0059] The generator can understand the context of the text in detail and integrate multiple scenes and situations into a single image. For example, the generator uses a generative AI to analyze the context of the text and integrate different scenes into a single image. For example, for the text "beach sunset and mountain scenery," the generator can generate an image that combines a beach sunset and a mountain scenery. The generator can also evaluate the importance of scenes and situations based on the context of the text and highlight important scenes. For example, for the text "urban night view and quiet park," the generator can generate an image depicting a quiet park against a backdrop of urban night scenery. The generator can also analyze the relationships between scenes and situations and combine related scenes. For example, for the text "historic buildings and modern cityscapes," the generator can generate an image that harmonizes historical buildings and modern cityscapes. This allows the generator to integrate multiple scenes and situations into a single image, providing more complex and detailed images.

[0060] The generation unit can generate images from different viewpoints based on the content of the text and allow the user to select from them. For example, the generation unit generates images from different viewpoints, such as a bird's-eye view or a ground-level view, based on the content of the text using a generation AI. For example, for the text "cityscape," images from both a bird's-eye view and a ground-level view are generated. The generation unit can also generate images from viewpoints of different times of day or seasons based on the content of the text. For example, for the text "cherry blossoms in spring and autumn leaves," images of both spring cherry blossoms and autumn leaves are generated. The generation unit can also regenerate images based on the viewpoint selected by the user. For example, if the user selects "night view," an image from a night view viewpoint is generated. This allows the user to select the optimal viewpoint by generating images from different viewpoints.

[0061] The text input unit can accept voice input, and the generation unit can analyze the voice input to generate an image. The text input unit, for example, analyzes what the user is saying in real time and generates an image based on the voice input. For example, if a user says, "A landscape with blue skies and white clouds," an image based on that content is generated. The text input unit can also convert voice into text using voice recognition technology. For example, voice recognition software automatically analyzes the voice and saves it as text. The generation unit analyzes the voice input and generates an image in the same way as the text input. For example, a generation AI generates an image based on the voice input. The generation unit can also analyze the emotion of the voice input and generate an image based on the emotion. For example, a voice input with a positive emotion generates an image with bright colors. This allows users to generate images more intuitively using voice input.

[0062] The generation unit can animate images generated from text to provide dynamic visual content. For example, the generation unit uses a generation AI to animate still images based on the content of the text. For example, for the text "Trees swaying in the wind," the generation unit generates an animation of trees swaying in the wind. The generation unit can also generate an animation that continuously displays multiple scenes based on the content of the text. For example, for the text "Scenery from sunrise to sunset," the generation unit generates an animation that continuously displays scenery from sunrise to sunset. The generation unit can also apply animation effects specified by the user. For example, it applies effects such as fade-in and fade-out, zoom-in and zoom-out. This allows for animation to provide more dynamic and attractive visual content.

[0063] The generation unit can use the emotion estimation function to analyze the emotion of text entered by a user and generate an image based on that emotion. For example, the generation unit uses the emotion estimation function to generate an image based on that emotion. For example, for the text "fun picnic," the generation unit generates an image depicting bright sunshine and smiling people. The generation unit can also use the emotion estimation function to generate an image with dark tones for text with negative emotions. For example, for the text "stormy night," the generation unit generates an image depicting dark clouds and heavy rain. The generation unit can also adjust the color tone and brightness of the image according to the intensity of the emotion. For example, for text with a strong emotion of joy, the generation unit generates an image with more vivid colors. In this way, the emotion estimation function can be used to generate images that correspond to the user's emotions.

[0064] The adjustment unit can analyze the user's emotions and automatically adjust the color tone and brightness based on the emotion. For example, the adjustment unit uses a generation AI to analyze the user's emotions and provide images with brighter colors and higher brightness to users with positive emotions. For example, it generates images with brighter colors for users with happy emotions. The adjustment unit can also provide images with darker colors and lower brightness to users with negative emotions. For example, it generates images with darker colors for users with sad emotions. The adjustment unit can also adjust the color tone and brightness according to the intensity of the emotion. For example, it generates images with more vivid colors for users with strong happy emotions. This allows the system to automatically adjust the color tone and brightness based on the user's emotions and provide images that appeal to the user's emotions more.

[0065] The adjustment unit can select a specific portion of an image and convert only that portion into a different style. For example, the adjustment unit can select a specific portion of an image and convert only that portion into an oil painting style. For example, trees in a landscape image can be converted into an oil painting style. The adjustment unit can also select a specific portion of an image and convert only that portion into an illustration style. For example, buildings in a landscape image can be converted into an illustration style. The adjustment unit can also select a specific portion of an image and convert only that portion into a watercolor style. For example, the sky in a landscape image can be converted into a watercolor style. In this way, by converting specific portions of an image into different styles, it is possible to provide images with a higher level of customization.

[0066] The adjustment unit can automatically suggest adjustments based on text specified by a user. For example, the adjustment unit automatically suggests adjustments to the hue and brightness of an image based on text specified by a user. For example, for the text "sunset landscape," it suggests adjusting to a warmer color tone. The adjustment unit can also automatically suggest adjustments to the contrast and saturation of an image based on text specified by a user. For example, for the text "vivid flowers," it suggests adjustments to increase the contrast and saturation. The adjustment unit can also automatically suggest adjustments to specific parts of an image based on text specified by a user. For example, for the text "blue sky," it suggests adjusting the color tone of the sky. This allows for more efficient image adjustments by automatically suggesting adjustments based on text specified by a user.

[0067] The adjustment unit can learn the user's past adjustment history and automatically optimize adjustments from the next time onwards. For example, the adjustment unit uses a generative AI to learn the user's past adjustment history and automatically optimize adjustments from the next time onwards. For example, it can suggest adjustments based on the color tones and styles that the user has used in the past. The adjustment unit can also store the user's past adjustment history in a database and learn from it using a machine learning algorithm. For example, it can analyze the patterns of adjustments the user has made in the past and reflect them in adjustments from the next time onwards. The adjustment unit can also suggest adjustments in real time based on the user's past adjustment history. For example, while the user is adjusting an image, it can suggest optimal adjustments based on the past adjustment history. In this way, by learning the user's past adjustment history, adjustments can be made more efficiently from the next time onwards.

[0068] The adjustment unit can use the emotion estimation function to analyze the emotion of the user when making adjustments and suggest adjustments that bring out positive emotions. The adjustment unit, for example, uses the emotion estimation function to analyze the emotion of the user when making adjustments and suggest adjustments that bring out positive emotions. For example, the adjustment unit preferentially suggests adjustments that make the user feel happy. The adjustment unit can also use the emotion estimation function to suggest adjustments to color tone and brightness based on the user's emotions. For example, it can suggest adjustments to bright color tone and high brightness to a user who has positive emotions. The adjustment unit can also use the emotion estimation function to suggest the application of a style based on the user's emotions. For example, it can suggest a fun style to a user who has positive emotions. In this way, the emotion estimation function can suggest adjustments that correspond to the user's emotions.

[0069] The style application unit can analyze a user's emotions and automatically suggest a style based on the emotion. For example, the style application unit uses a generative AI to analyze a user's emotions and suggest a bright color tone or fun style to a user with positive emotions. For example, a bright oil painting style is suggested for a user with joyful emotions. The style application unit can also select a style based on the user's emotions using an emotion estimation function. For example, a fun illustration style is suggested for a user with positive emotions. The style application unit can also adjust the application of the style according to the intensity of the emotion. For example, a more vivid style is suggested for a user with a strong joyful emotion. In this way, by automatically suggesting a style based on the user's emotions, it is possible to provide images that appeal more emotionally.

[0070] The style application unit can combine and apply multiple styles, allowing the user to create an original style. The style application unit, for example, provides a function that allows the user to select multiple styles and combine them to create an original style. For example, a style that combines an oil painting style and a watercolor painting style can be created. The style application unit can also provide an interface that allows the user to customize the color tone and texture of the style. For example, the user can use a color palette or texture selection tool. The style application unit can also provide a function that allows the user to save and reuse the original style created by the user. For example, the user can save the style created by the user as a template and apply it to other images. This allows the user to create an original style by combining multiple styles.

[0071] The style application unit can display a comparison of images before and after application of a style in real time, allowing the user to instantly confirm the differences. The style application unit, for example, displays images before and after application of a style side by side, providing an interface that allows the user to instantly confirm the differences. For example, the images before and after application are compared using a slider. The style application unit can also display a preview of images before and after application of a style in real time. For example, when a style is selected, the image after application is immediately displayed. The style application unit can also display images before and after application of a style side by side, allowing the user to visually compare them. For example, the image before application and the image after application are displayed side by side. This allows the user to instantly confirm the differences by displaying a comparison of images before and after application of a style in real time.

[0072] The style application unit can add a function that allows a user to customize a style by himself / herself. For example, the style application unit provides an interface that allows a user to customize the color tone or texture of a style. For example, by using a color palette or a texture selection tool. The style application unit can also provide a function that allows a user to adjust style parameters. For example, by adjusting the thickness or transparency of a brush. The style application unit can also provide a function that allows a user to save and reuse a style customized by a user. For example, a style customized by a user can be saved as a template and applied to other images. This allows a user to customize a style by himself / herself, thereby providing more personalized images.

[0073] The style application unit can add a function to learn styles from different cultures and eras and apply them. For example, the style application unit provides a function that allows the generation AI to learn styles from different cultures and apply them. For example, applying a Japanese ukiyo-e style or a French impressionist style. The style application unit can also learn styles from different eras and apply them. For example, applying a Renaissance painting style or a modern art style. The style application unit can also regenerate images based on the style of a culture or era selected by the user. For example, if a user selects "Japanese ukiyo-e style," an image based on that style is generated. This allows for the provision of a wider variety of images by applying styles from different cultures and eras.

[0074] The style application unit can use the emotion estimation function to analyze the emotional response of the user to a style selected by the user and suggest an optimal style. For example, the style application unit can use the emotion estimation function to analyze the emotional response of the user to a style selected by the user and suggest a style that elicits positive emotions. For example, the style application unit can preferentially suggest a style that evokes joyful emotions in the user. The style application unit can also use the emotion estimation function to adjust the application of a style based on the user's emotions. For example, the style application unit can suggest bright colors and a fun style to a user who has positive emotions. The style application unit can also adjust the application of a style according to the intensity of the emotion. For example, the style application unit can suggest a more vivid style to a user who has a strong emotion of joy. In this way, the emotion estimation function can be used to suggest an optimal style according to the user's emotions.

[0075] The interface can analyze a user's emotions in real time and customize the interface according to the emotions. For example, the interface can analyze a user's emotions in real time and provide an interface with bright colors and a fun design to a user who has positive emotions. For example, a user who has emotions of joy can be provided with an interface with bright colors. The interface can also adjust the layout and color scheme of the interface based on the user's emotions using an emotion estimation function. For example, a user who has positive emotions can be provided with an interface with a fun design. The interface can also adjust the degree of customization according to the intensity of the emotion. For example, a user who has emotions of strong joy can be provided with an interface with more vivid colors. In this way, a more comfortable operating environment can be provided by customizing the interface according to the user's emotions.

[0076] The interface may be added with a voice operation function, allowing the user to issue commands by voice. For example, the interface may be added with a voice operation function, allowing the user to issue commands by voice. For example, when the user says, "Change the color to blue," the color of the image changes to blue. The interface may also use voice recognition technology to analyze voice commands and execute corresponding operations. For example, voice recognition software automatically analyzes the voice and executes operations according to the instructions. The interface may also use the voice operation function to execute multiple operations in succession. For example, when the user says, "Save the image and open the next image," the interface will save the image and open the next image in succession. Thus, adding the voice operation function allows the user to operate the interface more intuitively.

[0077] The interface may incorporate virtual reality (VR) technology to enable a user to generate and adjust an image in a virtual space. For example, the interface may incorporate virtual reality (VR) technology to enable a user to generate an image in a virtual space. For example, an image may be generated in a virtual space using a VR headset. The interface may also use virtual reality technology to enable a user to adjust the image in the virtual space. For example, an image may be adjusted in color or brightness using a VR controller. The interface may also use virtual reality technology to enable a user to select and modify a specific portion of the image in the virtual space. For example, an image may be modified by clicking on a specific portion of the image in the virtual space. In this way, the introduction of virtual reality technology allows a user to generate and adjust images in a more immersive environment.

[0078] The interface may incorporate augmented reality (AR) technology to enable an image generated in the real world to be superimposed on the real world. For example, the interface may incorporate augmented reality (AR) technology to enable an image generated in the real world to be superimposed on the real world. For example, the image generated in the real world may be superimposed and displayed using a smartphone camera. The interface may also use augmented reality technology to enable a user to manipulate the image generated in the real world. For example, the user may tap the smartphone screen to adjust the position of the image superimposed on the real world. The interface may also use augmented reality technology to enable a user to save and share the image generated in the real world. For example, the image superimposed on the real world may be saved as a screenshot and shared on a social networking site. In this way, the introduction of augmented reality technology enables an image generated in the real world to be superimposed on the real world.

[0079] The interface can use the emotion estimation function to analyze the emotion a user feels when operating the interface and make suggestions to improve operability. For example, the interface can use the emotion estimation function to analyze the emotion a user feels when operating the interface and suggest operability that elicits positive emotions. For example, the interface can preferentially suggest operations that evoke joy in the user. The interface can also use the emotion estimation function to adjust the interface layout and color scheme based on the user's emotion. For example, a user with positive emotions can be provided with an interface with a fun design. The interface can also adjust the operability suggestions according to the intensity of the emotion. For example, a user with a strong emotion of joy can be suggested with more intuitive operations. In this way, the emotion estimation function can be used to suggest improvements to operability according to the user's emotion.

[0080] The generation unit can analyze the user's emotions and generate high-resolution images based on the emotions. For example, the generation unit uses a generation AI to analyze the user's emotions and provide images with brighter colors and higher resolution to users with positive emotions. For example, a high-resolution image with brighter colors is generated for a user with happy emotions. The generation unit can also provide images with darker colors and lower resolution to users with negative emotions. For example, a high-resolution image with darker colors is generated for a user with sad emotions. The generation unit can also adjust the image resolution and color tone according to the intensity of the emotion. For example, a high-resolution image with more vivid colors is generated for a user with a strong emotion of joy. In this way, by generating high-resolution images based on the user's emotions, it is possible to provide images that appeal to the user's emotions more.

[0081] The generation unit can display the generation process of the high-resolution image in real time, allowing the user to check the progress. The generation unit, for example, displays the generation process of the high-resolution image in real time and provides an interface that allows the user to check the progress. For example, the image being generated can be displayed together with a progress bar. The generation unit can also display each step of the generation process in detail. For example, each step of image analysis, generation, and adjustment can be displayed. The generation unit can also update the progress of the generation process in real time and notify the user. For example, the progress of the generation process can be notified by a pop-up message. In this way, the user can check the progress by displaying the generation process of the high-resolution image in real time.

[0082] The generation unit can add a function that allows the user to make more detailed adjustments to the high-resolution image after it has been generated. For example, the generation unit provides an interface that allows the user to make fine adjustments to color tone, brightness, contrast, and the like after the high-resolution image has been generated. For example, adjustments are made using sliders. The generation unit can also adjust specific parts of the generated image. For example, a part of the image can be selected and the color tone and brightness of only that part can be adjusted. The generation unit can also apply a style to the generated image. For example, an oil painting style or watercolor painting style can be applied to the generated image. This allows for fine adjustments to be made after the high-resolution image has been generated, enabling more detailed customization.

[0083] The generation unit can add a function for simultaneously generating high-resolution images at different resolutions and formats. For example, the generation unit provides a function for simultaneously generating high-resolution images at different resolutions and formats. For example, images at 4K resolution and 1080p resolution are generated simultaneously. The generation unit can also simultaneously generate images in different formats. For example, images in JPEG format and PNG format are generated simultaneously. The generation unit can also generate images based on a resolution and format specified by the user. For example, multiple images are generated simultaneously based on a resolution and format specified by the user. This allows for simultaneous generation of images at different resolutions and formats to meet the diverse needs of users.

[0084] The generation unit can add a function to generate an image optimized for a purpose specified by a user when generating a high-resolution image. The generation unit provides a function to generate an image optimized for a purpose specified by a user when generating a high-resolution image, for example. For example, a high-resolution image for a poster is generated. The generation unit can also generate a high-resolution image for a website. For example, an image for a website banner is generated. The generation unit can also generate a high-resolution image for a presentation. For example, an image for a presentation slide is generated. In this way, by generating an image optimized for a purpose specified by a user, a more appropriate image can be provided.

[0085] The generation unit can use the emotion estimation function to analyze the emotion of the user when generating a high-resolution image and suggest the optimal resolution and format. For example, the generation unit can use the emotion estimation function to analyze the emotion of the user when generating a high-resolution image and suggest a resolution and format that elicits positive emotions. For example, the generation unit can suggest a resolution and format that evokes a joyful emotion in the user. The generation unit can also use the emotion estimation function to select a resolution and format based on the user's emotion. For example, a high-resolution image is suggested for a user who has a positive emotion. The generation unit can also adjust the suggested resolution and format according to the intensity of the emotion. For example, a higher-resolution image is suggested for a user who has a strong joyful emotion. In this way, the emotion estimation function can be used to suggest the optimal resolution and format according to the user's emotion.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The generation unit can learn the user's past generation history and automatically optimize subsequent generations. For example, it can suggest a generation based on the color tone or style that the user has used in the past. The generation unit can also store the user's past generation history in a database and learn using a machine learning algorithm. For example, it can analyze the patterns of images the user has generated in the past and reflect them in subsequent generations. The generation unit can also suggest a generation in real time based on the user's past generation history. For example, while the user is generating an image, it can suggest the optimal generation based on the past generation history. In this way, by learning the user's past generation history, subsequent generations can be performed more efficiently.

[0088] The generation unit can analyze a user's emotions and generate high-resolution images based on the emotions. For example, the generation AI can analyze a user's emotions and provide images with brighter colors and higher resolution to users with positive emotions. For example, a high-resolution image with brighter colors is generated for a user with happy emotions. The generation unit can also provide images with darker colors and lower resolution to users with negative emotions. For example, a high-resolution image with darker colors is generated for a user with sad emotions. The generation unit can also adjust the image resolution and color tone according to the intensity of the emotion. For example, a high-resolution image with more vivid colors is generated for a user with a strong emotion of joy. In this way, by generating high-resolution images based on the user's emotions, it is possible to provide images that appeal to the user's emotions more.

[0089] The generation unit can add a function to learn styles from different cultures and eras and apply them. For example, the generation AI can learn styles from different cultures and apply them. For example, it can apply a Japanese ukiyo-e style or a French impressionist style. The generation unit can also learn styles from different eras and apply them. For example, it can apply a Renaissance painting style or a modern art style. The generation unit can also regenerate images based on the style of a culture or era selected by the user. For example, if a user selects "Japanese ukiyo-e style," it generates an image based on that style. This allows the application of styles from different cultures and eras to provide a wider variety of images.

[0090] The generation unit can use the emotion estimation function to analyze the emotion a user has when generating a high-resolution image and suggest the optimal resolution and format. For example, the emotion estimation function can be used to analyze the emotion a user has when generating a high-resolution image and suggest a resolution and format that elicits positive emotions. For example, the generation unit can suggest a resolution and format that evokes a joyful emotion in the user. The generation unit can also use the emotion estimation function to select a resolution and format based on the user's emotion. For example, a high-resolution image is suggested for a user with a positive emotion. The generation unit can also adjust the suggested resolution and format according to the intensity of the emotion. For example, a higher-resolution image is suggested for a user with a strong joyful emotion. In this way, the emotion estimation function can be used to suggest the optimal resolution and format according to the user's emotion.

[0091] The generation unit can display the generation process of the high-resolution image in real time, allowing the user to check the progress. For example, the generation process of the high-resolution image can be displayed in real time, providing an interface that allows the user to check the progress. For example, the image being generated can be displayed together with a progress bar. The generation unit can also display each step of the generation process in detail. For example, each step of image analysis, generation, and adjustment can be displayed. The generation unit can also update the progress of the generation process in real time and notify the user. For example, the progress of the generation process can be notified by a pop-up message. In this way, the generation process of the high-resolution image can be displayed in real time, allowing the user to check the progress.

[0092] The generation unit can use the emotion estimation function to analyze the emotion of text entered by a user and generate an image based on that emotion. For example, the generation AI can use the emotion estimation function to analyze the emotion of text entered by a user and generate an image based on that emotion. For example, for the text "fun picnic," it generates an image depicting bright sunshine and smiling people. The generation unit can also use the emotion estimation function to generate an image with dark tones for text with negative emotions. For example, for the text "stormy night," it generates an image depicting dark clouds and heavy rain. The generation unit can also adjust the color tone and brightness of the image according to the intensity of the emotion. For example, for text with a strong emotion of joy, it generates an image with more vivid colors. In this way, the emotion estimation function can be used to generate images that correspond to the user's emotions.

[0093] The generation unit can add a function for simultaneously generating high-resolution images at different resolutions and formats. For example, a function for simultaneously generating high-resolution images at different resolutions and formats is provided. For example, images at 4K resolution and 1080p resolution are generated simultaneously. The generation unit can also simultaneously generate images in different formats. For example, images in JPEG format and PNG format are generated simultaneously. The generation unit can also generate images based on a resolution and format specified by the user. For example, multiple images are generated simultaneously based on a resolution and format specified by the user. This allows the various needs of users to be met by simultaneously generating images at different resolutions and formats.

[0094] The generation unit can use the emotion estimation function to analyze the emotional response of the user to the style selected and suggest an optimal style. For example, the emotion estimation function can be used to analyze the emotional response of the user to the style selected and suggest a style that elicits positive emotions. For example, styles that evoke the user's emotion of joy can be preferentially suggested. The generation unit can also use the emotion estimation function to adjust the application of the style based on the user's emotion. For example, bright colors and a fun style can be suggested to a user with positive emotions. The generation unit can also adjust the application of the style according to the intensity of the emotion. For example, a more vivid style can be suggested to a user with a strong emotion of joy. In this way, the emotion estimation function can be used to suggest an optimal style according to the user's emotion.

[0095] The generation unit can add a function to generate an image optimized for a purpose specified by a user when generating a high-resolution image. For example, a function to generate an image optimized for a purpose specified by a user when generating a high-resolution image is provided. For example, a high-resolution image for a poster is generated. The generation unit can also generate a high-resolution image for a website. For example, an image for a website banner is generated. The generation unit can also generate a high-resolution image for a presentation. For example, an image for a presentation slide is generated. In this way, by generating an image optimized for a purpose specified by a user, a more appropriate image can be provided.

[0096] The generation unit can use the emotion estimation function to analyze the emotion of text entered by a user and generate an image based on that emotion. For example, the generation AI can use the emotion estimation function to analyze the emotion of text entered by a user and generate an image based on that emotion. For example, for the text "fun picnic," it generates an image depicting bright sunshine and smiling people. The generation unit can also use the emotion estimation function to generate an image with dark tones for text with negative emotions. For example, for the text "stormy night," it generates an image depicting dark clouds and heavy rain. The generation unit can also adjust the color tone and brightness of the image according to the intensity of the emotion. For example, for text with a strong emotion of joy, it generates an image with more vivid colors. In this way, the emotion estimation function can be used to generate images that correspond to the user's emotions.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The text input unit accepts text from the user. For example, it can accept sentences or keywords entered by the user. The text input unit can also accept voice input. Step 2: The generation unit analyzes the text received by the text input unit and generates a corresponding high-resolution image. For example, the generation AI may use a text generation AI (e.g., LLM) to analyze the text and generate an image. The generation AI may also use a multimodal generation AI to analyze both the text and the image and generate an image. Step 3: The adjustment unit makes fine adjustments to the image generated by the generation unit. For example, the adjustment unit can adjust the color, brightness, and contrast of the image. The adjustment unit can also change only specific parts. For example, the adjustment unit can change the color of trees in a landscape image or change the color of the sky to a sunset. Step 4: The style application unit applies various styles to the image adjusted by the adjustment unit. For example, the style application unit can select a style such as oil painting, illustration, or watercolor painting and apply it to the image.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 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.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The 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.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 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.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the 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.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The 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.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] 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.

[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 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.

[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 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.

[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 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0165] 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]

[0166] 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 text input section for accepting user text; a generation unit that analyzes the text received by the text input unit and generates a corresponding high-resolution image; an adjustment unit that performs fine adjustment of the image generated by the generation unit; a style application unit that applies a plurality of styles to the image adjusted by the adjustment unit. A system characterized by:

2. The text input unit Accepts voice input, The generation unit Analyzing the voice input to generate the image 2. The system of claim 1.

3. The adjustment unit Analyze the user's emotions and automatically adjust color tone and brightness based on the emotions.

2. The system of claim 1.

4. The style application unit Analyze the user's emotions and automatically suggest styles based on those emotions.

2. The system of claim 1.

5. The interface is Analyze the user's emotions in real time and customize the experience according to their emotions.

2. The system of claim 1.

6. The generation unit Analyzing the sentiment of the text and generating the image based on the sentiment 2. The system of claim 1.

7. The generation unit Analyzing the sentiment of the text entered by the user and generating the image based on that sentiment 2. The system of claim 1.

8. The interface is Analyzing the emotions of the user when operating the interface and making suggestions to improve operability 2. The system of claim 1.

Images