System

Abstract

An object of a system according to an embodiment is to efficiently generate a creative image.SOLUTION: A system according to an embodiment includes a background image generation unit, a subject generation unit, and an effect combination unit. The background image generation unit generates a background image that is an image of autumnal leaves. The subject generation unit generates autumnal foliage or an object based on the background image generated by the background image generation unit. The effect combination unit generates an optimal creative image by combining a plurality of effects for the autumnal leaves or the object generated by the subject generation 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] Conventional techniques have had the problem that generating creative images requires a lot of time and effort.

[0005] The system according to the embodiment aims to efficiently generate creative images. [Means for solving the problem]

[0006] The system according to the embodiment includes a background image generation unit, a subject generation unit, and an effect combination unit. The background image generation unit generates a background image that represents autumn leaves. The subject generation unit generates autumn leaves or objects based on the background image generated by the background image generation unit. The effect combination unit combines multiple effects with the autumn leaves or objects generated by the subject generation unit to generate an optimal creative image. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate creative images. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The design AI system according to an embodiment of the present invention utilizes design AI to streamline the production of social media projects. This system uses a generation AI to generate a background image that evokes autumn leaves, and then generates the leaves and objects using Illustrator's AI subject generation function. This allows the design AI system to streamline the production of social media projects and significantly reduce production time.

[0029] A design AI system according to an embodiment includes a background image generation unit, a subject generation unit, and an effect combination unit. The background image generation unit generates a background image that evokes autumn leaves. For example, the generation AI analyzes the color and shape of autumn leaves to recreate a realistic autumn foliage scene. The generation AI can also generate a background image based on prompts containing user instructions on what the user wants the generation AI to do. The subject generation unit generates autumn leaves and objects based on the background image generated by the background image generation unit. For example, when generating autumn leaves or tree objects, the generation AI analyzes their shape and color to generate realistic illustration materials. The generation AI can also generate illustration materials based on prompts containing user instructions on what the user wants the generation AI to do. The effect combination unit combines various effects with the autumn leaves and objects generated by the subject generation unit to generate an optimal creative image. For example, the generation AI combines effects such as "beauty" and "blur" to generate an optimal creative image. The generation AI can also generate an image combining effects based on prompts containing user instructions on what the user wants the generation AI to do. This allows the design AI system to streamline the production of social media projects and significantly reduce production time.

[0030] The background image generation unit can generate background images that reflect seasonal changes in real time. For example, the background image generation unit uses a generation AI to generate background images corresponding to each season: spring, summer, autumn, and winter. For example, it reflects in real time cherry blossoms in spring, lush green trees in summer, autumn leaves in autumn, and snowy scenery in winter. To reflect seasonal changes in real time, the background image generation unit inputs weather data and seasonal event information into the generation AI and generates background images based on that information. For example, it emphasizes the colors of autumn leaves during the autumn foliage season. When generating background images that reflect seasonal changes using the generation AI, the background image generation unit also recreates specific scenery from a region specified by the user. For example, it generates autumn leaves in Kyoto and snowy scenery in Hokkaido in real time. This allows for the generation of background images that reflect seasonal changes in real time.

[0031] The background image generation unit generates background images based on specific regions and cultures, recreating scenery unique to those regions. For example, the background image generation unit uses a generation AI to generate background images based on those regions. For example, it recreates scenery unique to those regions, such as temples in Kyoto or the Eiffel Tower in Paris. The background image generation unit also inputs the culture and historical background of the region into the generation AI and generates background images based on that information. For example, it reflects cultural elements such as the Great Buddha of Nara or the Statue of Liberty in New York. Furthermore, when recreating scenery unique to those regions using the generation AI, the background image generation unit also reflects specific events or festivals in the region specified by the user. For example, it incorporates the Gion Festival in Kyoto or the Rio Carnival into the background image. This allows the generation of background images based on specific regions and cultures, recreating scenery unique to those regions.

[0032] The background image generation unit can add dynamic effects to the background image to provide a more realistic experience. The background image generation unit, for example, uses a generation AI to add the movement of wind and rain to the background image. For example, it generates scenes of trees swaying in the wind and scenes of rain falling in real time. To add dynamic effects, the background image generation unit inputs weather data into the generation AI and generates a background image based on that data. For example, it reflects scenes of a storm or a scene of snow falling in real time. When adding dynamic effects to the background image, the background image generation unit also uses the generation AI to reflect specific effects specified by the user. For example, it generates a night sky with fireworks or a beach with waves crashing. This allows dynamic effects to be added to the background image to provide a more realistic experience.

[0033] The background image generation unit combines sound or music with a background image, engaging the user both visually and aurally. The background image generation unit combines sound or music with a background image, for example, using a generation AI. For example, it adds the chirping of birds or the murmuring of a river to a forest scene. To combine sound or music, the background image generation unit inputs sound data or music data into the generation AI and generates a background image based on that sound. For example, it adds the sound of waves or relaxing music to an ocean scene. The background image generation unit also uses the generation AI to reflect specific sound or music specified by the user when combining sound or music with a background image. For example, it combines music that the user likes with a background image. This allows the combination of sound or music with a background image to engage the user both visually and aurally.

[0034] The subject generation unit can simulate the movement of a subject and generate dynamic illustration material. The subject generation unit, for example, uses a generation AI to simulate the movement of a subject and generate dynamic illustration material. For example, it reproduces the movements of a running animal or a flying bird in real time. In addition, to simulate the movement of a subject, the subject generation unit inputs motion data into the generation AI and generates illustration material based on that data. For example, it reproduces the movements of a dancing person or a swimming fish. In addition, when simulating the movement of a subject using the generation AI, the subject generation unit reflects specific movements specified by the user. For example, it generates a character that takes a specific pose or action. In this way, it is possible to simulate the movement of a subject and generate dynamic illustration material.

[0035] The subject generation unit generates illustration materials from different viewpoints of a subject, providing a 360-degree visual experience. The subject generation unit generates illustration materials from different viewpoints of a subject, for example, using a generation AI. For example, it generates front, side, and back viewpoints of a character in real time. To generate different viewpoints of a subject, the subject generation unit inputs 3D model data into the generation AI and generates illustration materials based on the input data. For example, it reproduces 360-degree viewpoints of buildings and cars. When generating different viewpoints of a subject using the generation AI, the subject generation unit also reflects specific viewpoints specified by the user. For example, it generates viewpoints seen from a specific angle or height. This allows the generation of illustration materials from different viewpoints of a subject, providing a 360-degree visual experience.

[0036] The subject generation unit can automatically generate the subject's outfit or accessories and provide customizable illustration materials. The subject generation unit, for example, uses a generation AI to automatically generate the subject's outfit or accessories. For example, it generates a character's clothing, hat, and accessories in real time. In addition, to automatically generate outfits and accessories, the subject generation unit inputs fashion data and design data into the generation AI and generates illustration materials based on that data. For example, it generates outfits that reflect the latest fashion trends. In addition, when automatically generating the subject's outfit and accessories using the generation AI, the subject generation unit reflects a specific style or theme specified by the user. For example, it generates a vintage style or futuristic design. This makes it possible to automatically generate the subject's outfit and accessories and provide customizable illustration materials.

[0037] The effect combination unit can simulate the visual impact of effect combinations and generate optimal creative images. The effect combination unit, for example, uses a generation AI to simulate the visual impact of effect combinations. For example, it analyzes the visual impact when the effects of "beauty" and "blur" are combined. The effect combination unit also inputs visual data into the generation AI to simulate the visual impact of effect combinations and generates optimal creative images based on that data. For example, it analyzes color balance and lighting effects. The effect combination unit also reflects specific effects specified by the user when simulating the visual impact of effect combinations using the generation AI. For example, it analyzes the visual impact when specific filters and effects are combined. This allows the visual impact of effect combinations to be simulated and optimal creative images to be generated.

[0038] The effect combination unit can apply the effect combination to different media to generate multimedia-compatible creative images. The effect combination unit, for example, uses generation AI to apply the effect combination to videos or animations. For example, it generates videos that combine the effects of "beauty" and "blur." The effect combination unit also inputs media data into the generation AI to apply the effect combination to different media, and generates multimedia-compatible creative images based on that data. For example, it combines color correction and lighting effects with animations. The effect combination unit also uses generation AI to reflect specific effects specified by the user when applying the effect combination to different media. For example, it generates videos or animations that combine specific filters and effects. This allows the effect combination to be applied to different media to generate multimedia-compatible creative images.

[0039] The effect combination unit can apply effect combinations to different themes to generate creative images that correspond to those themes. The effect combination unit, for example, uses a generation AI to apply effect combinations to seasons and events. For example, it generates an image that combines effects that match spring cherry blossoms, summer ocean scenes, autumn leaves, and winter snow scenes. To apply effect combinations to different themes, the effect combination unit inputs theme data into the generation AI and generates creative images based on that data. For example, it combines effects that match events such as Christmas and Halloween. The effect combination unit also uses the generation AI to reflect specific themes specified by the user when applying effect combinations to different themes. For example, it generates an image that combines filters and effects that match specific seasons or events. This allows effect combinations to be applied to different themes to generate creative images that correspond to those themes.

[0040] The effect combination unit automates the entire production process, minimizing user effort. The effect combination unit automates the entire production process, for example, using generative AI. For example, it consistently automates everything from background image generation to AI-generated subject generation and effect combination. To automate the production process, the effect combination unit inputs prompt data into the generative AI, which then automates all production steps based on that data. For example, it automatically generates images based on user specifications. Furthermore, when automating the entire production process using generative AI, the effect combination unit reflects specific user-specified requirements. For example, it builds an automated process tailored to a specific theme or style. This automates the entire production process, minimizing user effort.

[0041] The effect combination unit can generate multiple proposals simultaneously, allowing the user to quickly select the optimal proposal. The effect combination unit generates multiple proposals simultaneously, for example, using a generation AI. For example, it generates multiple illustrations of different background images or subjects, allowing the user to select one. In addition, to generate multiple proposals simultaneously, the effect combination unit inputs prompt data to the generation AI and generates multiple images based on the prompt data. For example, it proposes multiple combinations of different effects. In addition, when generating multiple proposals simultaneously using the generation AI, the effect combination unit reflects specific requirements specified by the user. For example, it generates multiple proposals tailored to a specific theme or style. This allows multiple proposals to be generated simultaneously, allowing the user to quickly select the optimal proposal.

[0042] The effect combination unit can adapt the production process to different platforms, improving user convenience. For example, the effect combination unit uses a generation AI to adapt the production process to mobile apps and web apps. For example, it builds a production process optimized for operation on a smartphone or tablet. To adapt the production process to different platforms, the effect combination unit inputs platform data into the generation AI and optimizes the production process based on that data. For example, it automatically generates an interface for mobile devices. Furthermore, the effect combination unit uses a generation AI to reflect specific requirements specified by the user when adapting the production process to different platforms. For example, it builds a production process optimized for a specific device or OS. This allows the production process to adapt to different platforms, improving user convenience.

[0043] The effects combination unit can adapt the production process to different languages ​​or cultures and generate proposals from a global perspective. The effects combination unit, for example, uses a generative AI to adapt the production process to different languages ​​and cultures. For example, it builds a production process that supports multiple languages, such as English, French, and Chinese. To adapt to different languages ​​and cultures, the effects combination unit inputs language data and cultural data into the generative AI and generates proposals from a global perspective based on that data. For example, it generates images that reflect landscapes and cultural elements unique to a region. The effects combination unit also uses the generative AI to reflect specific requirements specified by the user when adapting the production process to different languages ​​and cultures. For example, it builds a production process optimized for a specific region or culture. This makes it possible to adapt the production process to different languages ​​and cultures and generate proposals from a global perspective.

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

[0045] The design AI system can also use the background image generation unit to generate background images according to specific time periods specified by the user. For example, morning scenes, daytime scenes, evening scenes, and night scenes can be generated in real time. The background image generation unit can also input time period data into the generation AI and generate background images based on that data. For example, it can generate scenes with morning light streaming in or scenes with a sunset glow. This makes it possible to provide background images according to specific time periods specified by the user.

[0046] The design AI system can further use the object generation unit to generate objects according to a specific style specified by the user. For example, it can generate objects in a variety of styles, including realistic, anime, and abstract styles. The object generation unit can also input style data into the generation AI and generate objects based on that data. For example, it can generate characters and objects that match the style specified by the user. This makes it possible to provide objects according to the specific style specified by the user.

[0047] The design AI system can further combine effects according to specific themes specified by the user in the effect combination section. For example, it can combine effects according to various themes, such as seasonal themes, event themes, and themes based on specific locations. The effect combination section can also input theme data into the generation AI and combine effects based on that data. For example, it can combine effects that match cherry blossoms in spring, the sea in summer, autumn leaves, and snowy winter scenery. This makes it possible to provide effects that match specific themes specified by the user.

[0048] The design AI system can further use the background image generation unit to generate a background image based on a specific location specified by the user. For example, it can generate a city or natural landscape specified by the user in real time. The background image generation unit can also input location data into the generation AI and generate a background image based on that data. For example, it can reproduce the landscape of a location specified by the user. This makes it possible to provide a background image based on the specific location specified by the user.

[0049] The design AI system can further use the subject generation unit to generate subjects that correspond to specific poses specified by the user. For example, it can generate characters or objects that assume specific poses specified by the user. The subject generation unit can also input pose data into the generation AI and generate subjects based on that data. For example, it can generate characters that assume specific poses specified by the user in real time. This makes it possible to provide subjects that correspond to specific poses specified by the user.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The background image generator generates a background image that evokes the image of autumn leaves. For example, the generator AI analyzes the color and shape of autumn leaves to recreate a realistic autumn foliage scene. The generator AI can also generate background images based on prompts from the user, including instructions on what the user wants the generator AI to do. Step 2: The subject generation unit generates autumn leaves and objects based on the background image generated by the background image generation unit. For example, when generating autumn leaves or tree objects, the generation AI analyzes their shape and color to generate realistic illustration materials. The generation AI can also generate illustration materials based on prompts that include instructions from the user on what the generation AI wants to do. Step 3: The effect combination unit combines various effects with the autumn leaves and objects generated by the subject generation unit to generate the optimal creative image. For example, the generation AI combines effects such as "beauty" and "blur" to generate the optimal creative image. The generation AI can also generate an image that combines effects based on prompts from the user, including instructions on what the user wants the generation AI to do.

[0052] (Example 2) The design AI system according to an embodiment of the present invention utilizes design AI to streamline the production of social media projects. This system uses a generation AI to generate a background image that evokes autumn leaves, and then generates the leaves and objects using Illustrator's AI subject generation function. This allows the design AI system to streamline the production of social media projects and significantly reduce production time.

[0053] A design AI system according to an embodiment includes a background image generation unit, a subject generation unit, and an effect combination unit. The background image generation unit generates a background image that evokes autumn leaves. For example, the generation AI analyzes the color and shape of autumn leaves to recreate a realistic autumn foliage scene. The generation AI can also generate a background image based on prompts containing user instructions on what the user wants the generation AI to do. The subject generation unit generates autumn leaves and objects based on the background image generated by the background image generation unit. For example, when generating autumn leaves or tree objects, the generation AI analyzes their shape and color to generate realistic illustration materials. The generation AI can also generate illustration materials based on prompts containing user instructions on what the user wants the generation AI to do. The effect combination unit combines various effects with the autumn leaves and objects generated by the subject generation unit to generate an optimal creative image. For example, the generation AI combines effects such as "beauty" and "blur" to generate an optimal creative image. The generation AI can also generate an image combining effects based on prompts containing user instructions on what the user wants the generation AI to do. This allows the design AI system to streamline the production of social media projects and significantly reduce production time.

[0054] The background image generation unit can generate background images that reflect seasonal changes in real time. For example, the background image generation unit uses a generation AI to generate background images corresponding to each season: spring, summer, autumn, and winter. For example, it reflects in real time cherry blossoms in spring, lush green trees in summer, autumn leaves in autumn, and snowy scenery in winter. To reflect seasonal changes in real time, the background image generation unit inputs weather data and seasonal event information into the generation AI and generates background images based on that information. For example, it emphasizes the colors of autumn leaves during the autumn foliage season. When generating background images that reflect seasonal changes using the generation AI, the background image generation unit also recreates specific scenery from a region specified by the user. For example, it generates autumn leaves in Kyoto and snowy scenery in Hokkaido in real time. This allows for the generation of background images that reflect seasonal changes in real time.

[0055] The background image generation unit generates background images based on specific regions and cultures, recreating scenery unique to those regions. For example, the background image generation unit uses a generation AI to generate background images based on those regions. For example, it recreates scenery unique to those regions, such as temples in Kyoto or the Eiffel Tower in Paris. The background image generation unit also inputs the culture and historical background of the region into the generation AI and generates background images based on that information. For example, it reflects cultural elements such as the Great Buddha of Nara or the Statue of Liberty in New York. Furthermore, when recreating scenery unique to those regions using the generation AI, the background image generation unit also reflects specific events or festivals in the region specified by the user. For example, it incorporates the Gion Festival in Kyoto or the Rio Carnival into the background image. This allows the generation of background images based on specific regions and cultures, recreating scenery unique to those regions.

[0056] The background image generation unit generates a background image according to the user's emotions, thereby eliciting positive emotions. The background image generation unit, for example, uses an emotion estimation function to analyze the user's emotional state in real time and generate a background image according to the emotion. For example, if the user wants to relax, it generates a calm landscape. The background image generation unit also generates a background image that elicits positive emotions based on the user's emotion data. For example, if the user is feeling stressed, it generates a natural landscape or a soothing image. The background image generation unit also uses the emotion estimation function to generate a background image according to the user's emotions by referring to data on background images that the user has previously preferred and generating a similar image. This allows the background image to be generated according to the user's emotions and elicit positive emotions.

[0057] The background image generation unit can add dynamic effects to the background image to provide a more realistic experience. The background image generation unit, for example, uses a generation AI to add the movement of wind and rain to the background image. For example, it generates scenes of trees swaying in the wind and scenes of rain falling in real time. To add dynamic effects, the background image generation unit inputs weather data into the generation AI and generates a background image based on that data. For example, it reflects scenes of a storm or a scene of snow falling in real time. When adding dynamic effects to the background image, the background image generation unit also uses the generation AI to reflect specific effects specified by the user. For example, it generates a night sky with fireworks or a beach with waves crashing. This allows dynamic effects to be added to the background image to provide a more realistic experience.

[0058] The background image generation unit combines sound or music with a background image, engaging the user both visually and aurally. The background image generation unit combines sound or music with a background image, for example, using a generation AI. For example, it adds the chirping of birds or the murmuring of a river to a forest scene. To combine sound or music, the background image generation unit inputs sound data or music data into the generation AI and generates a background image based on that sound. For example, it adds the sound of waves or relaxing music to an ocean scene. The background image generation unit also uses the generation AI to reflect specific sound or music specified by the user when combining sound or music with a background image. For example, it combines music that the user likes with a background image. This allows the combination of sound or music with a background image to engage the user both visually and aurally.

[0059] The background image generation unit can analyze the user's emotions when selecting a background image in real time and suggest an optimal background image. The background image generation unit, for example, uses an emotion estimation function to analyze the user's emotions when selecting a background image in real time and suggest an optimal background image based on the emotions. For example, if the user wants to relax, a calm landscape is suggested. The background image generation unit also builds a system that suggests optimal background images based on the user's emotion data. For example, if the user is feeling stressed, a natural landscape or a soothing image is suggested. The background image generation unit also uses the emotion estimation function to analyze the user's emotions when selecting a background image and suggest similar images by referring to data on background images that the user has previously preferred. This allows the user's emotions when selecting a background image to be analyzed in real time and suggest an optimal background image.

[0060] The subject generation unit can simulate the movement of a subject and generate dynamic illustration material. The subject generation unit, for example, uses a generation AI to simulate the movement of a subject and generate dynamic illustration material. For example, it reproduces the movements of a running animal or a flying bird in real time. In addition, to simulate the movement of a subject, the subject generation unit inputs motion data into the generation AI and generates illustration material based on that data. For example, it reproduces the movements of a dancing person or a swimming fish. In addition, when simulating the movement of a subject using the generation AI, the subject generation unit reflects specific movements specified by the user. For example, it generates a character that takes a specific pose or action. In this way, it is possible to simulate the movement of a subject and generate dynamic illustration material.

[0061] The subject generation unit generates illustration materials from different viewpoints of a subject, providing a 360-degree visual experience. The subject generation unit generates illustration materials from different viewpoints of a subject, for example, using a generation AI. For example, it generates front, side, and back viewpoints of a character in real time. To generate different viewpoints of a subject, the subject generation unit inputs 3D model data into the generation AI and generates illustration materials based on the input data. For example, it reproduces 360-degree viewpoints of buildings and cars. When generating different viewpoints of a subject using the generation AI, the subject generation unit also reflects specific viewpoints specified by the user. For example, it generates viewpoints seen from a specific angle or height. This allows the generation of illustration materials from different viewpoints of a subject, providing a 360-degree visual experience.

[0062] The subject generation unit can generate a facial expression or pose of a subject based on the user's emotions. The subject generation unit, for example, uses an emotion estimation function to analyze the user's emotional state in real time and generate a facial expression and pose of a subject based on that emotion. For example, if the user is happy, a smiling character is generated. The subject generation unit also builds a system that generates a facial expression and pose of a subject based on the user's emotional data. For example, if the user is sad, a character with a sad expression is generated. The subject generation unit also uses the emotion estimation function to generate a facial expression and pose of a subject based on the user's emotions by referring to data of facial expressions and poses that the user has preferred in the past and generating similar images. This makes it possible to generate a facial expression and pose of a subject based on the user's emotions.

[0063] The subject generation unit can automatically generate the subject's outfit or accessories and provide customizable illustration materials. The subject generation unit, for example, uses a generation AI to automatically generate the subject's outfit or accessories. For example, it generates a character's clothing, hat, and accessories in real time. In addition, to automatically generate outfits and accessories, the subject generation unit inputs fashion data and design data into the generation AI and generates illustration materials based on that data. For example, it generates outfits that reflect the latest fashion trends. In addition, when automatically generating the subject's outfit and accessories using the generation AI, the subject generation unit reflects a specific style or theme specified by the user. For example, it generates a vintage style or futuristic design. This makes it possible to automatically generate the subject's outfit and accessories and provide customizable illustration materials.

[0064] The subject generation unit can analyze the emotion of the user when selecting a subject in real time and suggest the most suitable subject. The subject generation unit, for example, uses an emotion estimation function to analyze the emotion of the user when selecting a subject in real time and suggest the most suitable subject based on that emotion. For example, if the user is having fun, it suggests a character with a bright expression. The subject generation unit also builds a system that suggests the most suitable subject based on the user's emotion data. For example, if the user wants to relax, it suggests a character with a calm expression. The subject generation unit also uses the emotion estimation function to analyze the emotion of the user when selecting a subject and suggests similar images by referring to data on subjects that the user has liked in the past. In this way, it is possible to analyze the emotion of the user when selecting a subject in real time and suggest the most suitable subject.

[0065] The effect combination unit can simulate the visual impact of effect combinations and generate optimal creative images. The effect combination unit, for example, uses a generation AI to simulate the visual impact of effect combinations. For example, it analyzes the visual impact when the effects of "beauty" and "blur" are combined. The effect combination unit also inputs visual data into the generation AI to simulate the visual impact of effect combinations and generates optimal creative images based on that data. For example, it analyzes color balance and lighting effects. The effect combination unit also reflects specific effects specified by the user when simulating the visual impact of effect combinations using the generation AI. For example, it analyzes the visual impact when specific filters and effects are combined. This allows the visual impact of effect combinations to be simulated and optimal creative images to be generated.

[0066] The effect combination unit proposes a combination of effects based on the user's emotions, thereby eliciting positive emotions. The effect combination unit, for example, uses an emotion estimation function to analyze the user's emotional state in real time and propose a combination of effects based on the emotions. For example, if the user wants to relax, it combines calm colors with a soft light effect. The effect combination unit also builds a system that proposes a combination of effects that elicit positive emotions based on the user's emotional data. For example, if the user is feeling stressed, it combines soothing colors with a blurring effect. The effect combination unit also uses the emotion estimation function to refer to data on effects that the user has previously preferred and proposes similar images when proposing a combination of effects based on the user's emotions. This makes it possible to propose a combination of effects based on the user's emotions and elicit positive emotions.

[0067] The effect combination unit can apply the effect combination to different media to generate multimedia-compatible creative images. The effect combination unit, for example, uses generation AI to apply the effect combination to videos or animations. For example, it generates videos that combine the effects of "beauty" and "blur." The effect combination unit also inputs media data into the generation AI to apply the effect combination to different media, and generates multimedia-compatible creative images based on that data. For example, it combines color correction and lighting effects with animations. The effect combination unit also uses generation AI to reflect specific effects specified by the user when applying the effect combination to different media. For example, it generates videos or animations that combine specific filters and effects. This allows the effect combination to be applied to different media to generate multimedia-compatible creative images.

[0068] The effect combination unit can apply effect combinations to different themes to generate creative images that correspond to those themes. The effect combination unit, for example, uses a generation AI to apply effect combinations to seasons and events. For example, it generates an image that combines effects that match spring cherry blossoms, summer ocean scenes, autumn leaves, and winter snow scenes. To apply effect combinations to different themes, the effect combination unit inputs theme data into the generation AI and generates creative images based on that data. For example, it combines effects that match events such as Christmas and Halloween. The effect combination unit also uses the generation AI to reflect specific themes specified by the user when applying effect combinations to different themes. For example, it generates an image that combines filters and effects that match specific seasons or events. This allows effect combinations to be applied to different themes to generate creative images that correspond to those themes.

[0069] The effect combination unit can analyze the emotions of the user when selecting an effect combination in real time and suggest an optimal effect combination. For example, the effect combination unit uses an emotion estimation function to analyze the emotions of the user when selecting an effect combination in real time and suggest an optimal effect combination based on the emotions. For example, if the user wants to relax, a gentle color tone and a soft light effect are combined. The effect combination unit also builds a system that suggests an optimal effect combination based on the user's emotion data. For example, if the user is feeling stressed, a soothing color tone and a blurring effect are combined. The effect combination unit also uses the emotion estimation function to analyze the emotions of the user when selecting an effect combination and suggest similar images by referring to data on effects that the user has previously preferred. In this way, the emotions of the user when selecting an effect combination can be analyzed in real time and an optimal effect combination can be suggested.

[0070] The effect combination unit automates the entire production process, minimizing user effort. The effect combination unit automates the entire production process, for example, using generative AI. For example, it consistently automates everything from background image generation to AI-generated subject generation and effect combination. To automate the production process, the effect combination unit inputs prompt data into the generative AI, which then automates all production steps based on that data. For example, it automatically generates images based on user specifications. Furthermore, when automating the entire production process using generative AI, the effect combination unit reflects specific user-specified requirements. For example, it builds an automated process tailored to a specific theme or style. This automates the entire production process, minimizing user effort.

[0071] The effect combination unit can generate multiple proposals simultaneously, allowing the user to quickly select the optimal proposal. The effect combination unit generates multiple proposals simultaneously, for example, using a generation AI. For example, it generates multiple illustrations of different background images or subjects, allowing the user to select one. In addition, to generate multiple proposals simultaneously, the effect combination unit inputs prompt data to the generation AI and generates multiple images based on the prompt data. For example, it proposes multiple combinations of different effects. In addition, when generating multiple proposals simultaneously using the generation AI, the effect combination unit reflects specific requirements specified by the user. For example, it generates multiple proposals tailored to a specific theme or style. This allows multiple proposals to be generated simultaneously, allowing the user to quickly select the optimal proposal.

[0072] The effect combination unit can adapt the production process to different platforms, improving user convenience. For example, the effect combination unit uses a generation AI to adapt the production process to mobile apps and web apps. For example, it builds a production process optimized for operation on a smartphone or tablet. To adapt the production process to different platforms, the effect combination unit inputs platform data into the generation AI and optimizes the production process based on that data. For example, it automatically generates an interface for mobile devices. Furthermore, the effect combination unit uses a generation AI to reflect specific requirements specified by the user when adapting the production process to different platforms. For example, it builds a production process optimized for a specific device or OS. This allows the production process to adapt to different platforms, improving user convenience.

[0073] The effects combination unit can adapt the production process to different languages ​​or cultures and generate proposals from a global perspective. The effects combination unit, for example, uses a generative AI to adapt the production process to different languages ​​and cultures. For example, it builds a production process that supports multiple languages, such as English, French, and Chinese. To adapt to different languages ​​and cultures, the effects combination unit inputs language data and cultural data into the generative AI and generates proposals from a global perspective based on that data. For example, it generates images that reflect landscapes and cultural elements unique to a region. The effects combination unit also uses the generative AI to reflect specific requirements specified by the user when adapting the production process to different languages ​​and cultures. For example, it builds a production process optimized for a specific region or culture. This makes it possible to adapt the production process to different languages ​​and cultures and generate proposals from a global perspective.

[0074] The effect combination unit can analyze the emotions of the user as they proceed through the creative process in real time and provide optimal support. The effect combination unit, for example, uses an emotion estimation function to analyze the emotions of the user as they proceed through the creative process in real time and provide optimal support based on the emotions. For example, if the user is feeling stressed, the effect combination unit makes suggestions that have a relaxing effect. The effect combination unit also builds a system that provides optimal support based on the user's emotion data. For example, if the user is experiencing difficulty, the effect combination unit provides specific advice and help. The effect combination unit also uses the emotion estimation function to analyze the emotions of the user as they proceed through the creative process and provides similar support by referring to data on support that the user preferred in the past. This makes it possible to analyze the emotions of the user as they proceed through the creative process in real time and provide optimal support.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The design AI system can also be equipped with a function to estimate the user's emotions and adjust the color tone of the background image based on the estimated emotion. For example, if the user wants to relax, it can generate a background image with calm colors. On the other hand, if the user wants to be energized, it can generate a background image with vivid colors. Furthermore, it can refer to data on colors that the user has previously preferred and reflect similar colors in the background image. This makes it possible to provide a background image with the optimal color tone according to the user's emotions.

[0077] The design AI system can also be equipped with a function to estimate the user's emotions and adjust the subject's movements based on the estimated emotions. For example, if the user wants to relax, it can generate a subject with slow movements. On the other hand, if the user wants to feel energized, it can generate a subject with active movements. It can also refer to data on the user's preferred movements in the past and reflect similar movements in the subject. This makes it possible to provide a subject with the optimal movements according to the user's emotions.

[0078] The design AI system can also be equipped with the ability to estimate the user's emotions and adjust the combination of effects based on the estimated emotions. For example, if the user wants to relax, it can combine calming effects. If the user wants to be energized, it can combine vibrant effects. It can also refer to data on effects that the user has previously preferred and combine similar effects. This makes it possible to provide the optimal combination of effects according to the user's emotions.

[0079] The design AI system can also be equipped with a function to estimate the user's emotions and suggest background image themes based on the estimated emotions. For example, if the user wants to relax, it can suggest natural scenery. If the user wants to be energized, it can suggest urban scenery. It can also refer to data on themes that the user has previously preferred and suggest similar themes. This makes it possible to provide a background image with the optimal theme according to the user's emotions.

[0080] The design AI system can also be equipped with a function to estimate the user's emotions and adjust the subject's facial expression based on the estimated emotions. For example, if the user wants to relax, it can generate a subject with a calm expression. On the other hand, if the user wants to feel energized, it can generate a subject with a lively expression. It can also refer to data on facial expressions that the user has previously preferred and reflect similar expressions in the subject. This makes it possible to provide a subject with the optimal expression according to the user's emotions.

[0081] The design AI system can also use the background image generation unit to generate background images according to specific time periods specified by the user. For example, morning scenes, daytime scenes, evening scenes, and night scenes can be generated in real time. The background image generation unit can also input time period data into the generation AI and generate background images based on that data. For example, it can generate scenes with morning light streaming in or scenes with a sunset glow. This makes it possible to provide background images according to specific time periods specified by the user.

[0082] The design AI system can further use the object generation unit to generate objects according to a specific style specified by the user. For example, it can generate objects in a variety of styles, including realistic, anime, and abstract styles. The object generation unit can also input style data into the generation AI and generate objects based on that data. For example, it can generate characters and objects that match the style specified by the user. This makes it possible to provide objects according to the specific style specified by the user.

[0083] The design AI system can further combine effects according to specific themes specified by the user in the effect combination section. For example, it can combine effects according to various themes, such as seasonal themes, event themes, and themes based on specific locations. The effect combination section can also input theme data into the generation AI and combine effects based on that data. For example, it can combine effects that match cherry blossoms in spring, the sea in summer, autumn leaves, and snowy winter scenery. This makes it possible to provide effects that match specific themes specified by the user.

[0084] The design AI system can further use the background image generation unit to generate a background image based on a specific location specified by the user. For example, it can generate a city or natural landscape specified by the user in real time. The background image generation unit can also input location data into the generation AI and generate a background image based on that data. For example, it can reproduce the landscape of a location specified by the user. This makes it possible to provide a background image based on the specific location specified by the user.

[0085] The design AI system can further use the subject generation unit to generate subjects that correspond to specific poses specified by the user. For example, it can generate characters or objects that assume specific poses specified by the user. The subject generation unit can also input pose data into the generation AI and generate subjects based on that data. For example, it can generate characters that assume specific poses specified by the user in real time. This makes it possible to provide subjects that correspond to specific poses specified by the user.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The background image generator generates a background image that evokes the image of autumn leaves. For example, the generator AI analyzes the color and shape of autumn leaves to recreate a realistic autumn foliage scene. The generator AI can also generate background images based on prompts from the user, including instructions on what the user wants the generator AI to do. Step 2: The subject generation unit generates autumn leaves and objects based on the background image generated by the background image generation unit. For example, when generating autumn leaves or tree objects, the generation AI analyzes their shape and color to generate realistic illustration materials. The generation AI can also generate illustration materials based on prompts that include instructions from the user on what the generation AI wants to do. Step 3: The effect combination unit combines various effects with the autumn leaves and objects generated by the subject generation unit to generate the optimal creative image. For example, the generation AI combines effects such as "beauty" and "blur" to generate the optimal creative image. The generation AI can also generate an image that combines effects based on prompts from the user, including instructions on what the user wants the generation AI to do.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0101] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0154] 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]

[0155] 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 background image generating unit that generates a background image representing an image of autumn leaves; a subject generation unit that generates autumn leaves or objects based on the background image generated by the background image generation unit; an effect combination unit that combines a plurality of effects with the autumn leaves or objects generated by the subject generation unit to generate an optimal creative image; A system characterized by:

2. The background image generation unit Generate background images that reflect seasonal changes in real time 2. The system of claim 1.

3. The background image generation unit Generate background images based on specific regions and cultures to recreate unique landscapes 2. The system of claim 1.

4. The background image generation unit Generate background images according to the user's emotions to elicit positive emotions 2. The system of claim 1.

5. The background image generation unit Add dynamic effects to background images for a more realistic experience 2. The system of claim 1.

Images