System

Abstract

An object of the system according to the embodiment is to quickly generate a background image, a portrait image, and a building image based on a user's style and touch.SOLUTION: A system includes a reception unit, a generation unit, and a provision unit. The reception part inputs contents that a user desires to draw. The generation unit analyzes the information input by the reception unit and generates a background image, a portrait image, or a building image on the basis of the style or touch of the user. The providing unit provides the user with the result generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was a problem in that it took a long time and cost a lot to create background images, portraits, and building images for manga and anime.

[0005] The system according to the embodiment aims to quickly generate background images, portraits, and building images based on the user's style and touch. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives input of the content the user wants to draw. The generating unit analyzes the information received by the receiving unit and generates a background image, portrait, or building image based on the user's style and touch. The providing unit provides the result generated by the generating unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can quickly generate background images, portraits, and building images based on the user's style and touch. [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) A system according to an embodiment of the present invention uses a generation AI to instantly draw backgrounds, figures, buildings, and other elements of manga and anime in the artist's own style and style. Furthermore, it also supports animation frame layout, significantly reducing production time and costs. For example, a user inputs the content they want to draw, such as the type and detailed requirements of the background, figure, or building. This information is then input into the generation AI. The generation AI then analyzes the input information and generates backgrounds, figures, and buildings based on the user's style and style. The generation AI then reproduces the user's style and style based on previously learned data. For example, it can generate backgrounds that match the style of a specific manga artist. Furthermore, the generation AI also supports animation frame layout. When a user inputs an anime scene, the generation AI divides the frames based on that scene and generates backgrounds and figures appropriate for each frame. This significantly reduces the time and cost required for animation production. This allows manga artists and animation producers to produce high-quality works in a short amount of time. For example, it significantly reduces the time required to generate backgrounds and figures, thereby reducing production costs. Furthermore, by automating the layout of animation frames, manual errors can be reduced and production can proceed more efficiently. This system is extremely useful for manga artists and anime producers, contributing to the improvement of production efficiency and quality.

[0029] A generation system according to an embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit inputs content that a user wants to draw. The content that the user wants to draw includes, but is not limited to, a background image, a portrait, or a building. The receiving unit can receive input in the form of, for example, text, an image, or audio. The generating unit uses a generation AI to analyze the information input by the receiving unit and generate a background image, portrait, or building image based on the user's drawing style and touch. The generating unit reproduces the user's drawing style and touch based on pre-trained data using technologies such as deep learning or GAN (generative artificial network). The generating unit can generate, for example, a background image that matches the drawing style of a specific manga artist. The generating unit can also divide frames based on anime scenes and generate background images and portraits appropriate for each frame. For example, the generating unit divides frames taking into account the movement of the scene and the position of the characters. The providing unit provides the results generated by the generating unit to the user. For example, the providing unit provides the user with a preview of the generated background image, portrait, or building image. The providing unit provides a preview taking into consideration, for example, the display format, resolution, user interface, etc. As a result, the generation system according to the embodiment allows a user to input content they want to draw and provides the generated result, thereby significantly reducing production time and costs.

[0030] The generation unit uses a generation AI to reproduce the user's style and touch based on pre-trained data. The generation unit uses a generation AI to reproduce the user's style and touch based on pre-trained data. The generation AI uses technologies such as deep learning and GAN (generative artificial network). The generation unit generates, for example, a background image that matches the style of a specific manga artist. The generation unit can also reproduce the style and touch based on data from the user's past works. For example, the generation unit generates a new background image by referring to background images the user has drawn in the past. The generation unit can also identify the style and touch based on information input by the user and generate the work based on that. For example, the generation unit analyzes text and images entered by the user to identify the style and touch. As a result, the generation AI can reproduce the user's style and touch based on pre-trained data, thereby generating a work that reflects the user's individuality.

[0031] The generation unit divides frames based on the scenes of the animation and generates background images or portraits suitable for each frame. The generation unit divides frames based on the scenes of the animation and generates background images or portraits suitable for each frame. The generation unit divides frames taking into consideration, for example, the length, content, and character movement of the scene. The generation unit can divide frames based on, for example, the movement of the scene and the position of the characters. For example, the generation unit adjusts the size and position of the frames according to the movement of the scene. The generation unit can also divide frames based on the position of the characters and generate background images and portraits suitable for each frame. For example, the generation unit generates background images taking into consideration the position and pose of the characters. In this way, by dividing frames based on the scenes of the animation and generating background images and portraits suitable for each frame, the efficiency of animation production can be improved.

[0032] The providing unit provides a preview of the generated background image, portrait, or building image to the user. The providing unit provides a preview of the generated background image, portrait, or building image to the user. The providing unit provides the preview taking into consideration, for example, a display format, a resolution, a user interface, and the like. For example, the providing unit displays the generated background image, portrait, or building image at high resolution. The providing unit can also customize the interface so that the user can easily check the generation results. For example, the providing unit adjusts the display method of the preview according to the user's screen size or device. In this way, providing a preview of the generated background image, portrait, or building image to the user makes it easier for the user to check the generation results.

[0033] The generation unit divides frames based on the movement of the scene or the placement of the characters. The generation unit divides frames based on the movement of the scene or the placement of the characters. The generation unit, for example, adjusts the size and placement of frames according to the movement of the scene. The generation unit can, for example, divide frames taking into account the position and pose of the characters. For example, the generation unit determines a frame division method based on the movement of the scene. The generation unit can also divide frames based on the placement of the characters and generate background images and portraits appropriate for each frame. For example, the generation unit analyzes the movement and placement of the characters and divides frames based on that. In this way, by dividing frames taking into account the movement of the scene and the placement of the characters, it is possible to generate a more natural and effective animation.

[0034] The system further includes a learning data unit that manages data previously learned by the generation AI. The learning data unit manages the data previously learned by the generation AI. The learning data unit manages data, taking into consideration, for example, how the data is stored, managed, and accessed. The learning data unit stores, for example, artwork data and data on users' past works. The learning data unit can also organize data so that the generation AI can learn efficiently. For example, the learning data unit classifies data based on the type and relevance of the data. In this way, by managing the data previously learned by the generation AI, the accuracy of generation can be improved.

[0035] The reception unit analyzes the user's past input history and suggests an appropriate input method. The reception unit analyzes the user's past input history and suggests an appropriate input method. The reception unit analyzes, for example, past input data, input frequency and patterns, etc. The reception unit automatically displays, for example, types of background images and portraits that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest an input method to be used in a specific time period based on the user's past input history. For example, the reception unit suggests the optimal input method based on data the user has input in the past. The reception unit can also accumulate the user's input history and analyze long-term input patterns. In this way, by analyzing the user's past input history, the optimal input method can be suggested and input efficiency can be improved.

[0036] The reception unit presents appropriate sample images or reference materials to the user according to the input content. The reception unit presents appropriate sample images or reference materials to the user according to the input content. The reception unit presents related sample images based on the input content, such as a background image, a portrait, or a building image. For example, when the user inputs a background image, the reception unit presents related sample images. Furthermore, when the user inputs a portrait, the reception unit can present samples of poses and facial expressions that can be used as reference. Furthermore, when the user inputs a building image, the reception unit can present reference materials for architectural styles and designs. For example, the reception unit automatically searches for and presents related sample images or reference materials based on the content input by the user. Furthermore, the reception unit can present more appropriate sample images or reference materials based on the user's past input history and feedback. In this way, by presenting appropriate sample images or reference materials according to the input content, the user can have a more concrete image.

[0037] The reception unit selects an appropriate input means according to the user's input method. The reception unit selects an appropriate input means according to the user's input method. The reception unit selects the optimal means based on the input method, such as voice input, text input, or image input. For example, when the user inputs by voice, the reception unit converts the input content into text using voice recognition technology. Furthermore, when the user inputs by text, the reception unit can analyze the input content and present an appropriate sample image. Furthermore, when the user inputs by image, the reception unit can analyze the input content using image recognition technology and provide related information. For example, the reception unit converts the content input by voice by the user into text in real time and confirms the input content. Furthermore, the reception unit analyzes the content input by the user as text and automatically searches for and presents related sample images. This allows the optimal input means to be selected according to the user's input method, thereby improving input convenience.

[0038] The reception unit presents highly relevant samples based on the input content, taking into account the user's geographical location information. The reception unit presents highly relevant samples based on the input content, taking into account the user's geographical location information. The reception unit acquires the user's geographical location information, for example, using GPS data, an IP address, a location information service, or the like. For example, if the user inputs a background image of a specific region, the reception unit presents sample images related to the region. Furthermore, if the user inputs a building image of a specific city, the reception unit can present samples related to the architectural style of the city. Furthermore, if the user inputs a portrait of a person from a specific country, the reception unit can present samples related to the culture or clothing of that country. For example, the reception unit automatically searches for and presents relevant sample images and reference materials based on the user's geographical location information. Furthermore, the reception unit can present more appropriate sample images and reference materials based on the user's past input history and feedback. In this way, by presenting highly relevant samples based on the user's geographical location information, more appropriate reference materials can be provided.

[0039] The reception unit analyzes the user's social media activity and suggests related input content. The reception unit analyzes the user's social media activity and suggests related input content. The reception unit analyzes, for example, the content of posts, the number of likes, the number of followers, etc. The reception unit, for example, suggests related background images or portraits based on images shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and suggest related building images. Furthermore, the reception unit can suggest related input content by referring to the activities of the user's friends on social media. For example, the reception unit automatically searches for and suggests related input content based on the user's social media activity. The reception unit can also suggest more appropriate input content based on the user's past input history and feedback. In this way, by analyzing the user's social media activity, related input content can be suggested and input efficiency can be improved.

[0040] The reception unit customizes the input method by reflecting the user's past feedback. The reception unit customizes the input method by reflecting the user's past feedback. The reception unit customizes the input method based on, for example, the user's ratings, comments, survey results, etc. The reception unit suggests the optimal input method based on, for example, feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. Furthermore, the reception unit can customize the input interface by reflecting the user's past feedback. For example, the reception unit adjusts the interface layout and functions based on the user's feedback. The reception unit can also accumulate user feedback and make long-term improvements. In this way, the input method can be customized by reflecting the user's past feedback, thereby improving user convenience.

[0041] The generation unit improves the accuracy of generation based on the user's past works during generation. The generation unit improves the accuracy of generation based on the user's past works during generation. For example, the generation unit generates a new background image by referring to a background image drawn by the user in the past. The generation unit can also generate a new portrait by referring to a portrait drawn by the user in the past. The generation unit can also generate a new building image by referring to a building image drawn by the user in the past. For example, the generation unit improves the accuracy of generation based on the user's past work data. The generation unit can also analyze the user's past works and identify the painting style and touch. In this way, the accuracy of generation can be improved by referring to the user's past works.

[0042] The generation unit applies different generation algorithms based on the theme and style specified by the user during generation. The generation unit applies different generation algorithms based on the theme and style specified by the user during generation. For example, the generation unit applies an algorithm to generate a specific background image based on the theme specified by the user. The generation unit can also apply an algorithm to generate a specific portrait based on the style specified by the user. The generation unit can also apply an algorithm to generate a specific building image based on the theme and style specified by the user. For example, the generation unit selects an optimal generation algorithm based on the theme and style specified by the user. The generation unit can also analyze user input and adjust the generation algorithm based on the analysis. This makes it possible to generate more appropriate images by applying different generation algorithms depending on the theme and style specified by the user.

[0043] The generation unit adjusts the level of detail of the generation based on the user's input during generation. The generation unit adjusts the level of detail of the generation based on the user's input during generation. For example, if the user inputs a detailed background image, the generation AI generates a detailed background image. Furthermore, if the user inputs a simple portrait, the generation unit can generate a simple portrait. Furthermore, if the user inputs a complex building image, the generation unit can generate a complex building image. For example, the generation unit analyzes the user's input and adjusts the level of detail of the generation based on that. Furthermore, the generation unit can adjust the resolution and level of detail of the generation according to the user's request. In this way, by adjusting the level of detail of the generation based on the user's input, a more appropriate image can be generated.

[0044] The generation unit determines the generation priority at the time of generation based on the time of submission by the user. The generation unit determines the generation priority at the time of generation based on the time of submission by the user. For example, if the user inputs a background image that is needed urgently, the generation unit causes the generation AI to generate it preferentially. Furthermore, the generation unit can also cause the generation AI to generate it preferentially if the user inputs a portrait image whose submission deadline is approaching. Furthermore, the generation unit can also cause the generation AI to generate it later if the user inputs a building image whose submission deadline is far away. For example, the generation unit determines the generation priority based on the time of submission by the user. Furthermore, the generation unit can adjust the timing of generation based on the user's project schedule. In this way, by determining the generation priority based on the time of submission by the user, generation can be performed in time for the submission deadline.

[0045] The generation unit adjusts the use of technical terminology in the generated content based on the user's level of expertise during generation. The generation unit adjusts the use of technical terminology in the generated content based on the user's level of expertise during generation. For example, if the user is a beginner, the generation unit may explain the generated content while avoiding technical terminology. Furthermore, if the user is an intermediate user, the generation unit may explain the generated content using appropriate technical terminology. Furthermore, if the user is an advanced user, the generation unit may explain the generated content using a lot of technical terminology. For example, the generation unit may evaluate the user's level of expertise based on the user's occupation, past experience, qualifications, etc. Furthermore, the generation unit may analyze the user's input content and adjust the use of technical terminology based on the analysis. In this way, by adjusting the use of technical terminology in the generated content according to the user's level of expertise, it is possible to provide a generated result that is easy for the user to understand.

[0046] The providing unit improves the accuracy of the provided image based on the user's past feedback when providing the image. The providing unit improves the accuracy of the provided image based on the user's past feedback when providing the image. The providing unit improves the accuracy of the provided image based on, for example, the user's ratings, comments, survey results, etc. The providing unit provides an optimal background image based on, for example, feedback provided by the user in the past. The providing unit can also preferentially provide a specific portrait based on the user's past feedback. Furthermore, the providing unit can customize the provided interface by reflecting the user's past feedback. For example, the providing unit adjusts the layout and functions of the interface based on the user's feedback. The providing unit can also accumulate user feedback and make long-term improvements. In this way, the accuracy of the provided image can be improved by referring to the user's past feedback.

[0047] The providing unit customizes the provided content based on the user's current project at the time of providing. The providing unit customizes the provided content based on the user's current project at the time of providing. The providing unit customizes the provided content based on, for example, the project's purpose, progress, related data, etc. The providing unit, for example, provides a background image related to the user's current project. The providing unit can also provide a portrait related to the user's current project. The providing unit can also provide a building image related to the user's current project. For example, the providing unit automatically searches for and provides related images based on the progress of the user's project. The providing unit can also adjust the provided content according to the user's project purpose. This enables more appropriate provision by customizing the provided content according to the user's current project.

[0048] The providing unit adjusts the level of detail of the provided content based on the user's input when providing the content. The providing unit adjusts the level of detail of the provided content based on the user's input when providing the content. For example, if the user inputs a detailed background image, the providing unit provides a detailed background image. Furthermore, if the user inputs a simple portrait, the providing unit can provide a simple portrait. Furthermore, if the user inputs a complex building image, the providing unit can provide a complex building image. For example, the providing unit analyzes the user's input and adjusts the level of detail of the provided content based on the analysis. Furthermore, the providing unit can adjust the resolution and level of detail of the provided content according to the user's request. This enables more appropriate provision by adjusting the level of detail of the provided content based on the user's input.

[0049] The providing unit, when providing images, prioritizes providing highly relevant images based on the user's geographical location information. The providing unit, when providing images, prioritizes providing highly relevant images based on the user's geographical location information. The providing unit acquires the user's geographical location information using, for example, GPS data, an IP address, a location information service, or the like. For example, if the user inputs a background image of a specific region, the providing unit prioritizes providing images related to that region. Furthermore, if the user inputs a building image of a specific city, the providing unit can prioritize providing images related to the architectural style of that city. Furthermore, if the user inputs a portrait image of a specific country, the providing unit can prioritize providing images related to the culture and clothing of that country. For example, the providing unit automatically searches for and provides relevant images based on the user's geographical location information. Furthermore, the providing unit can provide more appropriate images based on the user's past input history and feedback. This enables more appropriate provision by preferentially providing highly relevant images taking the user's geographical location information into consideration.

[0050] The providing unit analyzes the user's social media activity at the time of providing and provides related images. The providing unit analyzes the user's social media activity at the time of providing and provides related images. The providing unit analyzes, for example, the content of posts, the number of likes, the number of followers, etc. The providing unit provides, for example, related background images and portraits based on images shared by the user on social media. The providing unit can also analyze the content of the user's social media posts and provide related building images. Furthermore, the providing unit can provide related images by referring to the activity of the user's friends on social media. For example, the providing unit automatically searches for and provides related images based on the user's social media activity. The providing unit can also provide more appropriate images based on the user's past input history and feedback. In this way, by analyzing the user's social media activity, related images can be provided and the accuracy of the provision can be improved.

[0051] The providing unit customizes the delivery method based on the user's past feedback at the time of delivery. The providing unit customizes the delivery method based on the user's past feedback at the time of delivery. The providing unit customizes the delivery method based on, for example, the user's ratings, comments, survey results, etc. The providing unit suggests the optimal delivery method based on, for example, feedback previously provided by the user. The providing unit can also preferentially suggest a specific delivery method based on the user's past feedback. Furthermore, the providing unit can customize the delivery interface by reflecting the user's past feedback. For example, the providing unit adjusts the interface layout and functions based on the user's feedback. The providing unit can also accumulate user feedback and make long-term improvements. In this way, the delivery method can be customized by reflecting the user's past feedback, thereby improving the accuracy of delivery.

[0052] The learning data unit optimizes the learning algorithm based on past learning data during learning. The learning data unit optimizes the learning algorithm based on past learning data during learning. The learning data unit, for example, applies an optimal background image generation algorithm based on past learning data. The learning data unit can also apply an optimal human image generation algorithm based on past learning data. The learning data unit can also apply an optimal building image generation algorithm based on past learning data. For example, the learning data unit refers to past learning data and optimizes the learning algorithm based on the data. The learning data unit can also adjust the learning algorithm by reflecting user feedback. In this way, by referring to past learning data, the learning algorithm can be optimized and the generation accuracy can be improved.

[0053] The training data unit updates the training data based on user feedback during training. The training data unit updates the training data based on user feedback during training. The training data unit updates the training data based on, for example, feedback provided by the user. The training data unit can also preferentially update specific training data based on user feedback. Furthermore, the training data unit can also optimize the learning algorithm by reflecting user feedback. For example, the training data unit adds, modifies, or deletes training data based on user feedback. The training data unit can also accumulate user feedback and make long-term improvements. In this way, the training data can be updated by reflecting user feedback, thereby improving the accuracy of generation.

[0054] The learning data unit weights the learning data during learning based on the time of submission by the user. The learning data unit weights the learning data during learning based on the time of submission by the user. For example, the learning data unit prioritizes weighting of learning data that the user needs urgently. The learning data unit can also prioritize weighting of learning data for which the user's submission deadline is approaching. Furthermore, the learning data unit can also prioritize weighting of learning data for which the user's submission deadline is further away. For example, the learning data unit weights the learning data based on the time of submission by the user. The learning data unit can also adjust the priority of the learning data based on the user's project schedule. In this way, by weighting the learning data based on the time of submission by the user, learning can be completed in time for the submission deadline.

[0055] The training data unit enriches the training data based on information from different data sources during training. The training data unit enriches the training data based on information from different data sources during training. The training data unit integrates information from different data sources, such as an external database, an API, or a cloud service. The training data unit enriches the training data by integrating, for example, background image information from different data sources. The training data unit can also enrich the training data by integrating portrait information from different data sources. The training data unit can also enrich the training data by integrating building image information from different data sources. For example, the training data unit automatically collects information from an external database and adds it to the training data. The training data unit can also obtain data in real time via an API and update the training data. In this way, by integrating information from different data sources, the training data can be enriched and the generation accuracy can be improved.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The reception unit can also provide related trend information based on the user's input. For example, if the user inputs a specific background image, the reception unit can present the latest trends and popular styles related to that background image. If the user inputs a portrait, the reception unit can also provide information on current fashion and hairstyle trends. Furthermore, if the user inputs a building image, the reception unit can also present information on the latest architectural design and interior design trends. This allows the user to create more attractive works while referring to the latest trends.

[0058] The generation unit can also provide an interface that allows the user to make real-time corrections to the images generated by the generation AI. For example, if the user wants to change part of a generated background image, the user can make the correction directly through the interface. The generation unit can also make real-time corrections through the interface if the user wants to change the pose or expression of a portrait. Furthermore, the generation unit can also make corrections through the interface if the user wants to change the design or placement of a building image. This allows the user to customize the generated image to their own preferences.

[0059] The providing unit may also have an interface that allows users to provide feedback on generated images. For example, users can input comments and ratings on generated background images. The providing unit also allows users to input corrections and improvements to portraits. Furthermore, the providing unit allows users to provide specific feedback on building images. This allows for collecting user feedback and improving the accuracy and quality of the generation AI.

[0060] The training data unit can also integrate data from different cultures and regions to improve the diversity of the generative AI. For example, traditional Asian background paintings and European architectural styles can be added to the training data. The training data unit can also integrate art styles and designs from different eras. Furthermore, the training data unit can provide appropriate data when a user generates a work related to a specific culture or region. This allows the generative AI to generate works that correspond to a variety of cultures and styles.

[0061] The reception unit can also analyze the user's past input history and suggest an appropriate input method. For example, it can automatically display as candidates the types of background images or portraits that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the input method that will be used during a specific time period based on the user's past input history. In this way, by analyzing the user's past input history, it is possible to suggest the optimal input method and improve input efficiency.

[0062] The reception unit can also present appropriate sample images or reference materials to the user depending on the input content. For example, related sample images are presented based on the input content such as a background image, a portrait, or a building image. If the user inputs a background image, related sample images are presented. Also, if the user inputs a portrait, reference pose and facial expression samples can be presented. Furthermore, if the user inputs a building image, reference materials for architectural styles and designs can be presented. In this way, by presenting appropriate sample images and reference materials depending on the input content, the user can have a more concrete image.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The receiving unit inputs the content that the user wants to draw. The content that the user wants to draw can include, but is not limited to, a background image, a portrait, a building, etc. The receiving unit can receive input in the form of text, image, audio, etc. Step 2: The generation unit uses generation AI to analyze the information entered by the reception unit and generate background images, portraits, and building images based on the user's style and touch. The generation unit uses technologies such as deep learning and GAN (generative artificial network) to reproduce the user's style and touch based on pre-trained data. For example, it can generate background images that match the style of a specific manga artist, or divide frames based on anime scenes and generate background images and portraits that are appropriate for each frame. Step 3: The providing unit provides the results generated by the generating unit to the user. The providing unit provides the user with previews of the generated background images, portraits, and building images. The providing unit provides the previews taking into consideration the display format, resolution, user interface, etc.

[0065] (Example 2) A system according to an embodiment of the present invention uses a generation AI to instantly draw backgrounds, figures, buildings, and other elements of manga and anime in the artist's own style and style. Furthermore, it also supports animation frame layout, significantly reducing production time and costs. For example, a user inputs the content they want to draw, such as the type and detailed requirements of the background, figure, or building. This information is then input into the generation AI. The generation AI then analyzes the input information and generates backgrounds, figures, and buildings based on the user's style and style. The generation AI then reproduces the user's style and style based on previously learned data. For example, it can generate backgrounds that match the style of a specific manga artist. Furthermore, the generation AI also supports animation frame layout. When a user inputs an anime scene, the generation AI divides the frames based on that scene and generates backgrounds and figures appropriate for each frame. This significantly reduces the time and cost required for animation production. This allows manga artists and animation producers to produce high-quality works in a short amount of time. For example, it significantly reduces the time required to generate backgrounds and figures, thereby reducing production costs. Furthermore, by automating the layout of animation frames, manual errors can be reduced and production can proceed more efficiently. This system is extremely useful for manga artists and anime producers, contributing to the improvement of production efficiency and quality.

[0066] A generation system according to an embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit inputs content that a user wants to draw. The content that the user wants to draw includes, but is not limited to, a background image, a portrait, or a building. The receiving unit can receive input in the form of, for example, text, an image, or audio. The generating unit uses a generation AI to analyze the information input by the receiving unit and generate a background image, portrait, or building image based on the user's drawing style and touch. The generating unit reproduces the user's drawing style and touch based on pre-trained data using technologies such as deep learning or GAN (generative artificial network). The generating unit can generate, for example, a background image that matches the drawing style of a specific manga artist. The generating unit can also divide frames based on anime scenes and generate background images and portraits appropriate for each frame. For example, the generating unit divides frames taking into account the movement of the scene and the position of the characters. The providing unit provides the results generated by the generating unit to the user. For example, the providing unit provides the user with a preview of the generated background image, portrait, or building image. The providing unit provides a preview taking into consideration, for example, the display format, resolution, user interface, etc. As a result, the generation system according to the embodiment allows a user to input content they want to draw and provides the generated result, thereby significantly reducing production time and costs.

[0067] The generation unit uses a generation AI to reproduce the user's style and touch based on pre-trained data. The generation unit uses a generation AI to reproduce the user's style and touch based on pre-trained data. The generation AI uses technologies such as deep learning and GAN (generative artificial network). The generation unit generates, for example, a background image that matches the style of a specific manga artist. The generation unit can also reproduce the style and touch based on data from the user's past works. For example, the generation unit generates a new background image by referring to background images the user has drawn in the past. The generation unit can also identify the style and touch based on information input by the user and generate the work based on that. For example, the generation unit analyzes text and images entered by the user to identify the style and touch. As a result, the generation AI can reproduce the user's style and touch based on pre-trained data, thereby generating a work that reflects the user's individuality.

[0068] The generation unit divides frames based on the scenes of the animation and generates background images or portraits suitable for each frame. The generation unit divides frames based on the scenes of the animation and generates background images or portraits suitable for each frame. The generation unit divides frames taking into consideration, for example, the length, content, and character movement of the scene. The generation unit can divide frames based on, for example, the movement of the scene and the position of the characters. For example, the generation unit adjusts the size and position of the frames according to the movement of the scene. The generation unit can also divide frames based on the position of the characters and generate background images and portraits suitable for each frame. For example, the generation unit generates background images taking into consideration the position and pose of the characters. In this way, by dividing frames based on the scenes of the animation and generating background images and portraits suitable for each frame, the efficiency of animation production can be improved.

[0069] The providing unit provides a preview of the generated background image, portrait, or building image to the user. The providing unit provides a preview of the generated background image, portrait, or building image to the user. The providing unit provides the preview taking into consideration, for example, a display format, a resolution, a user interface, and the like. For example, the providing unit displays the generated background image, portrait, or building image at high resolution. The providing unit can also customize the interface so that the user can easily check the generation results. For example, the providing unit adjusts the display method of the preview according to the user's screen size or device. In this way, providing a preview of the generated background image, portrait, or building image to the user makes it easier for the user to check the generation results.

[0070] The generation unit divides frames based on the movement of the scene or the placement of the characters. The generation unit divides frames based on the movement of the scene or the placement of the characters. The generation unit, for example, adjusts the size and placement of frames according to the movement of the scene. The generation unit can, for example, divide frames taking into account the position and pose of the characters. For example, the generation unit determines a frame division method based on the movement of the scene. The generation unit can also divide frames based on the placement of the characters and generate background images and portraits appropriate for each frame. For example, the generation unit analyzes the movement and placement of the characters and divides frames based on that. In this way, by dividing frames taking into account the movement of the scene and the placement of the characters, it is possible to generate a more natural and effective animation.

[0071] The system further includes a learning data unit that manages data previously learned by the generation AI. The learning data unit manages the data previously learned by the generation AI. The learning data unit manages data, taking into consideration, for example, how the data is stored, managed, and accessed. The learning data unit stores, for example, artwork data and data on users' past works. The learning data unit can also organize data so that the generation AI can learn efficiently. For example, the learning data unit classifies data based on the type and relevance of the data. In this way, by managing the data previously learned by the generation AI, the accuracy of generation can be improved.

[0072] The reception unit estimates the user's emotions and prompts the user to confirm or correct the input content based on the estimated user emotions. The reception unit estimates the user's emotions and prompts the user to confirm or correct the input content based on the estimated user emotions. The reception unit estimates emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input, allowing the user to quickly confirm and correct the input content. For example, the reception unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. This allows the user to confirm or correct the input content based on the user's emotions, enabling more appropriate input.

[0073] The reception unit analyzes the user's past input history and suggests an appropriate input method. The reception unit analyzes the user's past input history and suggests an appropriate input method. The reception unit analyzes, for example, past input data, input frequency and patterns, etc. The reception unit automatically displays, for example, types of background images and portraits that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest an input method to be used in a specific time period based on the user's past input history. For example, the reception unit suggests the optimal input method based on data the user has input in the past. The reception unit can also accumulate the user's input history and analyze long-term input patterns. In this way, by analyzing the user's past input history, the optimal input method can be suggested and input efficiency can be improved.

[0074] The reception unit presents appropriate sample images or reference materials to the user according to the input content. The reception unit presents appropriate sample images or reference materials to the user according to the input content. The reception unit presents related sample images based on the input content, such as a background image, a portrait, or a building image. For example, when the user inputs a background image, the reception unit presents related sample images. Furthermore, when the user inputs a portrait, the reception unit can present samples of poses and facial expressions that can be used as reference. Furthermore, when the user inputs a building image, the reception unit can present reference materials for architectural styles and designs. For example, the reception unit automatically searches for and presents related sample images or reference materials based on the content input by the user. Furthermore, the reception unit can present more appropriate sample images or reference materials based on the user's past input history and feedback. In this way, by presenting appropriate sample images or reference materials according to the input content, the user can have a more concrete image.

[0075] The reception unit selects an appropriate input means according to the user's input method. The reception unit selects an appropriate input means according to the user's input method. The reception unit selects the optimal means based on the input method, such as voice input, text input, or image input. For example, when the user inputs by voice, the reception unit converts the input content into text using voice recognition technology. Furthermore, when the user inputs by text, the reception unit can analyze the input content and present an appropriate sample image. Furthermore, when the user inputs by image, the reception unit can analyze the input content using image recognition technology and provide related information. For example, the reception unit converts the content input by voice by the user into text in real time and confirms the input content. Furthermore, the reception unit analyzes the content input by the user as text and automatically searches for and presents related sample images. This allows the optimal input means to be selected according to the user's input method, thereby improving input convenience.

[0076] The reception unit estimates the user's emotion and determines the priority of input content based on the estimated user emotion. The reception unit estimates the user's emotion and determines the priority of input content based on the estimated user emotion. The reception unit estimates the emotion using technologies such as facial expression recognition, voice analysis, and text analysis. For example, when the user is feeling stressed, the reception unit prioritizes checking important input content. Furthermore, when the user is relaxed, the reception unit can prioritize checking detailed input content. Furthermore, when the user is in a hurry, the reception unit can prioritize checking input content that requires quick processing. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, the reception unit can record the user's voice and estimate the emotion using voice analysis technology. In this way, by determining the priority of input content according to the user's emotion, important content can be processed preferentially.

[0077] The reception unit presents highly relevant samples based on the input content, taking into account the user's geographical location information. The reception unit presents highly relevant samples based on the input content, taking into account the user's geographical location information. The reception unit acquires the user's geographical location information, for example, using GPS data, an IP address, a location information service, or the like. For example, if the user inputs a background image of a specific region, the reception unit presents sample images related to the region. Furthermore, if the user inputs a building image of a specific city, the reception unit can present samples related to the architectural style of the city. Furthermore, if the user inputs a portrait of a person from a specific country, the reception unit can present samples related to the culture or clothing of that country. For example, the reception unit automatically searches for and presents relevant sample images and reference materials based on the user's geographical location information. Furthermore, the reception unit can present more appropriate sample images and reference materials based on the user's past input history and feedback. In this way, by presenting highly relevant samples based on the user's geographical location information, more appropriate reference materials can be provided.

[0078] The reception unit analyzes the user's social media activity and suggests related input content. The reception unit analyzes the user's social media activity and suggests related input content. The reception unit analyzes, for example, the content of posts, the number of likes, the number of followers, etc. The reception unit, for example, suggests related background images or portraits based on images shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and suggest related building images. Furthermore, the reception unit can suggest related input content by referring to the activities of the user's friends on social media. For example, the reception unit automatically searches for and suggests related input content based on the user's social media activity. The reception unit can also suggest more appropriate input content based on the user's past input history and feedback. In this way, by analyzing the user's social media activity, related input content can be suggested and input efficiency can be improved.

[0079] The reception unit customizes the input method by reflecting the user's past feedback. The reception unit customizes the input method by reflecting the user's past feedback. The reception unit customizes the input method based on, for example, the user's ratings, comments, survey results, etc. The reception unit suggests the optimal input method based on, for example, feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. Furthermore, the reception unit can customize the input interface by reflecting the user's past feedback. For example, the reception unit adjusts the interface layout and functions based on the user's feedback. The reception unit can also accumulate user feedback and make long-term improvements. In this way, the input method can be customized by reflecting the user's past feedback, thereby improving user convenience.

[0080] The generation unit estimates the user's emotion and adjusts the representation of the generated image based on the estimated user emotion. The generation unit estimates the user's emotion and adjusts the representation of the generated image based on the estimated user emotion. The generation unit estimates the emotion using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is relaxed, the generation unit generates a background image with soft colors. Furthermore, if the user is excited, the generation unit can generate a portrait image with vivid colors. Furthermore, if the user is stressed, the generation unit can generate a building image with calm colors. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, the generation unit can record the user's voice and estimate the emotion using voice analysis technology. In this way, by adjusting the representation of the generated image according to the user's emotion, more appropriate images can be generated.

[0081] The generation unit improves the accuracy of generation based on the user's past works during generation. The generation unit improves the accuracy of generation based on the user's past works during generation. For example, the generation unit generates a new background image by referring to a background image drawn by the user in the past. The generation unit can also generate a new portrait by referring to a portrait drawn by the user in the past. The generation unit can also generate a new building image by referring to a building image drawn by the user in the past. For example, the generation unit improves the accuracy of generation based on the user's past work data. The generation unit can also analyze the user's past works and identify the painting style and touch. In this way, the accuracy of generation can be improved by referring to the user's past works.

[0082] The generation unit applies different generation algorithms based on the theme and style specified by the user during generation. The generation unit applies different generation algorithms based on the theme and style specified by the user during generation. For example, the generation unit applies an algorithm to generate a specific background image based on the theme specified by the user. The generation unit can also apply an algorithm to generate a specific portrait based on the style specified by the user. The generation unit can also apply an algorithm to generate a specific building image based on the theme and style specified by the user. For example, the generation unit selects an optimal generation algorithm based on the theme and style specified by the user. The generation unit can also analyze user input and adjust the generation algorithm based on the analysis. This makes it possible to generate more appropriate images by applying different generation algorithms depending on the theme and style specified by the user.

[0083] The generation unit adjusts the level of detail of the generation based on the user's input during generation. The generation unit adjusts the level of detail of the generation based on the user's input during generation. For example, if the user inputs a detailed background image, the generation AI generates a detailed background image. Furthermore, if the user inputs a simple portrait, the generation unit can generate a simple portrait. Furthermore, if the user inputs a complex building image, the generation unit can generate a complex building image. For example, the generation unit analyzes the user's input and adjusts the level of detail of the generation based on that. Furthermore, the generation unit can adjust the resolution and level of detail of the generation according to the user's request. In this way, by adjusting the level of detail of the generation based on the user's input, a more appropriate image can be generated.

[0084] The generation unit estimates the user's emotion and adjusts the length and size of the generated image based on the estimated user emotion. The generation unit estimates the user's emotion and adjusts the length and size of the generated image based on the estimated user emotion. The generation unit estimates the emotion using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the generation unit generates a longer background image when the user is relaxed. The generation unit can also generate a shorter portrait image when the user is in a hurry. Furthermore, the generation unit can generate a larger building image when the user is excited. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. In this way, more appropriate images can be generated by adjusting the length and size of the generated image according to the user's emotion.

[0085] The generation unit determines the generation priority at the time of generation based on the time of submission by the user. The generation unit determines the generation priority at the time of generation based on the time of submission by the user. For example, if the user inputs a background image that is needed urgently, the generation unit causes the generation AI to generate it preferentially. Furthermore, the generation unit can also cause the generation AI to generate it preferentially if the user inputs a portrait image whose submission deadline is approaching. Furthermore, the generation unit can also cause the generation AI to generate it later if the user inputs a building image whose submission deadline is far away. For example, the generation unit determines the generation priority based on the time of submission by the user. Furthermore, the generation unit can adjust the timing of generation based on the user's project schedule. In this way, by determining the generation priority based on the time of submission by the user, generation can be performed in time for the submission deadline.

[0086] The generation unit adjusts the use of technical terminology in the generated content based on the user's level of expertise during generation. The generation unit adjusts the use of technical terminology in the generated content based on the user's level of expertise during generation. For example, if the user is a beginner, the generation unit may explain the generated content while avoiding technical terminology. Furthermore, if the user is an intermediate user, the generation unit may explain the generated content using appropriate technical terminology. Furthermore, if the user is an advanced user, the generation unit may explain the generated content using a lot of technical terminology. For example, the generation unit may evaluate the user's level of expertise based on the user's occupation, past experience, qualifications, etc. Furthermore, the generation unit may analyze the user's input content and adjust the use of technical terminology based on the analysis. In this way, by adjusting the use of technical terminology in the generated content according to the user's level of expertise, it is possible to provide a generated result that is easy for the user to understand.

[0087] The providing unit estimates the user's emotion and adjusts the display method of the image to be provided based on the estimated user's emotion. The providing unit estimates the user's emotion and adjusts the display method of the image to be provided based on the estimated user's emotion. The providing unit estimates the emotion using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is relaxed, the providing unit displays a background image with soft colors. Furthermore, if the user is excited, the providing unit can display a portrait image with vivid colors. Furthermore, if the user is stressed, the providing unit can display a building image with calm colors. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, the providing unit can record the user's voice and estimate the emotion using voice analysis technology. This allows for more appropriate display by adjusting the display method of the image to be provided according to the user's emotion.

[0088] The providing unit improves the accuracy of the provided image based on the user's past feedback when providing the image. The providing unit improves the accuracy of the provided image based on the user's past feedback when providing the image. The providing unit improves the accuracy of the provided image based on, for example, the user's ratings, comments, survey results, etc. The providing unit provides an optimal background image based on, for example, feedback provided by the user in the past. The providing unit can also preferentially provide a specific portrait based on the user's past feedback. Furthermore, the providing unit can customize the provided interface by reflecting the user's past feedback. For example, the providing unit adjusts the layout and functions of the interface based on the user's feedback. The providing unit can also accumulate user feedback and make long-term improvements. In this way, the accuracy of the provided image can be improved by referring to the user's past feedback.

[0089] The providing unit customizes the provided content based on the user's current project at the time of providing. The providing unit customizes the provided content based on the user's current project at the time of providing. The providing unit customizes the provided content based on, for example, the project's purpose, progress, related data, etc. The providing unit, for example, provides a background image related to the user's current project. The providing unit can also provide a portrait related to the user's current project. The providing unit can also provide a building image related to the user's current project. For example, the providing unit automatically searches for and provides related images based on the progress of the user's project. The providing unit can also adjust the provided content according to the user's project purpose. This enables more appropriate provision by customizing the provided content according to the user's current project.

[0090] The providing unit adjusts the level of detail of the provided content based on the user's input when providing the content. The providing unit adjusts the level of detail of the provided content based on the user's input when providing the content. For example, if the user inputs a detailed background image, the providing unit provides a detailed background image. Furthermore, if the user inputs a simple portrait, the providing unit can provide a simple portrait. Furthermore, if the user inputs a complex building image, the providing unit can provide a complex building image. For example, the providing unit analyzes the user's input and adjusts the level of detail of the provided content based on the analysis. Furthermore, the providing unit can adjust the resolution and level of detail of the provided content according to the user's request. This enables more appropriate provision by adjusting the level of detail of the provided content based on the user's input.

[0091] The providing unit estimates the user's emotion and determines the priority of images to be provided based on the estimated user's emotion. The providing unit estimates the user's emotion and determines the priority of images to be provided based on the estimated user's emotion. The providing unit estimates the emotion using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the providing unit may prioritize providing important images. Furthermore, if the user is relaxed, the providing unit may prioritize providing detailed images. Furthermore, if the user is in a hurry, the providing unit may prioritize providing images that need to be provided quickly. For example, the providing unit may capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the providing unit may record the user's voice and estimate the emotion using voice analysis technology. In this way, the priority of images to be provided can be determined according to the user's emotion, thereby enabling important images to be provided preferentially.

[0092] The providing unit, when providing images, prioritizes providing highly relevant images based on the user's geographical location information. The providing unit, when providing images, prioritizes providing highly relevant images based on the user's geographical location information. The providing unit acquires the user's geographical location information using, for example, GPS data, an IP address, a location information service, or the like. For example, if the user inputs a background image of a specific region, the providing unit prioritizes providing images related to that region. Furthermore, if the user inputs a building image of a specific city, the providing unit can prioritize providing images related to the architectural style of that city. Furthermore, if the user inputs a portrait image of a specific country, the providing unit can prioritize providing images related to the culture and clothing of that country. For example, the providing unit automatically searches for and provides relevant images based on the user's geographical location information. Furthermore, the providing unit can provide more appropriate images based on the user's past input history and feedback. This enables more appropriate provision by preferentially providing highly relevant images taking the user's geographical location information into consideration.

[0093] The providing unit analyzes the user's social media activity at the time of providing and provides related images. The providing unit analyzes the user's social media activity at the time of providing and provides related images. The providing unit analyzes, for example, the content of posts, the number of likes, the number of followers, etc. The providing unit provides, for example, related background images and portraits based on images shared by the user on social media. The providing unit can also analyze the content of the user's social media posts and provide related building images. Furthermore, the providing unit can provide related images by referring to the activity of the user's friends on social media. For example, the providing unit automatically searches for and provides related images based on the user's social media activity. The providing unit can also provide more appropriate images based on the user's past input history and feedback. In this way, by analyzing the user's social media activity, related images can be provided and the accuracy of the provision can be improved.

[0094] The providing unit customizes the delivery method based on the user's past feedback at the time of delivery. The providing unit customizes the delivery method based on the user's past feedback at the time of delivery. The providing unit customizes the delivery method based on, for example, the user's ratings, comments, survey results, etc. The providing unit suggests the optimal delivery method based on, for example, feedback previously provided by the user. The providing unit can also preferentially suggest a specific delivery method based on the user's past feedback. Furthermore, the providing unit can customize the delivery interface by reflecting the user's past feedback. For example, the providing unit adjusts the interface layout and functions based on the user's feedback. The providing unit can also accumulate user feedback and make long-term improvements. In this way, the delivery method can be customized by reflecting the user's past feedback, thereby improving the accuracy of delivery.

[0095] The training data unit estimates the user's emotion and selects training data based on the estimated user's emotion. The training data unit estimates the user's emotion and selects training data based on the estimated user's emotion. The training data unit estimates the emotion using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is relaxed, the training data unit selects training data with soft colors. Furthermore, if the user is excited, the training data unit can select training data with bright colors. Furthermore, if the user is stressed, the training data unit can select training data with subdued colors. For example, the training data unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, the training data unit can record the user's voice and estimate the emotion using voice analysis technology. In this way, by selecting training data according to the user's emotion, more appropriate training data can be used.

[0096] The learning data unit optimizes the learning algorithm based on past learning data during learning. The learning data unit optimizes the learning algorithm based on past learning data during learning. The learning data unit, for example, applies an optimal background image generation algorithm based on past learning data. The learning data unit can also apply an optimal human image generation algorithm based on past learning data. The learning data unit can also apply an optimal building image generation algorithm based on past learning data. For example, the learning data unit refers to past learning data and optimizes the learning algorithm based on the data. The learning data unit can also adjust the learning algorithm by reflecting user feedback. In this way, by referring to past learning data, the learning algorithm can be optimized and the generation accuracy can be improved.

[0097] The training data unit updates the training data based on user feedback during training. The training data unit updates the training data based on user feedback during training. The training data unit updates the training data based on, for example, feedback provided by the user. The training data unit can also preferentially update specific training data based on user feedback. Furthermore, the training data unit can also optimize the learning algorithm by reflecting user feedback. For example, the training data unit adds, modifies, or deletes training data based on user feedback. The training data unit can also accumulate user feedback and make long-term improvements. In this way, the training data can be updated by reflecting user feedback, thereby improving the accuracy of generation.

[0098] The learning data unit estimates the user's emotions and adjusts the frequency of learning based on the estimated user emotions. The learning data unit estimates the user's emotions and adjusts the frequency of learning based on the estimated user emotions. The learning data unit estimates emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the learning data unit increases the frequency of learning when the user is relaxed. The learning data unit can also decrease the frequency of learning when the user is stressed. Furthermore, the learning data unit can adjust the frequency of learning when the user is excited. For example, the learning data unit captures the user's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. The learning data unit can also record the user's voice and estimate the emotion using voice analysis technology. This enables more appropriate learning by adjusting the frequency of learning according to the user's emotions.

[0099] The learning data unit weights the learning data during learning based on the time of submission by the user. The learning data unit weights the learning data during learning based on the time of submission by the user. For example, the learning data unit prioritizes weighting of learning data that the user needs urgently. The learning data unit can also prioritize weighting of learning data for which the user's submission deadline is approaching. Furthermore, the learning data unit can also prioritize weighting of learning data for which the user's submission deadline is further away. For example, the learning data unit weights the learning data based on the time of submission by the user. The learning data unit can also adjust the priority of the learning data based on the user's project schedule. In this way, by weighting the learning data based on the time of submission by the user, learning can be completed in time for the submission deadline.

[0100] The training data unit enriches the training data based on information from different data sources during training. The training data unit enriches the training data based on information from different data sources during training. The training data unit integrates information from different data sources, such as an external database, an API, or a cloud service. The training data unit enriches the training data by integrating, for example, background image information from different data sources. The training data unit can also enrich the training data by integrating portrait information from different data sources. The training data unit can also enrich the training data by integrating building image information from different data sources. For example, the training data unit automatically collects information from an external database and adds it to the training data. The training data unit can also obtain data in real time via an API and update the training data. In this way, by integrating information from different data sources, the training data can be enriched and the generation accuracy can be improved. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and training data unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives user input. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates background images and portraits using a generation AI. The provision unit is realized by the output device 40 of the smart device 14 and provides the generated images to the user. The training data unit is realized by the database 24 of the data processing device 12 and manages the data that the generation AI learns. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and learning data unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives user input. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates background images and portraits using a generation AI. The provision unit is realized by the speaker 240 of the smart glasses 214 and provides the generated images to the user. The learning data unit is realized by the database 24 of the data processing device 12 and manages the data learned by the generation AI. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and training data unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives input content from a user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates background images and portraits using a generation AI. The provision unit is realized by the display 343 of the headset type terminal 314 and provides the generated images to the user. The training data unit is realized by the database 24 of the data processing device 12 and manages the data that the generation AI learns. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and learning data unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives input content from a user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates background images and portraits using a generation AI. The provision unit is realized by the speaker 240 of the robot 414 and provides the generated images to the user. The learning data unit is realized by the database 24 of the data processing device 12 and manages the data learned by the generation AI.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The reception unit can also provide related trend information based on the user's input. For example, if the user inputs a specific background image, the reception unit can present the latest trends and popular styles related to that background image. If the user inputs a portrait, the reception unit can also provide information on current fashion and hairstyle trends. Furthermore, if the user inputs a building image, the reception unit can also present information on the latest architectural design and interior design trends. This allows the user to create more attractive works while referring to the latest trends.

[0103] The generation unit can also provide an interface that allows the user to make real-time corrections to the images generated by the generation AI. For example, if the user wants to change part of a generated background image, the user can make the correction directly through the interface. The generation unit can also make real-time corrections through the interface if the user wants to change the pose or expression of a portrait. Furthermore, the generation unit can also make corrections through the interface if the user wants to change the design or placement of a building image. This allows the user to customize the generated image to their own preferences.

[0104] The generation unit can also estimate the user's emotions and adjust the style of the generated image based on the estimated user emotions. For example, if the user is relaxed, a background image with soft colors and a calm atmosphere can be generated. If the user is excited, a vivid and dynamic portrait can be generated. Furthermore, if the user is stressed, a simple building image with calm colors can be generated. In this way, by adjusting the style of the generated image according to the user's emotions, more appropriate images can be provided.

[0105] The providing unit may also have an interface that allows users to provide feedback on generated images. For example, users can input comments and ratings on generated background images. The providing unit also allows users to input corrections and improvements to portraits. Furthermore, the providing unit allows users to provide specific feedback on building images. This allows for collecting user feedback and improving the accuracy and quality of the generation AI.

[0106] The generation unit can also estimate the user's emotions and adjust the details of the generated image based on the estimated user's emotions. For example, if the user is relaxed, a background image with fine details can be generated. If the user is in a hurry, a simple and quickly generated portrait image can be provided. Furthermore, if the user is excited, a complex and dynamic building image can be generated. In this way, by adjusting the details of the generated image according to the user's emotions, more appropriate images can be provided.

[0107] The training data unit can also integrate data from different cultures and regions to improve the diversity of the generative AI. For example, traditional Asian background paintings and European architectural styles can be added to the training data. The training data unit can also integrate art styles and designs from different eras. Furthermore, the training data unit can provide appropriate data when a user generates a work related to a specific culture or region. This allows the generative AI to generate works that correspond to a variety of cultures and styles.

[0108] The reception unit can estimate the user's emotions and prompt the user to confirm or correct the input content based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize the input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the user to quickly confirm and correct the input content. This allows for more appropriate input by prompting the user to confirm or correct the input content according to the user's emotions.

[0109] The reception unit can also analyze the user's past input history and suggest an appropriate input method. For example, it can automatically display as candidates the types of background images or portraits that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the input method that will be used during a specific time period based on the user's past input history. In this way, by analyzing the user's past input history, it is possible to suggest the optimal input method and improve input efficiency.

[0110] The reception unit can also present appropriate sample images or reference materials to the user depending on the input content. For example, related sample images are presented based on the input content such as a background image, a portrait, or a building image. If the user inputs a background image, related sample images are presented. Also, if the user inputs a portrait, reference pose and facial expression samples can be presented. Furthermore, if the user inputs a building image, reference materials for architectural styles and designs can be presented. In this way, by presenting appropriate sample images and reference materials depending on the input content, the user can have a more concrete image.

[0111] The reception unit can also estimate the user's emotions and prioritize input contents based on the estimated user's emotions. For example, if the user is feeling stressed, important input contents can be checked with priority. Also, if the user is relaxed, detailed input contents can be checked with priority. Furthermore, if the user is in a hurry, input contents that require quick processing can be checked with priority. In this way, by prioritizing input contents according to the user's emotions, important contents can be processed with priority.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The receiving unit inputs the content that the user wants to draw. The content that the user wants to draw can include, but is not limited to, a background image, a portrait, a building, etc. The receiving unit can receive input in the form of text, image, audio, etc. Step 2: The generation unit uses generation AI to analyze the information entered by the reception unit and generate background images, portraits, and building images based on the user's style and touch. The generation unit uses technologies such as deep learning and GAN (generative artificial network) to reproduce the user's style and touch based on pre-trained data. For example, it can generate background images that match the style of a specific manga artist, or divide frames based on anime scenes and generate background images and portraits that are appropriate for each frame. Step 3: The providing unit provides the results generated by the generating unit to the user. The providing unit provides the user with previews of the generated background images, portraits, and building images. The providing unit provides the previews taking into consideration the display format, resolution, user interface, etc.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0128] 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.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 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 reception unit for inputting the content that the user wants to draw; a generating unit that analyzes the information input by the receiving unit and generates a background image, a portrait image, or a building image based on the user's style and touch; a providing unit that provides a result generated by the generating unit to a user; Equipped with A system characterized by:

2. The generation unit Generative AI reproduces the user's style and touch based on pre-trained data 2. The system of claim 1.

3. The generation unit Divide the frames based on the anime scene and generate background or character images appropriate for each frame 2. The system of claim 1.

4. The providing unit Providing a preview of the generated background, portrait, or building image to the user 2. The system of claim 1.

5. The generation unit Frame division based on scene movement or character placement 2. The system of claim 1.

6. The generation AI further includes a learning data unit that manages data previously learned.

2. The system of claim 1.

7. The reception unit Estimates the user's emotions and prompts them to confirm or correct their input based on the estimated emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests appropriate input methods 2. The system of claim 1.

Images