system

Abstract

The system according to this embodiment aims to generate images that accurately reflect the user's requests and to modify and edit specific parts of them. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, and an editing unit. The collection unit collects user requests in an interactive format. The generation unit generates images based on the requests collected by the collection unit. The editing unit modifies and edits specific parts of the images generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to generate an image accurately reflecting the user's desire and to modify and edit specific parts.

[0005] The system according to the embodiment aims to generate an image accurately reflecting the user's desire and to modify and edit specific parts.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, a generation unit, and an editing unit. The collection unit collects the user's desire in an interactive form. The generation unit generates an image based on the desire collected by the collection unit. The editing unit modifies and edits specific parts of the image generated by the generation unit.

Advantages of the Invention

[0007] The system according to this embodiment can generate images that accurately reflect the user's requests and modify or edit specific parts of them. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An interactive image creator according to an embodiment of the present invention is a system that listens to user requests in a dialogue format and generates the optimal image. This interactive image creator allows users to communicate specific requests through dialogue, and the AI ​​generates an image based on those requests. It also includes a function to specify and modify / edit only specific parts of the generated image, enabling pinpoint adjustments requested by the user. For example, if a user requests a specific change such as "I want the background to be a blue sky," the AI ​​will modify only that part. Furthermore, the AI ​​uses machine learning to learn user preferences and can suggest more appropriate images. This improves the accuracy of image generation and increases customer satisfaction. It also improves efficiency by reducing editing time. Target users include digital content creators, advertising agencies, design studios, and media companies ranging from young adults to middle-aged individuals. These users often face frustrations such as being unable to generate images as intended and difficulty in making detailed modifications. The dialogue-based request collection and partial editing function solve these problems. The digital content production market is worth approximately 1 trillion yen annually, and demand is expected to increase with the growth of the digital advertising and media industries. With the advancement of AI technology and the ongoing digitalization of the creative industry, now is the time to enter the market. Interactive image creators enhance creative freedom and streamline the production process. They are powerful tools for transforming users' imaginations into reality. This allows interactive image creators to efficiently gather user requests and generate and edit images.

[0029] The interactive image creator according to this embodiment comprises a collection unit, a generation unit, and an editing unit. The collection unit collects user requests in a dialogue format. The collection unit can, for example, interact with the user using a chatbot to collect requests. The collection unit can also collect user requests using a voice dialogue system. For example, when the user communicates a request by voice, the collection unit converts it into text and collects it. Furthermore, the collection unit can also collect user requests in text format. For example, the collection unit provides an interface for the user to input requests in text. The generation unit generates images based on the requests collected by the collection unit using generative AI. The generation unit can, for example, generate images using a GAN (Generative Opposite Network). Furthermore, the generation unit can generate high-precision images using deep learning. For example, the generation unit generates images that conform to a specific style or theme based on the user's requests. Furthermore, the generation unit can learn the user's preferences using machine learning and suggest more appropriate images. For example, the generation unit analyzes the user's past selection history and generates the optimal image based on that. The editorial department modifies and edits specific parts of the image generated by the generation department. For example, the editorial department can provide an interface for modifying parts specified by the user. The editorial department can also apply filters and make partial drawing corrections. For example, if the user requests that the background be changed to a blue sky, the editorial department will modify only that part. Furthermore, the editorial department can provide automation tools to reduce editing time. For example, the editorial department can automate specific editing tasks to perform editing efficiently. This enables the interactive image creator according to the embodiment to efficiently collect user requests and generate and edit images.

[0030] The data collection unit collects user requests in a dialogue format. For example, the data collection unit can interact with users and collect requests using a chatbot. The chatbot uses natural language processing technology to understand user input and generate appropriate responses. When a user enters a request in text, the chatbot analyzes the content and extracts the necessary information. The data collection unit can also collect user requests using a voice dialogue system. The voice dialogue system uses speech recognition technology to convert the user's voice into text and analyze its content. For example, if a user says "I want a landscape painting" in voice, the voice dialogue system converts the voice into text and sends it to the data collection unit. Furthermore, the data collection unit can also collect user requests in text format. For example, the data collection unit provides an interface where users can enter requests in text. Users can freely enter text on the interface and convey their requests in detail. By combining these methods, the data collection unit can collect user requests from multiple angles and grasp them accurately and quickly. In addition, the data collection unit also plays a role in classifying and organizing user requests and appropriately handing them over to the generation and editing units. This allows the data collection unit to respond to diverse user needs and improve the overall efficiency of the system.

[0031] The generation unit uses generative AI to generate images based on requests collected by the collection unit. The generation unit can, for example, generate images using a GAN (Generative-Opposite Network). A GAN consists of two networks: a generative network and a discriminative network. The generative network generates a new image, and the discriminative network determines whether the image is real or fake. This allows the generative network to learn to generate more realistic images. The generation unit can also generate high-precision images using deep learning. Deep learning uses multi-layered neural networks to learn complex patterns and generate images that meet user requests. For example, the generation unit generates images that conform to a specific style or theme based on user requests. If a user requests a "night sky landscape," the generation unit will generate an image including stars, the moon, and a night scene. Furthermore, the generation unit can use machine learning to learn user preferences and suggest more appropriate images. For example, the generation unit analyzes the user's past selection history and generates the optimal image based on that. By learning the styles and themes of images the user has previously selected and generating new images based on that, it can provide images that match the user's preferences. This allows the generation unit to quickly produce high-quality images that meet user requirements, thereby improving user satisfaction.

[0032] The editorial team modifies and edits specific parts of images generated by the generation team. For example, the editorial team can provide an interface for users to modify parts they specify. Users can select a specific part of the image on the interface and issue instructions to modify that part. For example, if a user requests to "change the background to a blue sky," the editorial team will modify only that part. The editorial team can also apply filters and make partial drawing corrections. For example, if a user wants to change the color tone of an image, the editorial team will apply a filter to adjust the color tone. Furthermore, the editorial team can provide automation tools to reduce editing time. For example, the editorial team can automate certain editing tasks to perform editing efficiently. Automation tools can automatically perform specific editing tasks based on user instructions, significantly reducing editing time. This allows the editorial team to perform quick and accurate editing according to user requests, improving user satisfaction. In addition, the editorial team can collect user feedback and use it to improve the editing process. When users provide feedback on the editing results, the editorial team analyzes that feedback and identifies areas for improvement in the editing process. This allows the editorial team to consistently deliver high-quality editorial results and respond flexibly to user requests.

[0033] The image generator can learn user preferences using machine learning and suggest more appropriate images. For example, the generator can learn user preferences using a neural network. For instance, it can analyze the user's past selection history and suggest the optimal image based on that. The generator can also learn user preferences using a support vector machine. For example, it can learn preferences based on the user's survey results and suggest images based on that. Furthermore, the generator can learn user preferences using machine learning algorithms and suggest more appropriate images. For example, it can analyze a combination of the user's past selection history and survey results and suggest the optimal image based on that. This improves generation accuracy by suggesting images based on user preferences.

[0034] The generation unit can improve the accuracy of the generated image. For example, the generation unit can use techniques to improve resolution. For example, the generation unit can improve image resolution using super-resolution techniques. The generation unit can also improve image accuracy using noise reduction techniques. For example, the generation unit can remove noise from images using deep learning. Furthermore, the generation unit can also use techniques to improve detail. For example, the generation unit can improve detail by using techniques to enhance image edges. This improves the accuracy of the generated image.

[0035] The editorial department can reduce editing time. For example, they can reduce editing time by using automation tools. For instance, they can automate specific editing tasks to edit efficiently. They can also reduce editing time by using efficient editing processes. For example, they can perform editing tasks in stages to proceed efficiently. Furthermore, they can reduce editing time by using AI. For example, they can use AI to automate editing tasks to edit efficiently. This reduces editing time and enables efficient image editing.

[0036] The data collection unit can analyze a user's past request history and select the optimal collection timing. For example, the data collection unit can analyze the time periods when users previously submitted requests and collect requests during those times. For example, the data collection unit can collect requests on specific days or times based on a user's past request history. For example, the data collection unit can select the time period with the highest number of requests based on a user's past request history. This allows for the collection of requests at the optimal time based on past request history.

[0037] The data collection unit can filter requests based on the user's current projects and areas of interest. For example, the data collection unit prioritizes collecting requests related to the user's current projects. For example, the data collection unit filters relevant requests based on the user's areas of interest. For example, the data collection unit collects appropriate requests according to the progress of the user's projects. This allows for the collection of highly relevant requests by filtering requests based on current projects and areas of interest.

[0038] The data collection unit can prioritize collecting highly relevant requests by considering the user's geographical location information when collecting requests. For example, if the user is in a specific region, the data collection unit will prioritize collecting requests related to that region. For example, the data collection unit will filter highly relevant requests based on the user's geographical location information. For example, if the user is on the move, the data collection unit will collect requests based on their current location. In this way, by considering geographical location information, highly relevant requests can be prioritized.

[0039] The data collection unit can analyze users' social media activity and collect relevant requests when gathering requests. For example, the data collection unit can analyze users' social media posts and collect relevant requests. For example, the data collection unit can filter requests based on users' interests on social media. For example, the data collection unit can analyze users' social media activity history and collect the most relevant requests. In this way, relevant requests can be collected by analyzing social media activity.

[0040] The generation unit can select the optimal generation algorithm by referring to the user's past generation history when generating images. For example, the generation unit can select the optimal generation algorithm based on the image styles the user has preferred in the past. For example, the generation unit can select the most frequently used algorithm from the user's past generation history. For example, the generation unit can analyze the user's past generation history and propose the most suitable generation algorithm. In this way, the optimal generation algorithm can be selected by referring to past generation history.

[0041] The generation unit can customize the generated content based on the user's current projects and areas of interest during image generation. For example, the generation unit can generate images related to the user's current project. For example, the generation unit can generate relevant images based on the user's areas of interest. For example, the generation unit can generate appropriate images according to the progress of the user's project. By customizing the generated content based on the current project and areas of interest, more relevant images can be generated.

[0042] The generation unit can generate highly relevant images by considering the user's geographical location information during image generation. For example, if the user is in a specific region, the generation unit will generate images related to that region. For example, the generation unit will generate highly relevant images based on the user's geographical location information. For example, if the user is on the move, the generation unit will generate images based on their current location. In this way, highly relevant images can be generated by considering geographical location information.

[0043] The generation unit can analyze the user's social media activity and generate relevant images during image generation. For example, the generation unit can analyze the user's social media posts and generate relevant images. For example, the generation unit can generate images based on the user's interests on social media. For example, the generation unit can analyze the user's social media activity history and generate the most suitable image. In this way, relevant images can be generated by analyzing social media activity.

[0044] The editorial team can select the optimal editing method by referring to the user's past editing history during the editing process. For example, the editorial team can select the optimal editing method based on the editing methods the user has used in the past. For example, the editorial team can select the most frequently used editing method from the user's past editing history. For example, the editorial team can analyze the user's past editing history and propose the most suitable editing method. In this way, the optimal editing method can be selected by referring to past editing history.

[0045] The editorial team can customize the content of edits based on the user's current projects and areas of interest. For example, the editorial team can provide content relevant to the user's current project. For example, the editorial team can provide relevant content based on the user's areas of interest. For example, the editorial team can provide appropriate content according to the progress of the user's project. This allows for more relevant editing by customizing the content based on the user's current projects and areas of interest.

[0046] The editorial team can make highly relevant edits by considering the user's geographical location during the editing process. For example, if the user is in a specific region, the editorial team will make edits relevant to that region. For example, the editorial team will make highly relevant edits based on the user's geographical location. For example, if the user is on the move, the editorial team will make edits based on their current location. This makes it possible to make highly relevant edits by considering geographical location.

[0047] The editorial team can analyze users' social media activity during the editing process and make relevant edits. For example, the editorial team can analyze users' social media posts and make relevant edits. For example, the editorial team can make edits based on users' interests on social media. For example, the editorial team can analyze users' social media activity history and make optimal edits. In this way, relevant edits become possible by analyzing social media activity.

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

[0049] The data collection unit not only collects user requests through dialogue, but can also analyze users' past behavior history and usage patterns to predict potential requests. For example, the data collection unit can analyze image styles and themes that users have frequently used in the past and suggest new requests based on that. It can also predict and suggest edits that are likely to be needed next based on the edits that users have made in the past. Furthermore, the data collection unit can learn user behavior patterns and collect requests at the optimal time. This makes it possible to anticipate users' potential needs and collect requests more efficiently.

[0050] The generation unit not only generates images based on user requests, but also evaluates the quality of the generated images in real time and automatically corrects them as needed. For example, the generation unit evaluates the resolution and color tone of the generated images and automatically corrects them if they do not meet the standards. It can also evaluate the composition and balance of the generated images and adjust them as needed. Furthermore, the generation unit can incorporate user feedback in real time and optimize the generation process. This makes it possible to consistently maintain a high level of quality in the generated images.

[0051] The editorial team can not only modify and edit parts specified by the user, but also provide an interface to make the editing process more intuitive. For example, the editorial team can provide an interface that allows users to easily select specific parts of an image with touch gestures. Furthermore, the editorial team can provide a function that allows users to preview their edits in real time, enabling them to instantly check the results. In addition, the editorial team can provide an "undo" function that allows users to easily revert edits, preventing editing errors. This enables users to edit images intuitively and efficiently.

[0052] The data collection unit can not only collect user requests but also analyze users' social media activity and collect relevant requests. For example, the data collection unit can analyze users' social media posts and collect relevant requests. The data collection unit can filter requests based on users' interests on social media. Furthermore, the data collection unit can analyze users' social media activity history and collect the most relevant requests. In this way, relevant requests can be collected by analyzing social media activity.

[0053] The generation unit can generate highly relevant images by considering the user's geographical location. For example, if the user is in a specific region, the generation unit will generate images related to that region. The generation unit can generate highly relevant images based on the user's geographical location. Furthermore, if the user is on the move, the generation unit can generate images based on their current location. In this way, by considering geographical location, highly relevant images can be generated.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The collection unit collects user requests in a dialogue format. The collection unit can interact with users and collect requests using chatbots or voice dialogue systems. For example, if a user communicates a request by voice, it can be converted into text and collected. The collection unit can also provide an interface where users can input requests in text. Step 2: The generation unit generates images based on the requests collected by the collection unit. The generation unit generates images using generative AI, and can generate highly accurate images using, for example, GANs (Generative Opposite Networks) or deep learning. The generation unit can also generate images that conform to a specific style or theme based on the user's requests, learn the user's preferences using machine learning, and suggest more appropriate images. Step 3: The editorial team modifies and edits specific parts of the image generated by the generation team. The editorial team provides an interface for modifying user-specified parts, allowing for filter application and partial drawing corrections. For example, if a user requests to "change the background to a blue sky," only that part will be modified. Furthermore, the editorial team provides automation tools to reduce editing time, automating specific editing tasks for efficient editing.

[0056] (Example of form 2) An interactive image creator according to an embodiment of the present invention is a system that listens to user requests in a dialogue format and generates the optimal image. This interactive image creator allows users to communicate specific requests through dialogue, and the AI ​​generates an image based on those requests. It also includes a function to specify and modify / edit only specific parts of the generated image, enabling pinpoint adjustments requested by the user. For example, if a user requests a specific change such as "I want the background to be a blue sky," the AI ​​will modify only that part. Furthermore, the AI ​​uses machine learning to learn user preferences and can suggest more appropriate images. This improves the accuracy of image generation and increases customer satisfaction. It also improves efficiency by reducing editing time. Target users include digital content creators, advertising agencies, design studios, and media companies ranging from young adults to middle-aged individuals. These users often face frustrations such as being unable to generate images as intended and difficulty in making detailed modifications. The dialogue-based request collection and partial editing function solve these problems. The digital content production market is worth approximately 1 trillion yen annually, and demand is expected to increase with the growth of the digital advertising and media industries. With the advancement of AI technology and the ongoing digitalization of the creative industry, now is the time to enter the market. Interactive image creators enhance creative freedom and streamline the production process. They are powerful tools for transforming users' imaginations into reality. This allows interactive image creators to efficiently gather user requests and generate and edit images.

[0057] The interactive image creator according to this embodiment comprises a collection unit, a generation unit, and an editing unit. The collection unit collects user requests in a dialogue format. The collection unit can, for example, interact with the user using a chatbot to collect requests. The collection unit can also collect user requests using a voice dialogue system. For example, when the user communicates a request by voice, the collection unit converts it into text and collects it. Furthermore, the collection unit can also collect user requests in text format. For example, the collection unit provides an interface for the user to input requests in text. The generation unit generates images based on the requests collected by the collection unit using generative AI. The generation unit can, for example, generate images using a GAN (Generative Opposite Network). Furthermore, the generation unit can generate high-precision images using deep learning. For example, the generation unit generates images that conform to a specific style or theme based on the user's requests. Furthermore, the generation unit can learn the user's preferences using machine learning and suggest more appropriate images. For example, the generation unit analyzes the user's past selection history and generates the optimal image based on that. The editorial department modifies and edits specific parts of the image generated by the generation department. For example, the editorial department can provide an interface for modifying parts specified by the user. The editorial department can also apply filters and make partial drawing corrections. For example, if the user requests that the background be changed to a blue sky, the editorial department will modify only that part. Furthermore, the editorial department can provide automation tools to reduce editing time. For example, the editorial department can automate specific editing tasks to perform editing efficiently. This enables the interactive image creator according to the embodiment to efficiently collect user requests and generate and edit images.

[0058] The data collection unit collects user requests in a dialogue format. For example, the data collection unit can interact with users and collect requests using a chatbot. The chatbot uses natural language processing technology to understand user input and generate appropriate responses. When a user enters a request in text, the chatbot analyzes the content and extracts the necessary information. The data collection unit can also collect user requests using a voice dialogue system. The voice dialogue system uses speech recognition technology to convert the user's voice into text and analyze its content. For example, if a user says "I want a landscape painting" in voice, the voice dialogue system converts the voice into text and sends it to the data collection unit. Furthermore, the data collection unit can also collect user requests in text format. For example, the data collection unit provides an interface where users can enter requests in text. Users can freely enter text on the interface and convey their requests in detail. By combining these methods, the data collection unit can collect user requests from multiple angles and grasp them accurately and quickly. In addition, the data collection unit also plays a role in classifying and organizing user requests and appropriately handing them over to the generation and editing units. This allows the data collection unit to respond to diverse user needs and improve the overall efficiency of the system.

[0059] The generation unit uses generative AI to generate images based on requests collected by the collection unit. The generation unit can, for example, generate images using a GAN (Generative-Opposite Network). A GAN consists of two networks: a generative network and a discriminative network. The generative network generates a new image, and the discriminative network determines whether the image is real or fake. This allows the generative network to learn to generate more realistic images. The generation unit can also generate high-precision images using deep learning. Deep learning uses multi-layered neural networks to learn complex patterns and generate images that meet user requests. For example, the generation unit generates images that conform to a specific style or theme based on user requests. If a user requests a "night sky landscape," the generation unit will generate an image including stars, the moon, and a night scene. Furthermore, the generation unit can use machine learning to learn user preferences and suggest more appropriate images. For example, the generation unit analyzes the user's past selection history and generates the optimal image based on that. By learning the styles and themes of images the user has previously selected and generating new images based on that, it can provide images that match the user's preferences. This allows the generation unit to quickly produce high-quality images that meet user requirements, thereby improving user satisfaction.

[0060] The editorial team modifies and edits specific parts of images generated by the generation team. For example, the editorial team can provide an interface for users to modify parts they specify. Users can select a specific part of the image on the interface and issue instructions to modify that part. For example, if a user requests to "change the background to a blue sky," the editorial team will modify only that part. The editorial team can also apply filters and make partial drawing corrections. For example, if a user wants to change the color tone of an image, the editorial team will apply a filter to adjust the color tone. Furthermore, the editorial team can provide automation tools to reduce editing time. For example, the editorial team can automate certain editing tasks to perform editing efficiently. Automation tools can automatically perform specific editing tasks based on user instructions, significantly reducing editing time. This allows the editorial team to perform quick and accurate editing according to user requests, improving user satisfaction. In addition, the editorial team can collect user feedback and use it to improve the editing process. When users provide feedback on the editing results, the editorial team analyzes that feedback and identifies areas for improvement in the editing process. This allows the editorial team to consistently deliver high-quality editorial results and respond flexibly to user requests.

[0061] The image generator can learn user preferences using machine learning and suggest more appropriate images. For example, the generator can learn user preferences using a neural network. For instance, it can analyze the user's past selection history and suggest the optimal image based on that. The generator can also learn user preferences using a support vector machine. For example, it can learn preferences based on the user's survey results and suggest images based on that. Furthermore, the generator can learn user preferences using machine learning algorithms and suggest more appropriate images. For example, it can analyze a combination of the user's past selection history and survey results and suggest the optimal image based on that. This improves generation accuracy by suggesting images based on user preferences.

[0062] The generation unit can improve the accuracy of the generated image. For example, the generation unit can use techniques to improve resolution. For example, the generation unit can improve image resolution using super-resolution techniques. The generation unit can also improve image accuracy using noise reduction techniques. For example, the generation unit can remove noise from images using deep learning. Furthermore, the generation unit can also use techniques to improve detail. For example, the generation unit can improve detail by using techniques to enhance image edges. This improves the accuracy of the generated image.

[0063] The editorial department can reduce editing time. For example, they can reduce editing time by using automation tools. For instance, they can automate specific editing tasks to edit efficiently. They can also reduce editing time by using efficient editing processes. For example, they can perform editing tasks in stages to proceed efficiently. Furthermore, they can reduce editing time by using AI. For example, they can use AI to automate editing tasks to edit efficiently. This reduces editing time and enables efficient image editing.

[0064] The data collection unit can estimate the user's emotions and adjust the method of collecting requests based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect requests in a simple question format to reduce the burden. For example, if the user is relaxed, the data collection unit can ask detailed questions to elicit specific requests. For example, if the user is in a hurry, the data collection unit can prioritize voice input to quickly collect requests. This allows for the collection of more appropriate requests by adjusting the method of collecting requests according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0065] The data collection unit can analyze a user's past request history and select the optimal collection timing. For example, the data collection unit can analyze the time periods when users previously submitted requests and collect requests during those times. For example, the data collection unit can collect requests on specific days or times based on a user's past request history. For example, the data collection unit can select the time period with the highest number of requests based on a user's past request history. This allows for the collection of requests at the optimal time based on past request history.

[0066] The data collection unit can filter requests based on the user's current projects and areas of interest. For example, the data collection unit prioritizes collecting requests related to the user's current projects. For example, the data collection unit filters relevant requests based on the user's areas of interest. For example, the data collection unit collects appropriate requests according to the progress of the user's projects. This allows for the collection of highly relevant requests by filtering requests based on current projects and areas of interest.

[0067] The data collection unit can estimate the user's emotions and determine the priority of requests to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important requests. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed requests. For example, if the user is in a hurry, the data collection unit will prioritize collecting requests that require a quick response. This allows for the priority collection of important requests by determining the priority of requests according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0068] The data collection unit can prioritize collecting highly relevant requests by considering the user's geographical location information when collecting requests. For example, if the user is in a specific region, the data collection unit will prioritize collecting requests related to that region. For example, the data collection unit will filter highly relevant requests based on the user's geographical location information. For example, if the user is on the move, the data collection unit will collect requests based on their current location. In this way, by considering geographical location information, highly relevant requests can be prioritized.

[0069] The data collection unit can analyze users' social media activity and collect relevant requests when gathering requests. For example, the data collection unit can analyze users' social media posts and collect relevant requests. For example, the data collection unit can filter requests based on users' interests on social media. For example, the data collection unit can analyze users' social media activity history and collect the most relevant requests. In this way, relevant requests can be collected by analyzing social media activity.

[0070] The generation unit can estimate the user's emotions and adjust the style of the generated images based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate images with soft colors. If the user is excited, for example, the generation unit will generate images with vibrant colors. If the user is calm, for example, the generation unit will generate images with a simple and refined style. This allows for the generation of more appropriate images by adjusting the image style according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0071] The generation unit can select the optimal generation algorithm by referring to the user's past generation history when generating images. For example, the generation unit can select the optimal generation algorithm based on the image styles the user has preferred in the past. For example, the generation unit can select the most frequently used algorithm from the user's past generation history. For example, the generation unit can analyze the user's past generation history and propose the most suitable generation algorithm. In this way, the optimal generation algorithm can be selected by referring to past generation history.

[0072] The generation unit can customize the generated content based on the user's current projects and areas of interest during image generation. For example, the generation unit can generate images related to the user's current project. For example, the generation unit can generate relevant images based on the user's areas of interest. For example, the generation unit can generate appropriate images according to the progress of the user's project. By customizing the generated content based on the current project and areas of interest, more relevant images can be generated.

[0073] The generation unit can estimate the user's emotions and determine the priority of images to generate based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize generating relaxing images. For example, if the user is excited, the generation unit will prioritize generating visually stimulating images. For example, if the user is calm, the generation unit will prioritize generating simple and sophisticated images. In this way, by prioritizing images according to the user's emotions, more appropriate images can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0074] The generation unit can generate highly relevant images by considering the user's geographical location information during image generation. For example, if the user is in a specific region, the generation unit will generate images related to that region. For example, the generation unit will generate highly relevant images based on the user's geographical location information. For example, if the user is on the move, the generation unit will generate images based on their current location. In this way, highly relevant images can be generated by considering geographical location information.

[0075] The generation unit can analyze the user's social media activity and generate relevant images during image generation. For example, the generation unit can analyze the user's social media posts and generate relevant images. For example, the generation unit can generate images based on the user's interests on social media. For example, the generation unit can analyze the user's social media activity history and generate the most suitable image. In this way, relevant images can be generated by analyzing social media activity.

[0076] The editorial team can estimate the user's emotions and adjust the editing method based on the estimated emotions. For example, if the user is relaxed, the editorial team can provide detailed editing options. For example, if the user is in a hurry, the editorial team can provide simple editing options. For example, if the user is stressed, the editorial team can provide a simple editing interface. This allows for more appropriate editing by adjusting the editing method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The editorial team can select the optimal editing method by referring to the user's past editing history during the editing process. For example, the editorial team can select the optimal editing method based on the editing methods the user has used in the past. For example, the editorial team can select the most frequently used editing method from the user's past editing history. For example, the editorial team can analyze the user's past editing history and propose the most suitable editing method. In this way, the optimal editing method can be selected by referring to past editing history.

[0078] The editorial team can customize the content of edits based on the user's current projects and areas of interest. For example, the editorial team can provide content relevant to the user's current project. For example, the editorial team can provide relevant content based on the user's areas of interest. For example, the editorial team can provide appropriate content according to the progress of the user's project. This allows for more relevant editing by customizing the content based on the user's current projects and areas of interest.

[0079] The editorial team can estimate the user's emotions and prioritize which parts to edit based on those emotions. For example, if the user is stressed, the editorial team will prioritize editing important parts. If the user is relaxed, the editorial team will prioritize editing detailed parts. If the user is in a hurry, the editorial team will prioritize editing parts that require immediate attention. This allows for prioritizing important parts by determining the editing priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The editorial team can make highly relevant edits by considering the user's geographical location during the editing process. For example, if the user is in a specific region, the editorial team will make edits relevant to that region. For example, the editorial team will make highly relevant edits based on the user's geographical location. For example, if the user is on the move, the editorial team will make edits based on their current location. This makes it possible to make highly relevant edits by considering geographical location.

[0081] The editorial team can analyze users' social media activity during the editing process and make relevant edits. For example, the editorial team can analyze users' social media posts and make relevant edits. For example, the editorial team can make edits based on users' interests on social media. For example, the editorial team can analyze users' social media activity history and make optimal edits. In this way, relevant edits become possible by analyzing social media activity.

[0082] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0083] The data collection unit not only collects user requests through dialogue, but can also analyze users' past behavior history and usage patterns to predict potential requests. For example, the data collection unit can analyze image styles and themes that users have frequently used in the past and suggest new requests based on that. It can also predict and suggest edits that are likely to be needed next based on the edits that users have made in the past. Furthermore, the data collection unit can learn user behavior patterns and collect requests at the optimal time. This makes it possible to anticipate users' potential needs and collect requests more efficiently.

[0084] The generation unit not only generates images based on user requests, but also evaluates the quality of the generated images in real time and automatically corrects them as needed. For example, the generation unit evaluates the resolution and color tone of the generated images and automatically corrects them if they do not meet the standards. It can also evaluate the composition and balance of the generated images and adjust them as needed. Furthermore, the generation unit can incorporate user feedback in real time and optimize the generation process. This makes it possible to consistently maintain a high level of quality in the generated images.

[0085] The editorial team can not only modify and edit parts specified by the user, but also provide an interface to make the editing process more intuitive. For example, the editorial team can provide an interface that allows users to easily select specific parts of an image with touch gestures. Furthermore, the editorial team can provide a function that allows users to preview their edits in real time, enabling them to instantly check the results. In addition, the editorial team can provide an "undo" function that allows users to easily revert edits, preventing editing errors. This enables users to edit images intuitively and efficiently.

[0086] The data collection unit not only gathers user requests but can also estimate the user's emotions and adjust the request collection method based on those emotions. For example, if the user is stressed, the data collection unit can collect requests in a simple question format to reduce the burden. If the user is relaxed, the data collection unit can ask detailed questions to elicit specific requests. Furthermore, if the user is in a hurry, the data collection unit can prioritize voice input to quickly collect requests. In this way, by adjusting the request collection method according to the user's emotions, more appropriate requests can be collected.

[0087] The generation unit can estimate the user's emotions and adjust the style of the generated images based on those emotions. For example, if the user is relaxed, the generation unit can generate images with soft colors. If the user is excited, the generation unit can generate images with vibrant colors. Furthermore, if the user is calm, the generation unit can generate images with a simple and refined style. This allows for the generation of more appropriate images by adjusting the image style according to the user's emotions.

[0088] The editorial team can estimate the user's emotions and adjust the editing method based on those estimates. For example, if the user is relaxed, the editorial team can provide detailed editing options. If the user is in a hurry, the editorial team can provide simple editing options. Furthermore, if the user is stressed, the editorial team can provide a simple editing interface. This allows for more appropriate editing by adjusting the editing method according to the user's emotions.

[0089] The data collection unit can not only collect user requests but also analyze users' social media activity and collect relevant requests. For example, the data collection unit can analyze users' social media posts and collect relevant requests. The data collection unit can filter requests based on users' interests on social media. Furthermore, the data collection unit can analyze users' social media activity history and collect the most relevant requests. In this way, relevant requests can be collected by analyzing social media activity.

[0090] The generation unit can estimate the user's emotions and determine the priority of images to generate based on those emotions. For example, if the user is stressed, the generation unit will prioritize generating relaxing images. If the user is excited, the generation unit can prioritize generating visually stimulating images. Furthermore, if the user is calm, the generation unit can prioritize generating simple and sophisticated images. This allows for the generation of more appropriate images by prioritizing images according to the user's emotions.

[0091] The editorial team can estimate the user's emotions and prioritize which parts to edit based on those estimates. For example, if the user is stressed, the editorial team will prioritize editing important parts. If the user is relaxed, the editorial team can prioritize editing detailed parts. Also, if the user is in a hurry, the editorial team can prioritize editing parts that require immediate attention. This allows for prioritizing editing based on the user's emotions, ensuring that important parts are edited first.

[0092] The generation unit can generate highly relevant images by considering the user's geographical location. For example, if the user is in a specific region, the generation unit will generate images related to that region. The generation unit can generate highly relevant images based on the user's geographical location. Furthermore, if the user is on the move, the generation unit can generate images based on their current location. In this way, by considering geographical location, highly relevant images can be generated.

[0093] The following briefly describes the processing flow for example form 2.

[0094] Step 1: The collection unit collects user requests in a dialogue format. The collection unit can interact with users and collect requests using chatbots or voice dialogue systems. For example, if a user communicates a request by voice, it can be converted into text and collected. The collection unit can also provide an interface where users can input requests in text. Step 2: The generation unit generates images based on the requests collected by the collection unit. The generation unit generates images using generative AI, and can generate highly accurate images using, for example, GANs (Generative Opposite Networks) or deep learning. The generation unit can also generate images that conform to a specific style or theme based on the user's requests, learn the user's preferences using machine learning, and suggest more appropriate images. Step 3: The editorial team modifies and edits specific parts of the image generated by the generation team. The editorial team provides an interface for modifying user-specified parts, allowing for filter application and partial drawing corrections. For example, if a user requests to "change the background to a blue sky," only that part will be modified. Furthermore, the editorial team provides automation tools to reduce editing time, automating specific editing tasks for efficient editing.

[0095] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0096] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0097] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0098] Each of the multiple elements described above, including the collection unit, generation unit, and editing unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user requests using a chatbot or voice dialogue system on the smart device 14. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates images using GANs or deep learning. The editing unit is implemented in the control unit 46A of the smart device 14 and provides an interface for modifying and editing parts specified by the user. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

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

[0100] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0102] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0103] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0105] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0106] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0107] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0108] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0109] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0113] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the collection unit, generation unit, and editing unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user requests using the chatbot or voice dialogue system of the smart glasses 214. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates images using GANs or deep learning. The editing unit is implemented in the control unit 46A of the smart glasses 214 and provides an interface for modifying and editing parts specified by the user. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0115] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0116] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the collection unit, generation unit, and editing unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user requests using the chatbot or voice dialogue system of the headset terminal 314. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates images using GANs or deep learning. The editing unit is implemented in the control unit 46A of the headset terminal 314 and provides an interface for modifying and editing parts specified by the user. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0131] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0132] As shown in Figure 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.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0139] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the collection unit, generation unit, and editing unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects user requests using the robot 414's chatbot or voice dialogue system. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates images using GANs or deep learning. The editing unit is implemented, for example, by the control unit 46A of the robot 414, and provides an interface for modifying and editing parts specified by the user. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0148] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0150] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0151] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0152] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0156] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0157] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0158] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0159] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0160] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0161] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0163] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0164] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0165] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0166] (Note 1) A collection unit that gathers user requests in a dialogue format, A generation unit that generates images based on requests collected by the aforementioned collection unit, The system comprises an editing unit that modifies and edits a specific portion of the image generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Using machine learning, we learn user preferences and suggest more appropriate images. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Improve the accuracy of the generated images. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned editorial department, Reduce editing time The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is We estimate the user's emotions and adjust the method of collecting requests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze the user's past request history to select the optimal timing for data collection. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting requests, filter them based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and determines the priority of requests to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting requests, the system prioritizes collecting requests that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting requests, we analyze users' social media activity and gather relevant requests. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is It estimates the user's emotions and adjusts the style of the generated images based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating images, the system selects the optimal generation algorithm by referring to the user's past generation history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating images, the generated content is customized based on the user's current project and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and determines the priority of images to generate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating images, the system considers the user's geographical location to generate highly relevant images. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating images, the system analyzes the user's social media activity and generates relevant images. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned editorial department, It estimates the user's emotions and adjusts the editing method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned editorial department, During editing, the system selects the optimal editing method by referring to the user's past editing history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned editorial department, During editing, the editing process is customized based on the user's current project and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned editorial department, It estimates the user's emotions and determines the priority of the parts to edit based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned editorial department, When editing, the system takes the user's geographical location into consideration to perform more relevant edits. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned editorial department, During editing, analyze the user's social media activity and make relevant edits. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that gathers user requests in a dialogue format, A generation unit that generates images based on requests collected by the aforementioned collection unit, The system includes an editing unit that modifies and edits a specific portion of the image generated by the generation unit. A system characterized by the following features.

2. The generating unit is Using machine learning, we learn user preferences and suggest more appropriate images. The system according to feature 1.

3. The generating unit is Improve the accuracy of the generated images. The system according to feature 1.

4. The aforementioned editorial department, Reduce editing time The system according to feature 1.

5. The aforementioned collection unit is We estimate the user's emotions and adjust the method of collecting requests based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is Analyze the user's past request history to select the optimal timing for data collection. The system according to feature 1.

7. The aforementioned collection unit is When collecting requests, filter them based on the user's current projects and areas of interest. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and determines the priority of requests to collect based on those estimated emotions. The system according to feature 1.

Images